Marc Joëts
email: marc.joets@u-paris10.fr

Mood-misattribution e¤ect on energy markets: a biorhythm approach

Keywords: G02, C21, Q40 energy forward markets, mood-misattribution, behavioral …nance, extreme price movements, quantile regression

This paper investigates the relationship between emotion and European energy forward prices of oil, gas, coal and electricity during normal times and periods of extreme price movements relying on the biorhythm approach. To this end, we use the Seasonal A¤ective Disorder (SAD) variable to study the impact of emotion on energy market dynamics. Estimating OLS and quantile regressions, we …nd that seasonal patterns have a signi…cant impact during extreme volatility periods only. Further investigations reveal that the SAD a¤ect is sig-ni…cant during periods of price decrease, but insigni…cant during price increase. The out-of-sample predictive ability properties are also investigated and show that our "SAD model" outperforms signi…cantly the pure "macroeconomic one".

Introduction

Understanding energy price dynamics is a rather di¢ cult task given its apparent erratic behavior and the various potential factors that may be at play [START_REF] Sadorsky | Oil Price Shocks and Stock Market Activity[END_REF][START_REF] Hamilton | What Is an Oil Shock?[END_REF]Kilian, 2008, among others). Strong ‡uctuations increase energy market risks which lead in turn to distinct market apprehension and perception. Regarding the traditional economic and …nancial approaches, a rational agent is de…ned as someone who used all available information to anticipate future evolutions and allocate his portfolio, so that anticipations are well established on average. Under this hypothesis, rational investors will always choose equities with the best bene…t-risk trade o¤ in the E¢ cient Market Hypothesis (EMH) sense.

With regard to the development of behavioral …nance [START_REF] Shleifer | Ine¢ cient Markets: A Introduction to Behavioral Finance[END_REF]Thaler, 2005, among others), this traditional approach seems to be too restrictive in the sense that individual rationality appears to be bounded [START_REF] Simon | Models of Bounded Rationality[END_REF](Simon, , 1987a(Simon, and 1987b)). In this context, the economic agent is not a simple calculator but a human with biaises whose decision-making process is in ‡uenced by cognitive and emotional resentments. This characteristic leads to distinct asset valuations among investors which can create excess volatility in …nancial markets [START_REF] Black | Noise[END_REF]. The seminal work of [START_REF] Damasio | Descartes' Error: Emotion, Reason, and the Human Brain[END_REF] shows that emotion can a¤ect behavior and play a crucial role in the decision process where lack of feelings leads to suboptimal choices. In this way, recent researches in behavioral …nance have studied the in ‡uence of emotions through the mood misattribution impact on decision-making. According to Loewentein et al. (2001), the mood misattribution perspective relies on the hypothesis that individuals who take their decision under risk and uncertainty are unconsciously in ‡uenced by their relative emotional states even if moods are unrelated to their choices [START_REF] Schwarz | Mood, misattribution, and judgments of well-being: Informative and directive functions of a¤ective states[END_REF].

Recent studies have shown signi…cant mood impact on equity pricing.1 However, they focus on speci…c classes of assets. In order to bring new elements to the recent energy prices increase and assuming that excess volatility could be due in part, to some investors in ‡uenced by their emotional states, we investigate the impact of mood misattribution on forward energy market dynamics such as oil, gas, coal and electricity during both "normal times" and periods of extreme (upward and downward) price movements. By relying on forward energy prices, we are able to account for both fundamental and speculative pressures (Joëts and Mignon, 2011). 2 In order to investigate mood e¤ect, a biorhythm approach is adopted considering the Seasonal A¤ective Disorder (SAD) framework developed by [START_REF] Kamstra | Winter blues: a SAD stock market cycle[END_REF]. This approach known as 'winter blues' considers that seasonal variation in the number of hours of sunlight per day can lead to anxious state which in turn can a¤ect risk apprehension and decisionmaking. Therefore, the SAD variable can be seen as an approximation of emotion which can a¤ect the energy market behavior. Assuming that feelings in ‡uence behavior and risk perception of investors, we analyze mood e¤ect on energy market variations in an in-sample and out-of-sample contexts. Both normal and extreme volatility periods are considered using OLS and quantile regression approaches.

Our contribution is fourfold. First, the relationship between emotion and energy markets is studied using biorhytm approach through the SAD proxy variable. Second, by relying on European forward prices of oil, coal, gas and electricity, we purge short-run demand and supply from noise that a¤ects market ‡uctuations, and account for both fundamental and speculative pressures. Third, we investigate the emotional phenomenon of energy market dynamics considering normal and extreme market circumstances. Finally, we compare the out-of-sample properties of our SAD model against a pure macroeconomic model in term of predictive ability to see which strategy is the more …tted.

The rest of the paper is organized as follows. Section 2 presents the theoretical research background on which the investigation of investors'feelings is based. Section 3 reviews the existing literature on mood misattribution and equity pricing. Empirical application on energy markets is displayed in Section 4, and Section 5 concludes the article.

Mood in ‡uences on investor decision-making under uncertainty

In the traditional portfolio choice theories, the process of investors'decision is assumed to be quantitatively characterized by the weight of costs and bene…ts of all possible outcomes. In this perspective, rational investors choose the outcome with the best risk-bene…t trade o¤ (see [START_REF] Markowitz | Portfolio selection[END_REF]Sharpe, 1964, among others). This type of behavior is what [START_REF] Loewenstein | Risk as feelings[END_REF] describe as a 'consequentialist perspective' which does not account for the emotional impact on the decision-making process. However, in practice these traditional approaches may be viewed as unrealistic since feelings play a crucial role in the perception of the environment, especially under risky and uncertain context (see, [START_REF] Zajonc | Feeling and thinking: Preferences need no inferences[END_REF][START_REF] Schwarz | Feelings as information: Informational and motivational functions of a¤ective states[END_REF][START_REF] Forgas | Mood and Judgment: The A¤ect Infusion Model (AIM)[END_REF][START_REF] Isen | Positive a¤ect and decision making[END_REF]Loewenstein et al., 2001, among others).

Behavioral …nance coupled with the reconsideration of the rational investor concept bring to the light the recent interest in feelings impact on economic behavior leading to the development of a new class of models. The latters introduce anticipated emotions which are de…ned as emotions that are expected to be experienced by investors given a certain outcome level. This concept has been developed through the [START_REF] Loomes | Regret theory: An alternative theory of rational choice under uncertainty[END_REF]'s regret model and applied in …nance in the myopic loss aversion theory of [START_REF] Benartzi | Myopic loss aversion and the equity premium puzzle[END_REF]. Despite the fact that anticipated emotions constitute an advance over the traditional consequentialism approach, this concept appears to be relatively restrictive in the sense that it considers anticipated feelings rather than feelings experienced at the time of decision-making. According to [START_REF] Schwarz | Feelings as information: Informational and motivational functions of a¤ective states[END_REF], it seems coherent that people make di¤erent investment decisions depending on their positive and negative moods even if mood is unrelated to the decision context [START_REF] Schwarz | Mood, misattribution, and judgments of well-being: Informative and directive functions of a¤ective states[END_REF].

To overcome this limit, [START_REF] Loewenstein | Risk as feelings[END_REF] develop the risk-as-feelings model which incorporates emotions in ‡uence at the time of making decision by allowing anticipated emotions, subjective probabilities and extra factors (i.e. mood, ...) that a¤ect decision-making. 3 In their model framework, [START_REF] Loewenstein | Risk as feelings[END_REF] suppose that investors'decisions under risky and uncertain environment are strongly a¤ected by feeling perception. The authors use the three following premises derived from psychology : i) Cognitive evaluations include emotional reactions4 , ii) Emotions inform cognitive evaluations5 , and iii) Feelings can a¤ect behavior. 6 According to [START_REF] Loewenstein | Risk as feelings[END_REF]'s risk-as-feeling model, decision-making is the consequence of the interconnected processes of cognition evaluation and emotions which in turn a¤ect behavior.

In a complementary way of [START_REF] Loewenstein | Risk as feelings[END_REF], [START_REF] Forgas | Mood and Judgment: The A¤ect Infusion Model (AIM)[END_REF] develops an A¤ect Infusion Model (AIM) which covers the extent to which people rely to their respective feelings. Forgas argues that emotions in ‡uence decision process depending on the risky and uncertain choice environment context. In this framework, he de…nes two kinds of strategies depending on the situation. The …rst one is the Low A¤ect Infusion Strategies (LAIS), used under familiar situations which involve less riskier and low complexity circumstances. The second one is the High A¤ect Infusion Strategies (HAIS) which are employed for more complex decision processes, under highly risky context. According to the AIM of [START_REF] Forgas | Mood and Judgment: The A¤ect Infusion Model (AIM)[END_REF], feeling becomes predominant as risk and uncertainty increase. For instance, under optimal porfolio choice, investor should be characterized by HAIS framework where decisionmaking would be strongly dependent to her mood states.

According to this literature, feelings appear to have an in ‡uence on economic and …nancial behaviors. In a risky context, many factors can in ‡uence decision-making even if they are not related to decision. Mood is therefore seen as information as well as human misattribution emotions.

3 Mood-as-information and misattribution: literature review

In the literature, two types of feeling determinants are considered: the mood misattribution and the a¤ect heuristic. While the later argues that people's decision-making is governed by images and associated feelings that are induced by decision process, the former maintains that mood can be induced by the environmental context such as weather, biorhythms and social events. These determinants leading to mood ‡uctuations are likely to a¤ect investors'decision process and therefore …nancial stock markets.

Recent researches on behavioral and emotional …nance mainly focus on mood misattribution by studying empirical evidence of mood ‡uctuations on equity returns. These factors in ‡uencing the positive and negative mood states are likely to modify the risk assessments. [START_REF] Saunders | Stock prices and wall street weather[END_REF], focusing on weather-based in ‡uences7 on mood and behaviour, examines the potential impact of weather on both Dow Jones Industrial index from 1927 to 1989 and NYSE/AMEX indices from 1962 to 1989. Under the hypothesis that bad and good weathers lead to pessimistic and optimistic moods respectively and in turn to lower and higher returns, Saunders investigates the relationship between New York equity prices and the level of cloud cover in New York. He …nds a signi…cant relationship between both variables showing that mood misattribution e¤ect can exist and be exploited in portfolio consideration. [START_REF] Hirshleifer | Good day sunshine: Stock returns and the weather[END_REF] extended Saunders'analysis by considering the relationship between the de-seasonalized cloud cover and daily equity returns in 26 international markets from 1982 to 1997. Their results con…rm a signi…cant negative relationship between cloud cover and equity returns and the fact that weather a¤ects stock returns variabilities. 8 Cao and Wei (2002) based on psychological evidences, …nd signi…cant impact of temperature on equity returns of eight …nancial markets from July 3, 1962 to July 3, 2001. Lower temperatures lead to higher returns while higher temperatures lead either to higher or lower stocks.

