Anna Creti

Zied Ftiti
email: zied.ftiti@isg.rnu.tn

Khaled Guesmi
email: khaled.guesmi@ipag.fr

Oil price impact on financial markets: co-spectral analysis for exporting versus importing countries

Keywords: oil prices, stock markets, evolutionary co-spectral analysis JEL Classifications: C14, C22, G12, G15, Q43

, and developed by Ftiti (2010) that is the evolutionary co-spectral analysis. This method allows us to distinguish between short-run and long-run dependence. We find that interdependence between the oil price and the stock market is higher in exporters' markets than the importers' ones.

Introduction

Energy markets have been recently marked by considerable price movements. In particular, from 2001 to 2009, energy prices in international exchange platforms have been rising strongly, and record high prices for oil have been accompanied by important volatility and sudden decrease. This high volatility makes oil one of the major macro-economic factors causing unstable economic conditions for stock markets around the world.

Theoretically, oil price can impact stock markets trough several channels. First, the price of a share being equal to its discounted future cash flow, rising oil prices can increase the interest rate to limit inflationary pressure, tighten the cost of doing business, put pressure on output prices thus decreasing profits [START_REF] Jones | Oil Price Shocks and the Macroeconomy: What Has Been Learned Since 1996?[END_REF]. High interest rates also make bond investments more attractive than stock ones [START_REF] Chittedi | Do oil prices matters for Indoan Stock Markets? An Empirical Analysis[END_REF]. Financialization of oil markets and intensive oil trading can also be factored in [START_REF] Creti | On the links between stock and commodity markets' volatility[END_REF]. All these effects generally 2 trigger a negative relationship between oil and stock markets, which parallels the one between high oil prices and macroeconomic indicators.

Given that the roots of the link between oil and stock markets are of different nature, it is therefore interesting to explore whether the comovements between oil and stock price emerge in a given time frame, either short or long term. Moreover, while there is a sizeable empirical literature on oil and stock markets, less is known about this relationship in the context of oil importing versus oil exporting countries.

Most of the studies focus on oil importing countries, for instance the U.S. However, specific effects on different set of countries are worth investigating. For instance, an increase of oil prices negatively influences the economy of importing countries. When oil price rises, they can experience strong negative consequences and economic recession (Ferderer, 1996).

Instead, an increase in the oil prices influences positively oil exporting countries' economies.

Nevertheless, a decrease in the oil prices exhibits a negative relationship with economic growth of oil producers and can generate political and social instability [START_REF] Yang | An analysis of factors affecting price volatility of the US Oil market[END_REF].

Our study tackles the issues of oil and stock market interdependence in oil-exporting and importing countries by measuring the interaction between oil price and stock markets indices according the evolutionary co-spectral analysis as defined by [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF].

While existing studies applies either VAR or volatility analysis, we choose this technique as it presents several advantages. First, this kind of analysis does not impose any restrictions or pre-treatment of the data (as it is the case of volatility analysis, for instance, which requires the series to be stationary or cointegration techniques which can be only applied to time series data integrated of order one). Second, it does not have an "end-point problem": no future information is used, implied or required as in band-pass or trend projection methods. In addition, the evolutionary co-spectral analysis gives a robust frequency representation of nonstationary process. Finally, the most important advantage of frequency analysis consists of providing information about the time horizon of the interdependence between two series: the analysis delivers as result whether the variables under investigation present short, medium or long-term interdependence. This additional information allows understanding which cycles and periods are more synchronized than others.

Our study clearly shows changes in co-movements between oil prices and stock markets, thus partially contradicting the results of the studies that focus on the negative relationship between oil prices and stock market returns. Overall, our analysis shows two main findings.

Oil price shocks in periods of world turmoil or during fluctuations of the global business cycle (downturn or expansion) seem to have a significant impact on the relationship between oil and stock market prices, both in oil importing or oil exporting country. In exporting countries, our analysis unveils higher and multiple peaks which coincide with very important events (as oil price crisis which that has occurred in 2008). In the case of importing countries the pattern of interaction is clearly smoothed compared to exporting countries. All other oil price shocks originated by OPEC's productions cuts, hurricanes etc., do not seem to have a significant impact on the coherence between oil and stock markets in importing countries. In any case, the interdependency between oil and stock markets is not very strong in the shortrun, but it is revealed more clearly in the medium run. This paper is organized as follows. Section 2 presents Empirical methodology. Section 3 presents the data. Section 4 is concerned to results and discussion. Section 5 concludes.

Related Literature

The relationship between oil price and real economic activity has been widely investigated. [START_REF] Hamilton | Oil and the Macroeconomy Since World War II[END_REF] concludes that positive oil price shocks are a substantial cause for economic recession in the US. After this work, the impact of oil prices dynamic has motivated many studies, among them the ones focusing on the links between oil and stock prices. Most papers, devoted to oil-importing countries, show a negative relationship between oil prices and stock markets activities. One of the first paper exhibiting this relationship is [START_REF] Sadorsky | Oil price shocks and stock market activity[END_REF], who shows that oil prices shocks have symmetric effects on the economy, positive shocks have a greater impact on stock markets and economic activity than do negative oil price shocks.

Since this seminal paper, other studies have either confirmed this finding (as for instance Basher et al, 2010;[START_REF] Chen | Do higher oil prices push the stock market into bear territory[END_REF][START_REF] Elder | Oil price uncertainty[END_REF][START_REF] Jones | Oil and stock markets[END_REF][START_REF] Kilian | The impact of oil price shocks on the U.S market[END_REF]Masish et al., 2011;[START_REF] Wei | Energy, the stock market, and the Putty Clay investment model[END_REF] or pointed out that the impact of oil price on stock markets can be weakly significant [START_REF] Aspergis | Do structural shocks affect stock prices?[END_REF][START_REF] Miller | Crude oil and stock markets: stability, instability, and bubbles[END_REF].

In the following, we detail some of the empirical models on that topic. Hammoudeh et al. (2004) examines the long-run interaction between five GCC stock markets (Bahrain, Kuwait, Oman, Saudi Arabia, and UAE) and three global factors (oil spot price indices, US 3-month Treasury bill rate, and S&P index). They apply Cointegration tests and VEC model to weekly data from February 1994 to December 2004. The authors find that oil price movements do not have direct effects on any GCC stock markets, while the latter counts for less than 4% of the variations in oil prices after a 20 week period.

Using a multi-factor approach, [START_REF] Syed | Day-of-the-week effects in emerging stock markets[END_REF] study the impact of oil price changes on emerging stock market. They argued that oil price risk impacts stock price returns. [START_REF] Narayan | Do the US macroeconomic conditions affect Asian stock markets?[END_REF] use the EGARCH method to model daily data of crude oil prices and conclude that shocks influence constantly and asymmetrically the volatility over the longterm period. Asymmetric effect indicates that positive shocks affect oil price differently than negative shocks. [START_REF] Chiou | Jump dynamics and volatility: Oil and the stock markets[END_REF] examine the asymmetric effects of WTI daily oil prices on Standard&Poor 500 stock returns. Using the Autoregressive Conditional Jump Intensity model with expected, unexpected and negative unexpected oil price fluctuations, they find that high fluctuations in oil prices have asymmetric unexpected effects on stock returns. [START_REF] Malik | Volatility transmission between oil prices and equity sector returns[END_REF] rely on bivariate GARCH models to estimate the volatility transmission between weekly WTI oil prices and equity sector returns and find evidence of spillover mechanisms. [START_REF] Choi | Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment[END_REF] extend the time-varying correlations analysis by considering commodity prices of Brent oil, WTI oil, copper, gold and silver, and the S&P 500 index. They show that commodity correlations have increased since 2003, limiting hedging substitutability in portfolios. Finally, [START_REF] Arouri | Oil prices, stock markets and portfolio investment: evidence from sector analysis in Europe over the last decade[END_REF] used a GARCH model to inspect the effect of oil prices on European sector returns rather than only on aggregate stock market index returns. They concluded that oil prices tend to exercise a significant influence on various European sectors; however, the magnitude and the direction of the effect differ from one sector to another.