In the mood misattribution research, other studies have extended re ‡exion to broader proxies related to human biorhythms, and investigated misattribute impact of biological cycles on equity returns. [START_REF] Kamstra | Losing sleep at the market: The daylight-savings anomaly[END_REF], assuming that an interruption of body's circadian cycle can cause anxiety and depression [START_REF] Coren | Sleep Thieves[END_REF], investigate the in ‡uence of interruptions to sleep patterns induced, twice a year, by Daylight Savings Time Changes (DSTCs) on equity returns of US, Canadian, German and UK markets. They …nd a signi…cant negative relationship between returns and DSTCs re ‡ecting a negative impact of such biological e¤ect. [START_REF] Kamstra | Winter blues: a SAD stock market cycle[END_REF] further investigate the potential impact of biorhythms and emotions on investment decisions by considering a depressive phenomenon known as Seasonal Affective Disorder (SAD) or 'winter blues'. This phenomenon is characterized by the fact that seasonal variation in hours of sunlight in the day can lead to anxious states [START_REF] Cohen | Preliminary data on the metabolic brain pattern of patients with winter seasonal a¤ective disorder[END_REF][START_REF] Rosenthal | Winter blues. Seasonal A¤ective Disorder: What it is and How to Overcome it[END_REF] which in turn can a¤ect risk apprehension. Due to di¤erent SAD e¤ects depending on latitude locations, the authors investigate SAD/returns relationship including major equity indexes in both Northern and Southern Hemisphere countries. They …nd a signi…cant SAD e¤ect leading to seasonal pattern in returns. Then, due to SAD e¤ect, equity returns are predicted to be lower between Autumn Equinox and Winter Solstice. Moreover an asymmetric component appears between fall and winter. Investors are considered to be risk averse and shun risky assets during fall while they seem to resume their risky holding during winter. Recently, to check the robustess of global mood in- ‡uences, Dowling andLucey (2005, 2008) investigate the impact of seven mood proxies variables (i.e. weather data (precipitation, temperature, wind, geomagnetic storms) and biorhythm data (SAD, DSTCs, lunar phases)) on returns and variance of 37 global equity markets from 12th December 1994 to 10th November 2004 using various robust econometric methods (GARCH speci…cations). They …nd that SAD e¤ect is the most predominant one on equity pricing which means that winter blues is signi…cant in both returns and variance of stocks.

These studies put forward that mood misattribution e¤ect exists and tends to in ‡uence equity prices ‡uctuations. However, beyond this scope of research, it appears to be primordial to assess feeling e¤ects on a most widely class of assets. In this way, we investigate the relationship between mood and energy market dynamics to see whether recent price ‡uctuations can be attributed to emotional considerations. Speci…cally, we distinguish between usual and extreme phases, and focus on the relation between mood misattribution and market variations by considering SAD approach: mood is proxied by SAD variable, while regular and extreme variations by both OLS and quantile regressions.

4 Empirical investigation

Data description

We consider daily data over the January 3, 2005 to December 31, 2010 period. We rely on European forward prices at 1 month of oil, gas, coal, and electricity. Energy prices data are extracted from the Platt's Information Energy Agency. To control for the economic and …nancial environment that may impact all energy price series (such as increasing demand from Asian emerging countries or speculation), we rely on a European equity futures price index-which has the advantage of being available at a daily frequency. This variable also allows considering energy markets as …nancial assets and controls for the recent …nancial turmoil. Our retained equity variable is the Dow Jones Euro Stoxx 50, the European leading stock index for futures contracts, extracted from Datastream. In order to account the energy prices risk premium, the euro/dollar US exchange rate is considered as a control variable. Basic statistical characteristics are reported in Table 1. They reveal that all energy return series are asymmetric (oil, gas and electricity returns are right skewed while coal returns are left skewed) and leptokurtic, indicating fat tail distributions. Due to the speci…c nature of electricity market (i.e. non-storability, inelasticity of the supply,...), returns are frequently a¤ected by regime switching causing tails behavior higher than fossil energies (1.7 and 25 for the skewness and kurtosis respectively).

The mood proxy data de…ned as SAD variable, is calculated following [START_REF] Kamstra | Winter blues: a SAD stock market cycle[END_REF] where "arccos" is the arc cosine, the latitude which depends on countries location 10 , and t the sun's declination angle de…ned as follows:

t = 0:4102 sin 2 365 (julian t 80:25)

where "julian t " sets for the number position of the day in the year numbered from 1 to 365. 11

According to [START_REF] Kamstra | Winter blues: a SAD stock market cycle[END_REF], SAD variable is de…ned by (H t 12) from Autumn Equinox to Spring Equinox and 0 otherwise. In this framework, during SAD period, investors are considered to be risk averse and to allocate their portfolios to safer assets a¤ecting negatively energy market dynamics. On the contrary, from Spring Equinox to Autumn one, no SAD e¤ect exists. Beside, SAD phenomenon is deeply in ‡uenced by geographical location. Therefore, we expect to have stronger impact in Northern Hemisphere countries rather than Southern Hemisphere countries, the later being closest to the equator where seasonal variations in daylight are small.

Results and analysis

In order to investigate mood-misattribution e¤ect on energy markets during regular and extreme price movements, we adopt the traditional OLS framework as well as the quantile regression approach introduced by [START_REF] Koenker | Regression quantiles[END_REF].

Consider the following linear model:

Y t = X 0 t + t (4)
where Y and X are the endogeneous and exogeneous variables respectively, being the error term. In the traditional OLS framework, the dependent variable is supposed to ‡uctuate randomly around the conditional mean of the conditional distribution of Y (E [Y =X; ]), allowing to study the in ‡uence of exogeneous variables under regular time perspective. On the contrary, quantile analysis allows to examine the manner in which a set of explanatory variables can a¤ect the conditional distribution of the dependent variable. By this approach, we focus on extreme occurrences considering di¤erent quantiles of the conditional distribution. In order to account for both upward and downward price movements, two quantiles are considered ( = 0:05 and 0:95).

The following regressions are estimated

r (i) t = + SAD (j) t + Stoxx t + Rate t + " (i) t (5)
where r (i) t is the log-returns series for energy i (oil, gas, coal and electricity respectively). SAD (j) t is the emotional proxy variable at j hemisphere (Northern and Southern Hemispheres respectively). Stoxx t and Rate t are the control variables for the economic and …nancial environment.

Table 2 reports the results of the OLS estimation of Equation ( 5) considering SAD e¤ect on forward energy markets at 1 month during normal times. Distinguishing Northern and Southern Hemisphere countries, estimations reveal that SAD component has no any signi…cant impact on energy market ‡uctuations in regular circumstances. More precisely, emotions do not a¤ect energy markets when price movements are "usual" which corroborates the fact that during normal times, energy price dynamics are mainly governed by fundamentals. Regarding to the extreme market perspectives, Table 3 reports SAD e¤ect on energy returns using quantile regression approach. A geographic di¤erentiation is considered as well as downward and upward price movements. We see that SAD has di¤erent impacts depending on the hemisphere location. Considering Northern Hemisphere, results show that during periods of prices decrease, energy markets are in ‡uenced by emotional e¤ects. Indeed, regarding to the left-tail behavior, SAD variables are signi…cant and have negative impact for each market. This phenomenon re ‡ects a mood-misattribution bias where environment leads to depressive states which in turn a¤ects risk perception of investors. Moods are unrelated to energy portfolio choices, however investors are negatively a¤ected by their emotions which self-sustain risk aversion behavior. Under SAD framework, energy prices decreases may be partly explained by emotional considerations which tend to a¤ect investors'risk apprehension. Extreme movements are inherently associated with higher risk situation. From the upward point of view, SAD variable doesn't have any e¤ect on energy markets. Considering Southern Hemisphere, we observe the opposite phenomenon regarding SAD e¤ect on energy markets. As before, SAD is signi…cant for each market during periods of price decrease, however, this impact appears to be positive on risk perception. It is not surprising to …nd lack of negative seasonal patterns in Southern Hemisphere countries because they are located closest to the equator where seasonal variations in daylight are quite small. Therefore, investors from Southern Hemisphere countries are less in ‡uenced by SAD components.

Investors who su¤er from SAD e¤ect are supposed to be risk averse and shun risky assets during fall and to resume their risky holding during winter. Therefore, SAD should have negative impact during fall and positive e¤ect during winter. 12 To further investigate the asymmetric seasonal phenomenon between fall and winter during extreme volatility situations, we estimate the following quantile regression allowing both SAD fall and winter variables:

r (i) t = + 1 SAD f all(j) t + 2 SAD win(j) t + Stoxx t + Rate t + " (i) t (4)
where SAD f all(j) t is conducted from September 21 to December 20 for Northern Hemisphere countries, and from March 21 to June 20 for Southern Hemisphere countries. SAD win(j) t runs from December 21 to March 20 for North, and from June 21 to September 20 for South.

Table 4 reports the asymmetric e¤ect of SAD variables on energy markets at 1 month during extreme variations. Regarding Northern Hemisphere countries, SAD variable during fall has the expected e¤ect (signi…cant and negative) in downside risk context for each market. Investors are risk averse during fall and allocate their portfolios to safest assets which tend to impact energy downside risk. From upside point of view, SAD variable is signi…cant and positive during winter for oil, gas and electricity markets only. Regarding the asymmetric component, from Winter Solstice to Spring Equinox, investors' moods are heightened leading them to become more willing to resume the risk of their respective investments. From Southern Hemisphere perspective, both SAD fall and winter appear to have signi…cant and positive e¤ect in downside risk. Mood and energy prices are positively related which indicate the relative lower impact of emotion, in terms of seasonal variations, for countries closest to equator.

Our results are consistent with Forgas (1995)'s analysis which considers that agents are more in ‡uenced by moods under extreme situations rather than during normal ones. Extreme movements being inherently associated with high risk situation, recent energy prices ‡uctuations may be due in part to a misattribute emotional phenomenon which appears to be signi…cant and negative during periods of price decrease only for Northern Hemisphere countries. Surprisingly, this phenomenon is no longer valid during periods of price increase re ‡ecting that other factors should be considered.