All the above mentioned models do not consider different group of countries on the basis of the weight of oil in their economy. A few papers tackle this issue. [START_REF] Bjornland | Oil price shocks and stock market booms in a oil-exporting country[END_REF] shows that a 10% increase in oil price result in 2.5% of stock market index increase in Norway, an oil-exporting country. Yoon and Park (2011) and [START_REF] Park | Oil prices and stock markets in the U.S. and 13 European countries[END_REF] argue that the negative effect of oil price on stock markets only holds for oil-importing countries, but their analysis is limited to a few countries (Norway, Korea, Saudi Arabia and Russia). [START_REF] Filis | Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries[END_REF] investigate time-varying correlations between Brent oil prices and stock markets on both oil-importing and oil-exporting countries. Using multivariate asymmetric DCC-GARCH approach, they find that the conditional variances of oil and stock prices do not differ for oilimporting and oil-exporting economies. However, time-varying correlations depend on the origin of the oil shocks: the response from aggregate demand-side shocks is much greater than supply-side shocks originated by OPEC's production cuts. [START_REF] Wang | Oil price shocks and stock market returns: Evidence from oil-importing and oil-exporting countries[END_REF] use VAR analysis impulse response analysis to investigate the impact of oil demand and supply shocks in several oil-importing and oil-exporting countries. The author show that stock markets of oil importing countries react to oil supply shocks, but the effect is short lived. Demand shock affect stock market of both group of countries. Concerning specifically oil-exporting countries, Al Janabi and al. (2010) use bootstrap test for causality to study non-normal financial data with time-varying volatility. They conclude that oil prices do not tend to affect these stock markets and thus oil prices cannot be used as predictors for the Gulf Cooperation Council stock markets. More recently, [START_REF] Arouri | Oil prices and stock markets in GCC countries: Empirical evidence from panel analysis[END_REF] study the impact of oil prices shocks on Gulf countries, with a boostrap panel cointegration model, and provide evidence that the stock market performance of the Gulf markets is affected by positive oil price shocks.

Similar results were also documented by [START_REF] Bashar | Wild oil prices, but brave stock markets! The case of GCC stock markets[END_REF] and Hammoudeh and Aleisa (2004).

Our paper takes a novel perspective in assessing the links between oil prices and stock markets. We use a technique which has not yet been used so far in that context, that is evolutionary co-spectral analysis which is a time frequency approach. Contrary to time series models, our approach allows for a representation of non-stationary series without any risk of misspecification. Indeed, differently from traditional time series model-such as ARMA, Multivariate GARCH (CCC, DCC…), evolutionary spectral analysis does not depend on assumption on the data. The evolutionary spectral analysis does not show an "end-point problem": no future information is used, implied or required as in band-pass or trend projection methods. The most important contribution with respect to traditional time series analysis consists of the decomposition of series on two dimensions, that is frequency and time occurrence of the dependence. This allows to study time series according to different horizons, for instance short and medium-term. Therefore, we aim at complementing the existing studies to uncover whether the results of the previous literature are robust to model specification, in particular in the dynamic dimension of the oil-stock market relationship when importing and exporting countries are accounted for.

Empirical analysis and data

As mentioned in the introduction, our objective is to measure the dynamic interaction between oil price series and stock market index for oil importer and exporter countries. The interaction between these series is measured according to a frequency approach based on the theory of evolutionary co-spectral analysis of [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF]. We measure co-movement between series by the coherence function. We then propose a time-varying measure of this variable.

Theory of the evolutionary Co-spectral (Priestley and Tong : 1973)

According to [START_REF] Priestley | Evolutionary spectra for non stationary process[END_REF], a non-stationary discrete4 process or a continuous5 process can be written as equation ( 1). [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF] extend the theory of the evolutionary spectral analysis of [START_REF] Priestley | Evolutionary spectra for non stationary process[END_REF][START_REF] Priestley | Design relations for non stationary processes[END_REF], presented by [START_REF] Ftiti | The inflation Targeting effect on the inflation series: ANew Analysis Approach of evolutionary spectral analysis[END_REF], to the case of a bivariate non-stationary process. In this sub-section, we summarize this theory.