Out-of-sample Predictive Ability of SAD approach

Previous section shows that SAD variable, as a proxy for emotion, impacts sign…cantly energy prices dynamic during extreme downward ‡uctuation periods. This phenomenon appears to be preponderant in Northern Hemisphere countries which are considered to be more in ‡uenced by variations of hours of daylight. Considering that forecasting is central to apprehend energy prices dynamic in economic decision-making for government institutions and regulatory authorities, we investigate the out-of-sample properties of our SAD model against a pure macroeconomic model in term of predictive ability. The former is of the form of Equation ( 5), while the latter removes the e¤ect of the SAD variable. In this way, we use the conditional [START_REF] Giacomini | Tests of Conditional Predictive Ability[END_REF]'s approach to evaluate the relative merit of the two forecast alternatives. [START_REF] Giacomini | Tests of Conditional Predictive Ability[END_REF] propose a test of Conditional Predictive Ability which allows to compare the forecast properties of two models, given a general loss function. 13 Their test allows to directly re ‡ect the e¤ect of estimation uncertainty on relative forecast performance.

Moreover, it considers a less restrictive framework than previous methodologies since it permits a uni…ed treatment of nested and nonnested models and also can accommodate more general estimation procedures in the derivation of the forecast.

Suppose one wants to compare the accuracy of the two competing forecasts for the -steps-ahead of the variable Y t+ , using a loss function L t+ (:) and the information set F t . [START_REF] Giacomini | Tests of Conditional Predictive Ability[END_REF] propose to test the following null hypothesis:

H 0 : E h L t+ (Y t+ ; b f t;m f ) L t+ (Y t+ ; b g t;mg )jF t i = 0 (5) E [ L m;t+ jF t ] = 0
where b f t;m f f W t ; :::; W t m f +1 ; b t;m and b g t;mg f W t ; :::; W t m g+1 ; b t;m are measurable functions of a stochastic process W de…ned on a complete probability space ( ; F,P ). The expectations are conditional to the set of information F t . The null hypothesis says that one cannot predict which forecasting methods will be accurate at the t + target horizon. Following [START_REF] Giacomini | Tests of Conditional Predictive Ability[END_REF], the test statistic is of the form:

T h m;n = n n 1 T 1 X t=m h t L m;t+1 ! 0 b 1 n n 1 T 1 X t=m h t L m;t+1 ! (6) = nZ 0 m;n b 1 n Z m;n 2 q;1 q where Z m;n n 1 T 1 P t=m Z m;t+1 , Z m;t+1 h t L m;t+1 , and b n n 1 n 1 T 1 P t=m Z m;t+1
Z 0 m;t+1 is a q q matrix. h t is the test function which can be chosen by researchers to include variables that are relevant to help distinguish between the two models. 14 As suggested by the authors, in order to compare the accuracy of the two competing approaches (SAD model vs macroeconomic model ) we consider rolling window estimators. 15 Due to the relevance of SAD e¤ect, we focus on Northern Hemisphere countries and downturn movements only. Our in-sample estimation goes from January 3, 2005 to February 7, 2009 and produces sequences of -step-ahead forecasts for = 1 using rolling window estimation procedure with m = m f = m g = 1174 + . Then, in order to choose the best forecasting model, we use the two-step decision rule procedure proposed by [START_REF] Giacomini | Tests of Conditional Predictive Ability[END_REF]

0 n h T E [ L m;t+1 jF t ] indicates the decision rule: if b 0 n h T > c, the performance of g is better, whereas if b 0 n h T < c, f
is the best choice (c = 0; is a user-speci…ed threshold value). In our case, g and f are the SAD model and the macroeconomic model respectively.

Table 5 gathers results of the two-step test procedure for each energy market. The …rst step indicates that for each energy price the null hypothesis is rejected. Therefore, the two competing models (SAD model and macroeconomic model ) are not equally accurate on average. It means that whatever the forecast target date t + , one model outperforms the other one in term of forecast performance. The second step allows to choose the suitable model strategy by indicating the proportion of time one model outperforms the other. Results in Table 5 reveal that for each energy price, the SAD model outperforms the macroeconomic model in forecasting performance. Our SAD model is therefore more adequate to apprehend the energy prices dynamic reinforcing our …nding in favor of the emotional component of the markets. This …nding appears to be particulary relevant in the sense that it shows that extreme energy prices ‡uctuation could be dictated by irrational movements without any economic foundation.

Conclusion

This paper investigates the relationship between emotion and European energy forward prices during normal times and periods of extreme price movements. Relying on mood-misattribution hypothesis, we use Seasonal A¤ective Disorder (SAD) variable as a proxy to analyze the seasonal patterns e¤ect on energy risk apprehension. Using both OLS and quantile biorhytm approach, we show that SAD phenomenon appears to be signi…cant during extreme ‡uctuations only. More precisely, emotions a¤ect energy market dynamics during periods of price decrease. However, this e¤ect tends to be di¤erent depending on the geographical location. Indeed, while Northern Hemisphere countries are primarily a¤ected by negative seasonal relationships, SAD a¤ects positively Southern Hemisphere countries which is consistent with the fact that seasonal variations of daylight are smaller for this group. Paying a particular attention to the asymmetric e¤ect between fall and winter, we show a negative impact of SAD during fall and a positive one during winter for Northern countries, consistent with the seasonal hypothesis. Our …ndings put foward the key role played by feelings in phase of price falling. The signi…cant role played by emotion in markets dynamic is con…rmed in term of forecast performance. The out-of-sample investigation comparing the predictive ability of SAD model against pure macroeconomic model indicates that the emotional model outperforms signi…cantly the economic model. Therefore, feelings appear to be preponderant in prices dynamic. Future researchs might be focused on the relative performance of the SAD model on portfolio allocation.

  's formula. It gives an approximation of both the reduction of hours of daylight from Autumn Equinox to Winter Solstice, and the lengthening of the day from Winter Solstice to Spring Equinox. 9 SAD t is constructed to re ‡ect the relative length of night in fall and winter compared to the mean annual length of 12 hours. According to psychological considerations, SAD is characterized as a binary variable which varies only over the fall and winter.Following[START_REF] Kamstra | Winter blues: a SAD stock market cycle[END_REF], H t , the number of hours of night is di¤erent depending on the country location and can be calculated using standard approximation from spherical trigonometry.

		8 <	H t 12 for trading days in the fall and winter
	SAD t =	:	0	otherwise	(1)
	where H t is the time from sunset to sunrise at a particular location. Value
	12 denotes roughly average number of hours of night over the entire year at
	8 < any location. Therefore, H t = 24 7:72 arccos : 7:72 arccos tan 2 tan 2 360 tan ( t ) 360 tan ( t )	in the Northern Hemisphere in the Southern Hemisphere
					(2)
	SAD variable is de…ned as follows:

  : 1. Step 1: Regress L m;t+1 = L t+ Y t+ ; b f t;m f L t+ (Y t+ ; b g t;mg) on h t over the out-of-sample period and let b n the regression coe¢ cient.Apply test and, in case of rejection of the null, proceed to step 2.

	2. Step 2: b

See references in Section 3.

Indeed, the forward energy market can result in both physical delivery and speculative purposes.

For more details, see the excellent survey of[START_REF] Dowling | The role of feelings in investor decisionmaking[END_REF].

According to[START_REF] Zajonc | Feeling and thinking: Preferences need no inferences[END_REF], emotions are considered to be postcognitive.

Researches in psychology show that optimistic and pessimistic behaviors tend to be linked to good and negative moods (see,[START_REF] Isen | A¤ect, accessibility of material in memory, and behavior: A cognitive loop?[END_REF][START_REF] Bower | Mood and memory[END_REF][START_REF] Johnson | A¤ect, generalization, and the perception of risk[END_REF] Bower, 1991, among others).

According to pioneer works of[START_REF] Damasio | Descartes' Error: Emotion, Reason, and the Human Brain[END_REF], people with impaired ability to feeling emotions tend to make suboptimal decisions under risky and uncertain environment.

Psychological studies have seen that ‡uctuations of hours of sunshine can induce ‡uctuations in mood (see,[START_REF] Persinger | Lag responses in mood reports to changes in the weather matrix[END_REF][START_REF] Howarth | A multidimensional approach to the relationship between mood and weather[END_REF][START_REF] Eagles | The relationship between mood and daily hours of sunlight in rapid cycling bipolar illness[END_REF].

High and low cloud covers are associated to low and high stock returns respectively.

In Northern Hemisphere countries, Autumn Equinox, Winter Solstice, and Spring Equinox start respectively at September 21st, December 21st, and March 20th. In Southern Hemisphere countries they begin at March 21st, June 21st, and September 20th. For more details, see[START_REF] Kamstra | Winter blues: a SAD stock market cycle[END_REF].

0 Following[START_REF] Kamstra | Winter blues: a SAD stock market cycle[END_REF], we distinguished Northern Hemisphere and Southern Hemisphere countries by averaging larger markets in North and South latitudes respectively (for more details, see Appendix).1 1 juliant is equal to 1 for January 1

, 2 for January 2, and so on.

2 According to SAD principle, the predicted negative e¤ect during fall is the result of decrease in hours of sunlight. During winter, the predicted positive e¤ect is due to an increase in hours of sunlight.

3 This literature was initiated byDiebold and Mariano (1995),[START_REF] West | Asymptotic Inference about Predictive Ability[END_REF],[START_REF] Mccracken | Robust Out-of-Sample Inference[END_REF],Clark and McCracken (2001),[START_REF] Corradi | Predictive Ability with Cointegrated Variables[END_REF][START_REF] Chao | An Out-of-Sample Test for Granger Causality[END_REF], to name few.

4 We use the moving average of past loss di¤erences. 1 5 As clearly mentioned by the authors, this limited memory approach is privileged for two reasons: (i) it imposes no restrictions on the estimators other than …nite memory, and (ii) the analysis required is straightforward (seeGiacomini and White,

2006).

Appendix

Latitude data description

To construct simplest latitude values, we select larger markets in North and South Hemispheres respectively, then average each latitude in order to obtain two representatives values for North and South geographical locations. For North latitude, we obtain 48.89 by selecting and averaging: Turkey (Ankara), US (Washington), Canada (Ottawa), Italy (Roma), Switzerland (Bern), Austria (Vienna), France (Paris), Luxembourd, Belgium (Brussels), Germany (Berlin), UK (London), Netherlands (Amsterdam), Ireland (Dublin), Denmark (Copenhagen), Norway (Olso), Sweden (Stockholm), Finland (Helsinki), China (Beijing), and Japan (Tokyo). For South latitude, we obtain 30.33 by choosing: New-Zealand (Wellington), Indonesia (Jakarta), South Africa (Johannesburg), Chile (Santiago), Australia (Camberra), and Argentina (Buenos Aires). Notes: Between parentheses t-stats. a denotes rejection of the null hypothesis at 1%, 5% or 10% signi…cance level. Notes: Between parentheses t-stats. a denotes rejection of the null hypothesis at 1%, 5% or 10% signi…cance level. UR and DR denote upward and downward price movements respectively. Notes: Between parentheses t-stats. a denotes rejection of the null hypothesis at 1%, 5% or 10% signi…cance level. UR and DR denote upward and downward price movements respectively. 182:94 (0:00 )


Balázs Égert
email: balazs.egert@oecd.org

Evžen Kočenda
email: evzen.kocenda@cerge-ei.cz

Robert Blecker

Jürgen Von Hagen

Jan Hanousek

Tomáš Holub

Roman Horváth

Valérie Mignon

Tigran Poghosyan

The impact of macro news and central bank communication on emerging European forex markets

Keywords: exchange rate, macroeconomic news, central bank communication, monetary model, Central Europe, European Union JEL Classification: E31, F31, O11, P17

We analyze the impact of macroeconomic news and central bank communication on the exchange rates of three Central and Eastern European (CEE) currencies against the euro. In doing so, we first estimate standard and extended versions of the monetary model to capture deviations from the long-term monetary equilibrium. In the second stage, we employ a highfrequency GARCH model that includes accurately identified macroeconomic news, central bank communication and emerging market risk and allows for non-linear behavior as regards the deviation from equilibrium. Surprisingly, there is little support for non-linearity in the data. During the pre-crisis period (2004)(2005)(2006)(2007) the major CEE currencies generally respond to macroeconomic news in an intuitive manner that corresponds to exchange rate-related theories. During the crisis (2008)(2009), the responsiveness breaks down and the currencies react to news on the key economic indicator (real GDP growth). There is a lack of responsiveness to central bank communications during the pre-crisis period but all currencies react to central bank verbal interventions during the crisis. Our results show that the CEE currencies react to both macroeconomic news and central bank communications but this responsiveness differs during the pre-crisis and crisis periods. Detailed responses vary across the currencies and we conjecture that the exchange rate regime and the extent to which particular currencies are traded on the international forex market are potential explanations behind these differences.

Introduction and motivation

Whether and how financial markets react to the various types of information flowing into them has long been a subject of intense research. [START_REF] Blinder | Central Bank Communication and Monetary Policy: A Survey of Theory and Evidence[END_REF] document the clear influence of central bank communication on financial markets in developed countries. [START_REF] Andersen | Realtime Price Discovery in Global Stock, Bond and Foreign Exchange Markets[END_REF] produce similar evidence with respect to macroeconomic announcements. Empirical evidence on the effects of these two types of news has also been provided specifically for exchange rates [START_REF] Andersen | Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange[END_REF][START_REF] Barndorff-Nielsen | Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation[END_REF][START_REF] Ehrmann | Communication by Central Bank Committee Members: Different Strategies, Same Effectiveness?[END_REF][START_REF] Evans | How is macro news transmitted to exchange rates?[END_REF]. Most of the research focuses on developed countries, though. We contribute to the literature by focusing on European emerging markets and show that their currencies are responsive to both macro news and central bank communications in general but their responsiveness differs in periods before and during the crisis.

Exchange rates, similar to other financial market instruments, are quite responsive to developments in the real economy that are channeled to the market via macroeconomic news releases. [START_REF] Cavusoglu | Exchange rates and the effectiveness of actual and oral interventions: A survey on findings, issues and policy implications[END_REF] surveys the relevant literature and provides extensive evidence that developments in macroeconomic fundamentals are important for exchange rate movements. For the key currencies there exists persuasive evidence that the effects of news are incorporated into the exchange rate with considerable speed (Chaboud et al., 2004). In addition, [START_REF] Chen | News announcements and price discovery in foreign exchange spot and futures markets[END_REF] show that macroeconomic announcements affect both spot and futures rates for the EUR-USD and JPY-USD markets. [START_REF] Mun | The joint response of stock and foreign exchange markets to macroeconomic surprises: Using US and Japanese data[END_REF] further shows that both the dollar and the yen respond to surprise component in the monetary news (money supply, inflation and interest rate) in accordance with theoretical predictions of the monetary approach to exchange rate determination. Similar to stocks, negative news have a larger impact than positive news of the same magnitude [START_REF] Andersen | Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange[END_REF]Galati and Ho, 2001;[START_REF] Laakkonen | Exchange Rate Volatility, Macro Announcements and Choice of the Intraday Seasonality Filtering Method[END_REF]. These effects change over time (Galati and Ho, 2001) and differ with the specific type of macroeconomic news (Edison, 1996;[START_REF] Ehrmann | Communication by Central Bank Committee Members: Different Strategies, Same Effectiveness?[END_REF]. Evans and Lyon (2008) and Laakonen (2007) argue that a large part of the effects of macroeconomic news should be attributed to order flows and [START_REF] Fratzscher | On the Long-Term Effectiveness of Exchange Rate Communication and Interventions[END_REF] and Laakonen (2007) show that macroeconomic news releases have produced about 15% of the exchange rate variation.

From the mid-1990s monetary authorities, especially in the US and the Eurozone, began to favor verbal interventions on account of foreign exchange sales and purchases [START_REF] Fratzscher | On the Long-Term Effectiveness of Exchange Rate Communication and Interventions[END_REF]. Central bank communication became more important in influencing the exchange rate via the coordination channel [START_REF] Cavusoglu | Exchange rates and the effectiveness of actual and oral interventions: A survey on findings, issues and policy implications[END_REF]. Official statements of the ECB about the euro-dollar exchange rate were shown by [START_REF] Marcel | Communication and exchange rate policy[END_REF]Fratzscher ( , 2005) ) to have both short-and long-run effects on the exchange rate, plus they were effective even without being accompanied by actual interventions. Even rumors about actual interventions were shown by Dominguez and Panthaki (2007) to cause exchange rates to move. In the same line [START_REF] Rosa | The high-frequency response of exchange rates to monetary policy actions and statements[END_REF] shows that exchange rate of the dollar with respect to several major currencies reacts fast to the FED communication. On the other hand, Jansen and de [START_REF] David | Talking Heads: The Effects of ECB Statements on the Euro-Dollar Exchange Rate[END_REF] showed that the effect of verbal statements by national central banks in the Eurozone was small and short-lived, in particular if combined with the release of macroeconomic news. Similarly, Siklos and Bohl (2007) find that actual interest rate moves had a larger impact on the exchange rate than verbal interventions, a feature hinting at deeds being more important than words. They point out that the estimation techniques used have a bearing on the conclusions and that the way central bank statements are coded in empirical works also matters. Still, verbal interventions by central banks tend to reduce exchange rate volatility in a number of developed as well as emerging economies [START_REF] Marcel | Communication and exchange rate policy[END_REF][START_REF] Fišer | Central bank communication and exchange rate volatility: a GARCH analysis[END_REF][START_REF] Jérôme | Jumps, Cojumps and Macro Announcements[END_REF][START_REF] Goyal | The Indian Exchange Rate and Central Bank Action: A GARCH Analysis[END_REF].

What is the empirical evidence for the CEE region? Emerging markets, where exchange rates are known to be volatile [START_REF] Bleaney | Exchange Rate Regimes, Inflation and Growth in Developing Countries --An Assessment[END_REF], are much less explored and one of the main reasons for this is the lack of data [START_REF] Cavusoglu | Exchange rates and the effectiveness of actual and oral interventions: A survey on findings, issues and policy implications[END_REF]. So far, we know very little about how the findings from developed markets apply to CEE markets. In terms of central bank communication, Égert (2007) foreign financial capital [START_REF] Jotikasthira | Asset Fire Sales and Purchases and the International Transmission of Financial Shocks[END_REF] 2 and spillovers from financial turmoil in advanced markets result in loosening of exchange rate policies in emerging markets [START_REF] Coudert | Exchange rate volatility across financial crises[END_REF]. We contribute with several novelties. First, we use a monetary model to calculate the nominal equilibrium exchange rate to be used in high-frequency exchange rate models. Second, the determinants of short-term exchange rate movements are studied using a set of accurately identified macroeconomic news and central bank communications not employed in the exchange rate analysis of CEE currencies so far. Third, we use a non-linear modeling framework, which allows the exchange rate to move back to the monetary equilibrium at different speeds depending on the size of the deviation from equilibrium. The paper describes our modeling strategy in Section 2, data in Section 3 and empirical results in Section 4. Conclusions are offered in Section 5.

Modeling strategy

Our modeling strategy has two stages in which we follow the approach of Égert (2010). First, we use the monetary model to compute long-run equilibrium exchange rates. Second, we estimate high-frequency exchange rate models by incorporating the deviation from the monetary equilibrium, macroeconomic news, central bank communication and a set of control variables. The idea of the two-stage approach is to produce daily interpolations of the long-term exchange rate that is in line with monetary fundamentals, from which deviations can be observed easily. In fact, it is current practice to evaluate the long-term real exchange rate (also termed equilibrium real exchange rate) by estimating a reduced form real exchange rate model. The problem with this approach is that it gives the deviations of the real exchange rate from its estimated long-run values, and that deviations can be corrected either via changes in the nominal exchange rate or changes in domestic or foreign prices. Therefore, it is more convenient to estimate a nominal exchange rate model where adjustments to equilibrium can occur only through changes in the nominal exchange rate [START_REF] Crespo-Cuaresma | The monetary approach to exchange rates in the CEECs[END_REF]).

An easy way to obtain long-term values of the exchange rate for modelling high frequency exchange rates is to use a moving average or a trend obtained on the basis of a filtering technique. This is a similar method as that of [START_REF] Fidrmuc | Volatiltiy of exchange rates in selected new EU members: Evidence from daily data[END_REF] who use the deviations from a long-term average of daily exchange rates in a conditional volatility equation. Our approach expands theirs by using the deviations from long-term values in the mean equation and by providing a more structural assessment of the long-term value of the nominal exchange rate, which incorporates information with regard to the underlying fundamentals.

Monetary equilibrium

The monetary model of the exchange rate is used to estimate the nominal equilibrium exchange rate. we subsequently use dynamic ordinary least squares (DOLS) to estimate the long-run relationship. Stock and Watson (1993) show that DOLS accounts for the endogeneity of the regressors and the serial correlation in the residuals by incorporating the lags and leads of the regressors in first differences:

t j t i n i k k j j i t i n i i t X X Y ε γ β β + ∆ + + = - = - = = ∑ ∑ ∑ , 1 , , 1 0 2 1 , (2)
where k 1 and k 2 denote leads and lags, respectively. The length of leads and lags is determined on the basis of the Schwarz information criteria. The presence of cointegration is assessed upon the stationarity of the residuals obtained from the long-term relationship

( t t i n i i t X Y ε β β + + = ∑ = , 1 0
). We also make use of the error correction term as a test of cointegration as Kremers, Ericsson and Dolado (1992) argue that it is more powerful than the residual-based Dickey-Fuller test. Furthermore, we cross-check cointegration using Johansen's trace statistics.