Consider, for example, a bivariate continuous parameter process  ,

) ( ), ( t Y t X
in which each component is an oscillatory process. Each component can be written as follows:

     ) ( ) ( ) ( 1 1 , w dZ e w A t X x iwt x t (1)      ) ( ) ( ) ( 2 2 , w dZ e w A t Y y iwt y t (2) where ) ( )] ( ) ( [ ), ( ] ) ( [ , ) ( ] ) ( [ 0 )] ( ) ( [ )] ( ) ( [ )] ( ) ( [ 1 * 1 1 2 1 1 2 1 2 1 2 * 1 2 * 1 2 * 1 w d w dZ w dZ E and w yy d w dZ E w xx d w dZ E w w for w dZ w dZ E w dZ w dZ E w dZ w dZ E xy y X y x y x y y x x           with  *
. denoting the conjugate function of  . .

Let

y x F F ,
denote respectively the families of oscillatory functions as:

 t,x (w 1 )  A t,x (w 1 )e iwt   ,  t,x (w 1 )  A t,x (w 1 )e iwt  
. [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF] define the evolutionary power cross-spectrum at time t with respect to the families

y x F F , , ) ( , w dH XY t by ) ( ) ( ) ( ) ( * , , , w d w A w A w dH XY Y t X t XY t   (3) Further, if   ) ( ), ( t Y t X
is a bivariate stationary process, so that y x F and F may be chosen to be the family of complex exponentials, namely

  iwt Y x e F F   , ) ( , w dH XY t
reduces to the classical definition of the cross-spectrum. Thus, for each t, we may write

)] ( ) ( ) ( ) ( [ ) ( * * , , , w dZ w A w dZ w A E w dH Y Y t X X t XY t



(4) [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF] (6) and h t,XY (w)dw may then be termed the evolutionary cross-spectral density function.

Estimation of the evolutionary Co-spectral density function

The evolutionary cross-spectral density function estimation, which we develop here, is an extension of [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF] from the estimation of the evolutionary spectral density function in the univariate case, such as developed by [START_REF] Priestley | Evolutionary spectra for non stationary process[END_REF][START_REF] Priestley | Design relations for non stationary processes[END_REF]. In our analysis, we are interested in time series as discrete process. 6 We analyse two pairs of seriesoil price series and stock market index of a country. Therefore, we detail the procedure to estimate the evolutionary cross-spectral density function.

Let a non-stationary discrete bivariate process   The estimation of the evolutionary cross-spectral density function needs two filters. For the discrete univariate process, [START_REF] Priestley | Design relations for non stationary processes[END_REF] gives two relevant windows. These are relevant filters and they are tested by several works, such as [START_REF] Ahamada | Tests for covariance stationarity and white noise, with application to Euro/US Dollar exchange rate[END_REF], [START_REF] Ftiti | The inflation Targeting effect on the inflation series: ANew Analysis Approach of evolutionary spectral analysis[END_REF]. For the discrete bivariate process, [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF] adopt the same choice, that is:

) ( ), ( t Y t X have the Gramer representation for each     w  :           ) ( ) ( ) ( ) ( , , w dZ e w A Y and w dZ e w A X Y iwt Y t t X iwt X t t with ) ( )] ( ) ( [ ), ( ] ) ( [ , ) ( ] ) ( [ 0 )] ( ) ( [ )] ( ) ( [ )] ( ) ( [ 1 * 1 1 2 1 1 2 1 2 1 2 * 1 2 * 1 2 * 1 w d w
              2 / ' 0 2 / ' ' / 1 0 ) 2 /( 1 ' T v si T v si T W et h u si h u si h g v T u    (7)
Then, the estimation of the evolutionary cross-spectral density function is as follows:

) , ( ) , ( ) ( ) ( ˆ' , v t w U v t w U v W w h Y Z v X T XY t      (8) with       Z u u t iw x du e u t X u g w t U ) ( ) ( ) ( ) , ( (9)
      Z u u t iw Y du e u t Y u g w t U ) ( ) ( ) ( ) , ( (10)
In this paper, we take 20 ' 7   T and h