High-frequency exchange rate modeling

We incorporate the monetary equilibrium in our model of exchange rate returns ( t e ∆ ).5 4 , , 1 1

n t j j t i i t t j i e Z D α β δ ε = = ∆ = + + + ∑ ∑ Our linear specification is the following: , ( 3a
) 4 2 2 2 , , 1 1 1 1 n t j j t i i t t t j i Z D σ ζ ψ λ αε βσ - - = = =+ + + + ∑ ∑ , ( 3b
)
where Z contains the explanatory variables. The explanatory variables are taken in the conditional variance equation as they are (the raw data) but also in absolute values ( Z ). D 1 , D 2 , D 3 , and D 4 , are dummy variables that take a value of 1 on Tuesday, Wednesday, Thursday and Friday and zero otherwise. These dummies are to capture a day-of-the-week effect often identified in the existing literature as an important phenomenon of daily exchange rate movements. This pattern has also been documented for CEE countries. Next, we allow for nonlinear effects in the explanatory variables as a function of the deviation from the monetary equilibrium (DEV). We indeed look at whether deviations from the equilibrium are linked in a nonlinear fashion to changes in the exchange rate in the mean equation. The nonlinear model with one nonlinear variable can be written along the lines of the framework proposed by Hansen (1999):

6 2 4 , , 11 1 1 2 4 , , 21 1 1 n j j t i i t t t j i t n j j t i i t t t j i Z D DEV if T e Z D DEV if T α β δ ϕ ε ρ α β δ ϕ ε ρ - = = - = =  + ⋅ + + ⋅ + ≤   ∆ =   + ⋅ + + ⋅ + >   ∑ ∑ ∑ ∑ , (4a) 4 2 2 2 , , 1 1 1 1 n t j j t i i t t t j i Z D σ ζ ψ λ αε βσ - - = = =+ + + + ∑ ∑ , ( 4b
)
where ρ is the threshold variable and T denotes the threshold value of the threshold variable that separates the two regimes. The threshold variable is the deviation from equilibrium (DEV). Thus, a deviation from the monetary equilibrium is allowed to have a different effect on exchange rate returns depending on the distance of the exchange rate from its equilibrium value. Note that monthly deviations from the monetary equilibrium are linearly interpolated to daily frequencies. The errors are assumed to be white noise. The two-regime model can be easily extended to three regimes:

2 4 , , 11 1 1 1 2 4 , , 21 2 1 1 1 2 4 , , 31 2 1 1 n j j t i i t t t j i n t j j t i i t t t j i n j j t i i t t t j i Z D DEV if T e Z D DEV if T T Z D DEV if T α β δ ϕ ε ρ α β δ ϕ ε ρ α β δ ϕ ε ρ - = = - = = - = =  + ⋅ + + ⋅ + ≤     ∆ = + ⋅ + + ⋅ + ≥ >    + ⋅ + + ⋅ + >    ∑ ∑ ∑ ∑ ∑ ∑ , ( 5a
) 4 2 2 2 , , 1 1 1 1 n t j j t i i t t t j i Z D σ ζ ψ λ αε βσ - - = = =+ + + + ∑ ∑ . ( 5b
)
The selection between linear and nonlinear models is done as follows. We first estimate the linear model and the two-regime model. A grid search with steps of 1% of the distribution is carried out to find the value of the threshold variable that minimizes the sum of squared residuals of the estimated two-regime model. Hansen (1999) shows that the null hypothesis of 2 1 ϕ ϕ = can be tested using a likelihood ratio test. Given that the likelihood ratio test statistic does not follow a standard asymptotic distribution, as the threshold value is not identified under the null hypothesis, the distribution of the test statistic is obtained through bootstrapping based on random draws with replacements using 1000 replications (Hansen, 1999).

If the likelihood ratio test statistic rejects the null hypothesis of the linear model against the two-regime model (on the basis of the bootstrapped critical values), we also analyze whether there are three different regimes instead of two regimes. A three-regime model is estimated using the two threshold values of the threshold variable that minimize the sum of squared residuals across the estimated models. 6 The bootstrap procedure described above is applied to the two-regime and three-regime models.

Data

Exchange rates and macroeconomic data

Daily exchange rate quotes of the CEE currencies (the Czech koruna, Hungarian forint, and Polish zloty) with respect to the euro at 14:15 Central European Daylight Time for the period 2004-2009 were downloaded from Datastream. [START_REF] Bubák | Volatility Transmission in Emerging European Foreign Exchange Markets[END_REF] show that the exchange rate volatility of the three currencies under research exhibit different patterns before 2007 and after the outbreak of the 2007 financial crisis. In our analysis, we therefore distinguish two subsample periods to enable us to compare before the crisis (2004)(2005)(2006)(2007) and during the crisis (2008)(2009). 7 The monetary model is estimated on monthly data from 1995 to 2009/10. A detailed description of the monthly data is provided in the Data Appendix. In the following sections we further introduce the data on central bank communications (Section 3.2) and macroeconomic announcements (Section 3.3).

Central bank communication about exchange rates

Oral (verbal) central bank intervention is an official statement by the central bank expressing the bank's view about an over or undervaluation of the exchange rate with respect to macroeconomic fundamentals [START_REF] Fratzscher | On the Long-Term Effectiveness of Exchange Rate Communication and Interventions[END_REF]. Oral (verbal) interventions may influence the exchange rate via the coordination or signaling channel. The coordination channel involves coordinating individual expectations to converge on models driving an exchange rate towards parity level [START_REF] Sarno | Official Intervention in the Foreign Exchange Market: Is it Effective, and, If So, How Does it Work?[END_REF]. The signaling channel provides the market with information about future monetary and intervention policies and relevant macroeconomic fundamentals [START_REF] Mussa | The Role of Official Intervention, Occasional Papers No. 6, The Group of Thirty[END_REF].

Central bank statements specifically related to exchange rates were obtained from Factiva (a Dow Jones company) relying on a targeted search. The following combinations of 6 The threshold from the two-regime model is held fixed and a grid search is used to identify the second threshold. We impose the restriction that the two thresholds should be separated at least by 10% of our sample observations and that the 10% of the sample is trimmed on both sides of the distribution. Once the second threshold is identified, a backward grid search is done to identify the first threshold as suggested by Hansen (1999). 7 This division also corresponds to the worldwide decline of stock markets after December 2007 and to the fact that NBER designated the US to be in recession from December 2007 to June 2009. In Table 1 we show examples of central bank communications together with the date and the expected or desired effect of the particular statement on exchange rates. The statements enter our specification as dummy variables coded 1 for the day of identified statements and zero otherwise. We do not differentiate whether the statement was made by a board member, vice-governor or a governor as the communication data are not frequent enough to allow for effective differentiation.

Statements worded to strengthen or weaken a currency should be regarded as aiming to affect the exchange rate level, while statements on stability are meant to dampen exchange rate volatility. From Table A1 in the Appendix we can see that central bank communication about exchange rates is not very frequent. This is not surprising because all three countries target inflation and central bank communication is primarily focused on inflation and interest rates as witnessed in the selected statements shown in Table 1. Czech National Bank statements were primarily aimed to weaken the currency or to keep it stable. This is an intuitively reasonable distribution as the Czech currency has been steadily appreciating over the time and therefore only a few comments were meant to strengthen its value. In the case of the Hungarian forint an overwhelming majority of comments was intended to reduce volatility, much less were aimed to strengthen or weaken the currency. This communication activity fully corresponds to the exchange rate arrangement whose purpose was to keep the forint stable with respect to the euro. Until March 2008 the Hungarian Central Bank managed the forint under a ±15 % fluctuation band with respect to the euro and generally expressed heightened concerns about the forint exchange rate [START_REF] Kočenda | Macroeconomic Sources of Foreign Exchange Risk in New EU Members[END_REF].

Communications of the National Bank of Poland were more frequent than in other countries and relatively equally distributed among the three types of statements, with the smallest number trying to weaken the zloty.8

Macroeconomic announcements

We gathered data on ten types of macroeconomic announcements (news) that are divided into three categories. These are announcements on prices (CPI, PPI), real economy (industrial production, real GDP growth, retail sales, trade balance, current account, unemployment), and monetary policy (monetary aggregate, interest rate). For each country we have announcements originating in the respective country. Macroeconomic news from the Eurozone are captured by changes in the euro/dollar cross rate. The macroeconomic announcements used in this paper are reported by Bloomberg and Reuters with a clearly defined calendar and timing of news releases. Reuters also contains the market expectations of specific news, the so-called consensus forecasts of financial market analysts, which constitute a proxy for market expectations similar to the one used in Andersen et al. (2007). 9 The above arrangement is particularly important since it enables us to analyze the effect of news from its excess impact perspective. Because financial markets form expectations about scheduled important news, it is not the news itself that matters but its difference from what the market expects it to be (market consensus). The news deviation, or its excess, may then affect exchange rates. Based on the approach of [START_REF] Hanousek | Foreign News and Spillovers in Emerging European Stock Markets[END_REF] we construct a data set of announcements in the following way. There is news associated with indicator i in the form of various macroeconomic releases or announcements that are known ahead of time to materialize on specific dates t.

The announcements are usually reported on a monthly basis with the exception of the GDP that is reported quarterly. 10 confirmed that he considers oral interventions as the most appropriate channel when a central bank wants to communicate its position on an exchange rate and he feels that written minutes cannot serve this purpose adequately due to the substantial time lag. For these reasons we refrain from including written communication in our analysis.

The extent of such news is not known but expectations on the market form a forecast. The excess impact of a news announcement is then defined as the deviation of the news from the prior market expectation.

Further, since announcements are often reported in different units we standardize them to allow meaningful comparison (see e.g. [START_REF] Andersen | Realtime Price Discovery in Global Stock, Bond and Foreign Exchange Markets[END_REF]. Formally, the excess impact 9 Data on market expectations do not always coincide with the date of news releases. Expectations may change between the publication of market expectation and that of news releases. However, market analysts tend to interpret macro news in the light of earlier market forecasts and empirical researchers usually implicitely assume no change in expectations between those dates (see eg. [START_REF] Evans | How is macro news transmitted to exchange rates?[END_REF]. Further, similar to other researchers in the field, we are unable to account for announcements for which market expectations are not formed and not made publicly available. 10 There is also news in the form of an unexpected announcement that can be understood as a truly exogenous shock or surprise. The number of such news is negligible and we do not consider them in the present study. news variable is labeled as xn it and defined as (sn it -E t-1 [sn it ]) / σ i , where sn it stands for the value or extent of the scheduled announcement i at time t, E t-1 [sn it ] is the value of the announcement for time t expected by the market at time t-1 and σ i is the sample standard deviation of the announcement i. The standardization does not affect the properties of the coefficients' estimates as the sample standard deviation σ i is constant for any announcement indicator i. Hence, the macroeconomic announcements enter our estimation as follows: they have a non-zero value on the day of the announcement, coded in a way that a positive surprise is a value of any given macro news in excess of market expectations, and take the value of zero on days without announcement.

Empirical results

We first report the results of the long-term monetary model estimation in Table 2. In the first stage we estimate the basic monetary model comprising the exchange rate, relative money supply and relative output. We then augment the benchmark model by adding additional explanatory variables: the interest differential and proxies for the Balassa-Samuelson effect (the relative price of non-tradable goods and the labor productivity differential). We select the model for each country in which the variables are cointegrated and in which the coefficients are statistically significant and have the expected sign. The error correction terms from the DOLS models are negative and statistically significant, a feature evidencing weak cointegration. Coefficients of the explanatory variables are all statistically significant and exhibit intuitively correct signs.

In the second stage we estimate the high-frequency GARCH models. The first observation, reported in Table 3, is the absence of strong nonlinearities in our data. For all countries and periods, the bootstrapped p-values show that the null hypothesis of the linear model cannot be rejected against the alternative hypothesis of a two-regime model. In Tables 4 and5 we report the estimate results of the linear model and show a different degree of responsiveness during the pre-crisis and crisis periods. 11

11 We estimate our model for the pre-crisis period (2004)(2005)(2006)(2007), the crisis period (2008)(2009), and the whole span (2004)(2005)(2006)(2007)(2008)(2009). These results are not reported but they are available upon request.

For each currency we estimate GARCH(1,1) models with both the mean and conditional variance equations augmented by the set of news, central bank communications, day-of-the-week-effect dummies and the control variables (country risk premium, short-term interest differential and the euro-dollar cross rate). Further, the linear, two-regime and three-regime models are estimated to allow for differences due to a) nonlinearity in deviation from monetary equilibrium and b) the alternative interpolation of the monthly deviation series to a daily frequency (linear and cubic interpolation). Based on our model specification tests (Hansen, 1999) we estimate models where a deviation from monetary equilibrium is compared to actual fit and linear interpolation to daily frequency is used. 12As for macroeconomic news during the pre-crisis period (2004)(2005)(2006)(2007), the single most important macroeconomic announcement seems to be news about producer price inflation: the coefficients associated with this variable are statistically significant across all three currencies (Table 4). Improved price development (inflation below expectations) contributes to an appreciation of the Czech and Polish currencies. This finding is in accord with PPP theory. The coefficient is counter-intuitively positive for the Hungarian currency, though. This finding might be associated with the fact that the Hungarian forint has been subject to an exchange rate regime managed to make the forint stable vis-à-vis the euro. Still, evidence on the effect of price news on the three currencies is in line with the purchasing power parity hypothesis: lower prices are compensated by a strengthening of the domestic currency, which in turn keeps the real exchange rate stable. Finally, the sensitivity of the exchange rate to the news on producer price inflation is intuitively appealing as the manufacturing sectors in the three countries are quite import-intensive and trade with the EU is substantial. As all three countries are inflation targeters, the influence of CPI news is understandably missing.

Statistically significant coefficients associated with news on current accounts are found for the Hungarian and Polish currencies. These negative coefficients are in line with the theory, suggesting that improvements in current account balances lead, under regular circumstances, to an increased inflow of the foreign currency to a domestic country and to the strengthening of the domestic currency exchange rate [START_REF] Dornbusch | Exchange Rates and the Current Account[END_REF]. Further, news on improved retail sales lead to an appreciation of the Polish currency. A surprise increase in retail sales directly translates into increased demand for tradable goods and the currency to pay for them. However, the coefficient value indicates that in Poland the proportion of imported goods to domestic goods is almost equal as the coefficient is relatively small. In addition, the importance of this specific real factor (retail sales) is in line with the evidence in [START_REF] Kočenda | Macroeconomic Sources of Foreign Exchange Risk in New EU Members[END_REF], who show its positive effect on the risk premium of the Polish zloty.

The surprise component of unemployment announcements results in a depreciation of the Czech currency to an extent comparable to that of the producer price index. This is somewhat puzzling as lower unemployment should strengthen the currency. Finally, a positive surprise in the key interest rate is reflected in the appreciation of the Polish zloty.

The effect of central bank communications on the Polish currency is limited.

Statements intended to weaken the currency seem to contribute to a marginal appreciation instead. This counter-intuitive result could be explained by market expectations on currency strengthening no matter what the central bank wishes. Communications aimed at the stability of the exchange rate produce the same effect, both in terms of direction and magnitude.

Comments intended to strengthen the currency are not associated with a significant coefficient. The responsiveness of the Polish zloty is in line with the findings of [START_REF] Rozkrut | Quest for central bank communication: Does it pay to be "talkative[END_REF] who show that the Monetary Policy Committee's view on the zloty is more balanced than views of central bankers in the Czech Republic and Hungary with respect to their own currencies.

The Czech and Hungarian currencies do not show responses to central bank communications, as the respective coefficients are insignificant. [START_REF] Fratzscher | Oral Interventions versus Actual Interventions in FX Markets -an Event-study Approach[END_REF] shows that communications produce less impact and are less effective when the exchange rate is close to the equilibrium level and market uncertainty is low. The Czech currency steadily appreciated from 2004 to 2007 in line with equilibrium and experienced slight ups and downs during the crisis. However, its development over time has been remarkably stable. The Hungarian currency was under an arrangement that imposed stability on the forint with respect to the euro and this stability was achieved during 2004-2007. Hence, the lack of responsiveness to central bank communication is in line with the above argument. For Hungary it is the dual nature of the monetary policy framework during the period in question (inflation targeting and exchange rate stability with respect to the euro) that further complicates the effectiveness of central bank communications.

The control variables complete the picture: day-of-the-week effects are present, emerging market risk tends to weaken all three currencies and the dollar/euro cross rate affects only the Hungarian forint.

The overall lack of statistically significant coefficients for the Czech currency might be caused by the fact that the Czech koruna is traded more heavily on the international forex market than the other CEE currencies. This means there is less room for the effects of news and verbal interventions and more weight given to market forces. Further, the Czech koruna enjoys a large degree of confidence among knowledgeable international money managers and investors. Over the twenty years of transformation, the Czech koruna appreciated in value with respect to all the major currencies and it is almost considered a new safe haven for currency traders. 13 Results for the crisis period (2008)(2009) are reported in Table 5. The pattern of the responses to both macroeconomic announcements as well as central bank communications radically changes in this period. It seems that during the severe crisis, markets ceased react to macroeconomic news and currencies responded only to the most important information about economic development: news about real GDP growth. Surprise improvements in real output growth create a strong appreciatory effect on the Czech and Polish currencies, while lack of variation precludes inference for the Hungarian forint. An exception from the general lack of responsiveness during the crisis is the positive surprise in consumer prices that delivers a strengthening effect on the Polish zloty. The above findings reflect those of [START_REF] Cai | The impact of macroeconomic announcements on real time foreign exchange rates in emerging markets[END_REF], who show that in nine emerging markets (including our three CEE countries) during the pre-crisis period (2000)(2001)(2002)(2003)(2004)(2005)(2006), market uncertainty lowers responsiveness to macro news. We conjecture that our strong result on the lack of responsiveness of the exchange rate to news during the crisis is due to the exceptionally harsh character of the past crisis. During this time most of the economic news published in the media and by the news agencies focused on a potential recession and low economic growth. Other fundamentals are easily overlooked and news on GDP growth seems to be all that matters. When assessed from a different angle, during the severe crisis potentially less important fundamentals do not matter as much as during the calm days because of the stronger impact of contagion coming from other markets.

In terms of central bank communications the results differ completely from those found before the crisis begun because during the crisis period all currencies react to central bank communications. In particular, strengthening announcements have produced the desired responses for the Czech koruna. On the other hand, weakening announcements delivered strong appreciation effects for the Hungarian forint. This result is surprising; it might indicate that markets either misinterpreted the announcement or did not care, and therefore other effects dominated. 14 13 Many economic statistics support this perception, unlike in cases of the Polish zloty or Hungarian forint, as documented by Bloomberg.

Communications aimed at the stability of the currency produce an 14 We have identified only four weakening statements made by the Hungarian central bank in 2009 and none in 2008. Two of those statements are short and clear and two of them are complicated communications involving other economic variables such as inflation, exports, imports, etc. appreciatory effect in the Polish zloty. In any event, it seems that during the crisis markets do care what central banks have to say.

Control variables intuitively fit the pattern indicated by the results on macroeconomic news and central bank communications. Day-of-the-week effects vanish, emerging-market risk remains present and spillovers carried via the dollar/euro cross rate grow even stronger and become statistically significant for all three currencies.

Conclusions

In this paper, we analyzed the impact of macroeconomic news and central bank communications on currencies in emerging markets of the Central and Eastern Europe (CEE).

In our two-stage modeling strategy we first estimated variants of the monetary model to calculate the nominal equilibrium exchange rate allowing for a nonlinear return of the exchange rate to the monetary equilibrium depending on the size of the deviation from equilibrium. Next we integrated equilibrium exchange rates into the high-frequency GARCHtype models accounting for the effects of macroeconomic announcements and central bank communication. We provided evidence on the determinants of the short-term exchange rate movements of CEE currencies by including a large set of accurately identified macroeconomic news and central bank communications that have not been employed in the exchange rate analysis of the CEE currencies so far.

Our results show the absence of strong nonlinearities in our dataset. The results also show remarkably different patterns of how CEE exchange rates react to macroeconomic news announcements and central bank communications before and during a severe economic crisis.

During the pre-crisis period (2004)(2005)(2006)(2007), the three CEE currencies in general responded to various macroeconomic news in an intuitive manner, corresponding to exchange rate-related theories. During the crisis (2008)(2009), the relationships break down and the currencies react to news on the key economic indicator (real GDP growth). Heightened uncertainty seems to narrow the vision of the markets towards this key economic indicator as contagion from other markets impact specific countries' forex markets. In terms of central bank communications there is a lack of responsiveness during the pre-crisis period (with the exception of the Polish currency). All currencies react to verbal central bank interventions during the crisis period, though.