. We make the same choice7 as [START_REF] Artis | Instability of Velocity of Money, a New Approach Based on the Evolutionary Spectrum[END_REF], Priestley (1995), [START_REF] Ahamada | Tests for covariance stationarity and white noise, with application to Euro/US Dollar exchange rate[END_REF], Essaadi and Boutahar (2008) and [START_REF] Ftiti | The inflation Targeting effect on the inflation series: ANew Analysis Approach of evolutionary spectral analysis[END_REF].

According to [START_REF] Priestley | Non-Linear and Non-Stationary Time Series Analysis[END_REF] In order to respect conditions (i) and (ii), we choose { i t } and { j w } as follows:

  I i i i t 1 20 18    Where        20 T I
and T the sample size 7 1

)) We finally have a co-spectral density function in 7 frequencies. However, we retain only two frequencies reflecting respectively short-term and medium-term. Indeed, the first frequency 20 /  traduces the medium-term interdependence and the frequency 20 / 4 traduces the short-term one. The shift from the frequency domain to the time domain takes place through the following formula:

1 ( 3 1 ( 20          
  / 2
, where  is the frequency. For example, the frequency

Coherence Function

According to [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF], the evolutionary cross-spectral density function may be written as:

) ( ) ( ) ( , , , w Q i w C w h XY t XY t XY t   (11)     ) , ( Im ) ( ) , ( ) ( , , t w h w Q t w h R w C XY XY t j XY XY t   (12)
and the real-valued functions . 8 The coherency function is defined by the following expression: [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF] or, more generally, as a measure of the linear relationship between corresponding components at frequency w in the processes  

    2 / 1 2 2 * 2 / 1 , , , , ) ( ) ( )] ( ) ( [ ( ) ( ) ( ) ( w dZ E w dZ E w dZ w dZ E w h w h w h w C Y x Y YY t XX t XY t XY t   (13)
) (t Y and   ) (t X .
The estimation of the coherency function is based on the estimation of the cross-spectral density function between two processes  

) (t Y and   ) (t X
and the estimation of the autospectral density function of each process. So, the estimation coherency can be written as follows:

  2 / 1 , , , , ( ) ( ˆ) ( ) ( ˆw h w h w h w C YY t XX t XY t XY t  (14)

Data description

In this study, we use monthly data for oil prices and stock market indices. The sample consists of oil-importing (US, Italy, Germany, Netherland and France) and exporting countries (Emirate Arab Units, Kuwait Saudi Arabia and Venezuela). To select the sample, we have adopted two criteria: (i) the presence of a well-established stock market and (ii) a rank in the top 20 oil-importers and exporters countries.

The Brent crude oil index is used as it accounts for the 65% of the world oil daily production (IMF, 2010). The data range from 03/09/2000 to 03/12/2010 and have been extracted from Federal Reserve Bank of Saint Louis and Datastream Database. The time horizon depends on data availability and includes, in addition to the major economic crisis and political events such as the different monetary and financial crises in Asian and Latin American and Middle East region, the first and the Gulf war, the Russian economic crisis and the terrorist attack in US. This will allows making important conclusion regarding the link between the dynamic of oil prices and the financial market returns. Taking into account the peaks and troughs of oil prices and the events that have taken place during our period of study, the relation between oil and stock market indexes exhibit some noteworthy aspects.

Empirical findings and discussions

Oil price and stock market movement

First, in exporting countries (Figure 2), we remark that stock markets have the same As preliminary result, we notice that the visual inspection of the series does not provide a clear distinction between stock market performance and oil prices on oil-importing and oilexporting countries. However, we observe that stock indices of importing countries (Figure3)

do not move in the same direction with oil prices. For example, during the sub-period 2000-2003, oil prices exhibited an increase, whereas the majority of the stock markets showed a decrease. For the sub-period 2007-2008, stock prices decrease when the oil prices were increasing constantly.