We provide evidence that the exchange rates of the CEE currencies are responsive to both macro news and central bank communications in general but this responsiveness differs significantly during pre-crisis and crisis periods. Detailed responses vary across the currencies and we conjecture that the exchange rate regime and monetary policy conduct affect these responses. Further, the extent to which particular currencies are traded on the international foreign exchange market might be potential explanation behind these differences, too. Finally, our results show that exchange rate related verbal communication of central banks does matter when markets experience high uncertainty, while during calmer days markets are less attentive.

  employed an event study framework and finds that interventions coupled with central bank communication and backed by interest rate news have quite a lasting effect on the exchange rate of the Czech, 1 In this paper, we augment the relevant literature by analyzing the impact of macroeconomic news and central bank communication on CEE currencies, notably the Czech Republic, Hungary and Poland, for which data on macroeconomic and central bank news are available. In this respect, we fill the gap in the literature by analyzing the effects of macroeconomic news and central bank communication in Central Europe as this topic is grossly under-researched despite the fact that the region attracts considerable amounts of Hungarian and Polish currencies. Similarly, Gábriel and Pintér (2006) report a smoothing effect of central bank communication on the Hungarian forint and Fišer and Horváth (2010) show a reduction in the volatility of the Czech koruna due to verbal communication.

  and GARCH terms that are needed because of the volatility clustering observed in the exchange rate-return series.

  keywords were used for specific countries. Czech Republic: (exchange rate AND koruna) AND (Czech National Bank OR CNB OR central bank OR governor); Hungary: (exchange rate AND forint) AND (National Bank of Hungary OR Central Bank of Hungary OR central bank OR governor); Poland: (exchange rate AND zloty) AND (National Bank of Poland OR NBP OR central bank OR governor).

Table 2b .

 2b Estimation results for the monetary model for the HUF/EUR exchange rate(Hungary), 1995(Hungary), -2010

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	UR (SIC) -2.94	-3.778*	-3.034	-3.812	-4.055	-4.955**	-3.577	-5.874***
	ECT	-0.074*** -0.111*** -0.084*** -0.122*** -0.137*** -0.139*** -0.075** -0.121***
	r=0	37.26*** 76.03*** 56.91*** 91.13*** 78.66*** 126.43*** 74**	98.89***
	r=1	12.83	46.74*** 23.41	54.54*** 41.87	75.11*** 38.48	62.85***
	CONST -0.068*** -0.092*** -0.054** -0.092*** 0.925*** 0.960*** 0.825*** 0.371***
	M3	0.710*** 1.275*** 0.683*** 1.290*** 0.907*** 0.907*** 0.926*** 1.342***
	GDPR	-1.686***		-1.671***		-0.893***		-2.007***
	IP		-1.112***		-1.132***		-0.201*		-1.120***
	IRS			-0.001	0.000	0.003*** 0.002*	0.007*** 0.003*
	CPIPPI					-0.977*** -1.022***
	PROD							-0.919*** -0.476***
		ECM	ECM	ECM	ECM	ECM	ECM	ECM	ECM
	No OBS	183	189	183	189	183	189	177	179
	R 2	0.098	0.144	0.15	0.19	0.492	0.385	0.129	0.195
	R 2 adj.	0.083	0.13	0.131	0.173	0.478	0.369	0.104	0.172
	SIC	-5.199	-5.155	-5.231	-5.183	-5.716	-5.431	-5.185	-5.158
		BASE	BASE	BASE	BASE	BASE	BASE	BASE	BASE
	No OBS	183	188	183	188	183	189	177	178
	R 2 adj.	0.829	0.876	0.833	0.881	0.95	0.924	0.871	0.884
	SIC	-3.026	-3.23	-2.999	-3.205	-4.153	-3.588	-3.204	-3.153

Notes: See table

2a

Table 2c .

 2c Estimation results for the monetary model for the PLN/EUR exchange rate(Poland), 1995(Poland), -2010

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	UR (SIC) -2.980	-3.080	-3.003	-3.142	-3.227	-3.234	-2.895	-2.96
	ECT	-0.041** -0.046** -0.042** -0.048** -0.072**	-0.084*** -0.041*	-0.046**
	r=0	64.75*** 61.38*** 91.63*** 91.78*** 119.42*** 118.75*** 152.77*** 155.69***
	r=1	8.92	12.19	23.08	23.17	48.37**	46.55*	76.15*** 80.61***
	CONST -0.048*** -0.040*** -0.030*	-0.006	2.960*** 2.932*** -0.303*	-0.288*
	M3	0.272*** 0.248*** 0.259*** 0.238*** 0.700*** 0.66***	0.189*** 0.192***
	GDPR	-0.300**		-0.381***		-0.433***		-0.163
	IP		-0.107**		-0.166***		-0.217***		-0.074
	IRS			-0.002	-0.003	-0.003*** -0.004***		0.000
	CPIPPI					-2.947*** -2.882*** 0.001
	PROD							0.239	0.233*
		ECM	ECM	ECM	ECM	ECM	ECM	ECM	ECM
	No OBS	183	191	183	191	183	190	177	179
	R 2 adj.	0.086	0.133	0.084	0.134	0.385	0.388	0.089	0.139
	SIC	-4.657	-4.739	-4.632	-4.718	-5.007	-5.038	-4.615	-4.676
		BASE	BASE	BASE	BASE	BASE	BASE	BASE	BASE
		183	191	183	191	183	190	177	179
	R 2 adj.	0.298	0.332	0.312	0.348	0.798	0.794	0.312	0.351
	SIC	-2.076	-2.114	-2.051	-2.094	-3.232	-3.198	-1.971	-1.986

Notes: See table

2a

Table 3 . Testing for nonlinearity in the deviation from monetary equilibrium

 3 Test No. 2 0.388 0.071 0.68 0.209 0.468 0.640 0.176 0.283 0.234 Notes: Test No. 1: H0: linear vs. H1: 2-regime nonlinearity Test No. 1: H0: 2-regime nonlinearity vs. H1: 3-regime nonlinearity Bold figures indicate statistical significance at the 5% level.

	2004-07	2008-09	2004-09
	CZK HUF PLN CZK HUF PLN CZK HUF PLN
	Nonlinearity -bootstrapped p-values
	Test No. 1 0.287 0.369 0.531 0.208 0.251 0.075 0.608 0.005 0.800

Using econometric estimates,[START_REF] Geršl | Foreign exchange interventions under inflation targeting: the Czech experience[END_REF] provide some evidence that the Czech central bank's market interventions, in various periods between 1997 and

2002, had a statistically significant, short-lived and economically not very important impact on the exchange rate of the Czech currency and its volatility.

[START_REF] Jotikasthira | Asset Fire Sales and Purchases and the International Transmission of Financial Shocks[END_REF] show that developed-country-domiciled mutual and hedge fund holdings account for about 14-19% of the free-float adjusted market capitalization in Central Europe. Specifically, 16.59% in the Czech, 16.98% in the Hungarian and 13.29% in the Polish equity markets.

The augmented Dickey-Fuller test (ADF), the Philips-Perron test (PP), the Elliot-Rothernberg unit root test, the Stock unit root test and the Kwiatkowski, Phillips, Schmidt and Shin stationarity test (KPSS) are used to check the integration order.

Returns are calculated as follows throughout the paper: (e(t)/e(t-1)-1)*100.

We acknowledge the existence of written central bank communication in the form of, for example, monetary policy minutes or voting records. These documents are usually issued with a time lag after the key information (from a monetary policy meeting) has been communicated verbally during a press conference. Hence, any key information in its verbal form precedes its written form. Further, since all three countries under research target inflation, most of the discussion contained in written communication is centered on interest rates and inflation. When exchange rate issues are in a written communication, it is from the perspective of how the exchange rate development affects the monetary policy framework, in particular the expected inflation, and definitely not how the exchange rate should be affected. Dr. Lubomír Lízal, a board member of the Czech National Bank,

We use linear interpolation. It means that we do not have the same value for the long-term exchange rate within a month, but we have changing values within a month that move from one monthly figure to the other one via linear interpolation.

This research was supported by a GAČR grant (402/11/0020). The views of the authors do not necessarily reflect those of the OECD. The usual disclaimer applies.

Data appendix

Notes: ECT denotes the error correction term, while UR is the residual based cointegration test. ECM indicates that the reported statistics refer to the error correction model and the long-run relationship estimated using DOLS, respectively. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. M3 is monetary aggregate. GDPR is real GDP interpolated linearly from quarterly to monthly frequency. IP is industrial production. IRS is short-run interest rates. CPIPPI is the relative price of nontradable goods over that of tradable goods, proxied by the CPI to PPI ratio. PROD is labour productivity calculated using industrial production and employment in industry. All variables (except interest rates) are taken in logs and as the differential against the corresponding Eurozone variable.

(1)

(


Laurent Ferrara
email: laurent.ferrara@banque-france.fr

Clément Marsilli
email: clement.marsilli@banque-france.fr.

Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession

Keywords: JEL Classification: C2, C5, E3 Great Recession, Forecasting, Financial variables, MIDAS approach

, that enables to forecast quarterly GDP growth rates using exogenous variables sampled at higher frequencies. Empirical results show that, overall, stock prices help to improve the accuracy of GDP forecasts by comparison with a standard opinion survey variable, while oil prices and term spread appear to be less informative.

Introduction

In the wake of the financial and banking crisis, most of all industrialized countries have experienced a very severe economic recession during the years 2008 and 2009, sometimes referred to as the Great Recession.

This Great Recession has emphasized the necessary re-assessment of financial markets in their ability to anticipate the business cycle. Regarding the role of financial market variables, there is a huge literature pointing out the leading property of those series to forecast macroeconomic fluctuations (see a review in [START_REF] Stock | Forecasting output and inflation: the role of asset prices[END_REF]. For example, [START_REF] Kilian | The economic effects of energy price shocks[END_REF], reviewed the impact of energy prices shocks, especially oil prices, on macroeconomic fluctuations and [START_REF] Hamilton | What is an oil shock?[END_REF], put forward a non-linear Markov-Switching model to predict US GDP growth rate through oil prices. The term spread has also been widely considered in empirical approaches to assess in a quantitative manner future GDP growth, we refer among others to [START_REF] Estrella | How stable is the predictive power of the yield curve? Evidence from Germany and the United States[END_REF] for the US and to [START_REF] Duarte | Predicting real growth and the probability of recession in the Euro-area using the yield spread[END_REF] or [START_REF] Bellégo | Forecasting Euro Area recessions using time-varying binary response models for financial variables[END_REF] for the euro area. Recently, Farmer (2011) also emphasized the major role of stock prices during the recent Great Recession.

When dealing with variables sampled at various frequencies (quarterly GDP and monthly financial information), the MIDAS approach put forward by Ghysels and his co-authors has proved to be a useful tool (see [START_REF] Ghysels | MIDAS regressions: further results and new directions[END_REF]. Especially in the forecasting framework, several empirical papers have shown the ability of financial information to predict macroeconomic fluctuations; we refer for example to [START_REF] Clements | Macroeconomic forecasting with mixed-frequency data : forecasting output growth in the United States[END_REF] for the US or [START_REF] Marcellino | Factor-MIDAS for now-and forecasting with raggededge data: a model comparisaon for German GDP[END_REF] for Germany.

In this paper, we assess the impact of financial returns as leading indicators for GDP growth for the four main euro area countries (Germany, France, Italy and Spain), as well as for the euro area as a whole. We carry out a forecasting analysis, over the period ranging from 2007q1 to 2009q4, focused on three well-known financial variables, namely oil prices, stock prices, and spread between long and short interest rates, for several forecasting horizons.

The MIDAS framework

The MIDAS approach (MIxed DAta Sampling) has been put forward in the econometric literature by Ghysels and his co-authors (see [START_REF] Ghysels | MIDAS regressions: further results and new directions[END_REF] and enables to use variables of various frequencies in a single univariate model. Especially a MIDAS regression allows to explain a low frequency variables by exogenous variables of higher frequency, without any aggregation procedure and within a parsimonious framework. This approach is typically used in macroeconomics to describe quarterly GDP fluctuations using monthly data that are generally available for short-term analysts, as for example industrial production index, retail sales, housing permits or opinion surveys.

The standard univariate MIDAS regression for explaining a stationary variable (y t ), augmented with a first order autoregressive component, is given by:

y t = β 0 + β 1 B (θ) x (m) t + λy t-1 + ε t (1)
where (x (m) t

) is an exogeneous stationary variable sampled at a frequency higher than (y t ) such that we observe m times (x (m) t

) over the period [t -1, t]. The term B (θ) controls the polynomial weights that allows the frequency mixing. Indeed, the MIDAS specification consists in smoothing the past values of (x (m) t

) by using the polynomial B (θ) of the form,

B (θ) = K k=1 b k (θ)L (k-1)/m ( 2
)
where K is the number of data points on which the regression is based, L is the lag operator such that

L s/m x (m) t = x (m) t-s/m ,
= (θ 1 , θ 2 ), b k (θ) ≡ b k (θ 1 , θ 2 ) = exp θ 1 k + θ 2 k 2 K k=1 exp (θ 1 k + θ 2 k 2 ) (3)
The parameter θ is part of the estimation problem. It is only influenced by the information conveyed by the last K values of the high frequency variable (x (m) t ); the windows size K being an exogenous specification. Other parameterizations of the weight functions have been used in the literature, but we choose (3) since it constitutes a parsimonious and reasonable restriction for which the weights are always positive.

In our work, we use univariate MIDAS regressions designed to accommodate direct multi-step forecasting such that:

y t+h|t = β(h) 0 + β(h) 1 B θ(h) x (m) t + λ(h) y t ( 4
)
where h is the monthly forecast horizon, (y t ) is the quarterly GDP growth, and (x

(m) t
) is the monthly financial return. Within this framework, we get that m = 3.

Empirical Results

In this section, we implement forecasts for quarterly GDP growth rates for each of the main euro area countries (Germany, France, Italy and Spain), as well as for the euro area as a whole, starting from the same set of explanatory variables, namely oil prices, stock prices and the spread between long and short-term interest rates (10-year government bond -3-month interbank rate). The financial time series are detailed in Table 1.

The output growth measure considered in this study is the quarterly growth rate of chain-linked Gross Domestic Product as released by the national instituts of statistics of the four countries, namely: We carry out an in-sample analysis over the period 1990q1-2006q4, then we implement a quasi-realtime experience over the crisis period from 2007q1 to 2009q4. Knowing that financial data are available the last working day of the month, we suppose that forecasts for a given quarter are computed at the end of each month, for 12 horizons ranging from h = 0 (nowcasts computed at the end of the last month of the reference quarter) to h = 11/3 (forecasts computed 11 months before the end of the reference quarter). For each date t, the MIDAS regression optimally exploits the monthly fluctuations of the last K = 10 data of the (x (m) t

INSEE (France) ,
) series using the weight polynomial, given in equation [START_REF] Clements | Macroeconomic forecasting with mixed-frequency data : forecasting output growth in the United States[END_REF].

In this paper, for each of the five economies (France, Germany, Italy, Spain, and euro area), we specify three univariate MIDAS regressions based on the three financial variable returns. The direct multistep forecasting approach used in our work allows parameter estimation using an OLS method and unconstrained Levenberg-Marquardt algorithm on Matlab, for each horizon from h = 0 to h = 11/3 .

In 1, we can draw below some conclusions that seem useful for practitioners.

First, for all five economies, it turns out that financial MIDAS models are able to improve the benchmark ESI MIDAS model for at least one forecasting horizon, although the gain is not uniform through various horizons. In general, the optimal forecast horizon lies between 3 and 5 months, depending on the country: over 4 months for Italy, from 4 to 5 months for Germany and France, and 2 to 4

1 Obviously, note that empirical results are conditioned by this choice.

5 months for Spain. This horizon is often encountered as the optimal horizon for financial variables in the empirical literature that deals with the linkages between financial and macroeconomic variables.

When comparing financial variables according to their forecasting power, it turns out that stock prices generally seem to be the most informative variable in terms of predicting output growth, specially for France, Italy, and the Euro Area. In fact, this Great Recession was initiated by a turmoil on financial markets, equity prices having experienced large falls. Thus this does not seem surprising that stock turns out that this variable is a reliable predictor of turning points in the business cycles, as advocated in many papers (see for example Rubebusch and Williams, 2009), but does not appear as precise predictor of quantitative GDP growth. The same remark holds for oil prices. Indeed, in general, oil prices do not help to improve forecasts by comparison with the ESI, except in the case of Spain for which the oil price variable gives significant better results than the ones obtained from ESI, for forecast horizons ranging from 2 to 4 months (ratios lower than 0.90). Note also that oil prices present a ratio lower than one for Italy and France, for a very short term horizon (the last two months).

Lastly, when comparing countries, it turns out that for France, stock prices provide significantly better results in forecasting GDP than the ESI, for almost each forecast horizon, which is a remarkable result.

Also for the Euro Area as a whole, stock prices lead to an almost systematic gain by comparison with the ESI, except for the 2-month and 3-month horizons. As regards other countries, it is striking to note that those financial variables do not help to improve German GDP forecasts by comparison with the ESI. This result is interesting for practitioners in charge of short-term forecasting for Germany. This means that variables reflecting macroeconomic fundamentals, like for example industrial production index, are likely much more important to get accurate macroeconomic forecasts than financial market variables. For Spain, only oil prices possess a leading pattern between 2 and 4 months. For Italy, we observe a gain as regards stock prices for h = 0, 1 and also the ratio is lower than 0.90 for h = 4.

Conclusions

In this paper, we compare the forecasting ability of three well known financial variables (oil prices, stock prices and term spread), through a MIDAS approach, with a synthetic opinion survey (the European Sentiment Indicator), for various euro area GDPs (Germany, France, Italy, Spain and the euro area as a whole). Indeed, financial and soft data are often used by short-term forecasters due to their timeliness and leading properties, by comparison with hard data such as industrial production.

From the results that we get, it turns out that stock prices generally enable to improve forecasting accuracy by comparison with the soft indicator, while term spread and oil prices do not help in general.

In addition, we show that for Germany financial variables do not present a better predictive power than soft data, while results are mixed for other countries and strongly depend on the forecast horizon.

  and b K is the weight function that can take various shapes. As in Ghysels et al. (2007), we implement the two parameter exponential Almon lag polynomial such as θ

Figure 1 :

 1 Figure 1: Evolution of RMSFE ratios r (h) according h, for the five economies and the three financial variables

Table 1 :

 1 DeStatis (Germany), Istat (Italy), and INE (Spain) and by Eurostat for the euro area at mid-July 2011. Description of variables

	Description

  order to evaluate the accuracy of those forecasts, we compare them with those stemming from a benchmark MIDAS model based on the Economic Sentiment Index as leading indicator (noted ESI MIDAS), a key opinion survey variable to predict output growth, see for example[START_REF] Mourougane | Can confidence indicators be useful to predict short term real GDP growth[END_REF] or Ferrara (2007)1 . As a comparative measure, we present in table 2 the ratios of Root Mean Squared Forecasting Errors (RMSFEs) of GDP growth between the Financial MIDAS models and the benchmark ESI MIDAS model for each h horizon defined by: For a given horizon h, when the ratio r (h) is lower than one, it means that the MIDAS model based on a given financial variable outperforms the benchmark ESI MIDAS model and the opposite prevails when the ratio is greater than one (see results in table2).

			r (h) =	(h) Financial MIDAS (h) RMSFE RMSFE ESI MIDAS
		Forecasting horizons h
		0	1/3	2/3	1	4/3	5/3	2	7/3	8/3	3	10/3 11/3
	France
	Stocks	1,04 1,02 0,95 1,05 0,85 0,91 1,00 0,97 0,99 0,97	0,94	1,01
	Oil	0,96 0,94 1,22 1,14 1,05 1,20 1,05 1,03 1,09 1,06	1,02	1,03
	Spread	1,12 1,04 1,27 1,15 1,03 1,17 1,03 0,98 1,06 1,04	1,00	1,01
	Germany
	Stocks	1,23 1,23 1,09 1,11 0,97 1,06 1,02 1,01 1,01 0,97	0,98	1,01
	Oil	1,21 1,19 1,18 1,24 0,99 0,97 1,01 1,03 1,05 1,01	0,98	1,03
	Spread	1,17 1,20 1,20 1,23 1,13 1,06 1,02 1,00 1,02 1,00	1,00	1,03
	Italy
	Stocks	0,92 0,92 1,02 1,05 0,87 1,01 1,05 1,06 1,07 1,11	1,04	1,04
	Oil	0,95 0,95 1,19 1,23 1,02 1,15 1,09 1,18 1,17 1,24	1,14	1,09
	Spread	1,00 0,95 1,13 1,23 1,02 1,12 1,12 1,11 1,15 1,19	1,08	1,06
	Spain
	Stocks	1,33 1,46 0,96 1,00 0,94 1,11 1,13 1,14 1,18 1,21	1,21	1,33
	Oil	1,44 1,41 0,87 0,93 0,87 1,17 1,25 1,26 1,21 1,23	1,23	1,36
	Spread	1,38 1,36 0,94 1,14 1,04 1,24 1,25 1,22 1,26 1,28	1,29	1,45
	Euro Area
	Stocks	0,91 0,87 1,03 1,05 0,95 0,98 0,98 0,97 0,98 0,97	0,96	0,98
	Oil	1,00 0,96 1,21 1,19 1,10 1,06 1,05 1,05 1,11 1,04	1,02	1,02
	Spread	1,02 0,99 1,20 1,18 1,11 1,11 1,09 1,02 1,11 1,06	0,98	1,03

Table 2 :

 2 Ratio r (h) of RMSFE for the five economies and the three financial variables Starting from the results presented in table2 and figure