The results of dynamic coherence

The analysis resulting from the time-varying coherence functions as computed from equation ( 14) between each stock market index and the Crude oil prices is shown in Figures 4 and5, for oil-exporting and importing countries respectively. According to the graphs in Figures 4 and5, we observe a divergence between the mediumterm interdependence of and the short-term one. More precisely, for all studied markets (exporting and importing ones), the interdependence between oil prices and stock market indices is less important in the short term than in the medium term. In the short term, the average interdependence does not exceed 10%, while in the medium-term, on average, it reaches more than 40%. Hence, for both importing and exporting countries, stock market indices react weakly to transitory fluctuations of oil price (short-term interdependence). Stock market indices for all countries, instead, react strongly to persistent fluctuations of oil price (medium-term interdependence).

In the short-run, there is no difference in the pattern of dynamic coherence function between exporting and importing price. The dynamic interaction between oil price and stock market indices is weak in the short-run, but it rises slightly in crisis period. In fact, the shortrun dynamic of stock market, for both importing and exporting countries, does not depend strongly on oil price in stable periods. However, in crisis periods, stocks markets are affected by oil price, even though this interdependency is not very strong in the short-run. For example, we observe a rise in the short-run coherence pattern around the occurrence of some exogenous shocks (see Tables 1 and2 in the Appendix).

The nature of dynamic interaction is different in medium-term. According to Figures 4 and5, we observe a higher interdependence, in the medium-run between oil price and stock market indices of all countries. However, there is some difference in the pattern between exporting and importing countries. Indeed, for exporting countries (Figure 4), we have different regimes in the time-varying dynamic coherence; nevertheless, there is a fewer periodicity (or fewer regimes) for the case of importing countries. In other words, exporting countries experience higher and multiple peaks which coincide with very important events (such as the 2008 oil price crisis). Moreover, in the case of importing countries the pattern of interaction is clearly smoothed compared to exporting countries. We conclude that stocks markets indices in exporting countries are more interdependent to oil price than importing countries, as in the former countries stock markets are dominated by oil companies, much larger than other listed companies.10 This effect is novel with respect to the results obtained by [START_REF] Wang | Oil price shocks and stock market returns: Evidence from oil-importing and oil-exporting countries[END_REF].

Concerning exporting countries, we observe a peak in coherence pattern observed around the year 2001 for all countries (40%). This high level of coherence between oil and stock market prices is due to the rapid increase in the housing market and construction industry, a result of decreasing interest rates worldwide in 2000. In addition, the 2001 attack can explain the higher coherence level observed in this period.

In 2003 there is a relatively less high of the coherence pattern in the case of exporting countries (Dubai, Kuwait and South Africa). This result is explained by the war in Iraq in Mars 2003 and PdVSA Strike in Venezuela. We observe a breakdown, for all exporting countries, in coherence pattern at the date of 2006. We explain this decrease in interdependence between oil price and stock market index by the military attack in Nigeria which caused the shutting down of more than 600 000 billion barrel per day.

Another period of interest is the one running from 2006 until mid 2008, characterized by high oil prices due to rising demand, mainly by China. The coherence level shows an increasing and positive pattern for all countries. This aggregate demand-side oil price shock has a positive effect on stock markets (both in oil-importing and oil-exporting countries), as it signals an increase in world trade. These findings are in line with [START_REF] Hamilton | Causes and consequences of the oil shock of 2007-08[END_REF] and [START_REF] Kilian | The impact of oil price shocks on the U.S market[END_REF], who suggest that aggregate demand-side oil price shocks, originated by world economic growth, have a positive impact on stock prices.

From mid-2006 and early 2009, the coherence pattern rises sharply and reaches a higher value (higher than 40%) for all stock markets, both in exporting and importing countries. The main event in this period is the global financial crisis initiated from the export of US mortgages to the rest of the world, as asset backed securities, which can be regarded as an aggregate demand-side oil price shock (International Energy Agency 2009). The higher interaction between oil and stock market prices can be explained by the fact that such crisis caused stock markets to enter bearish territories and caused oil prices to decline heavily, as also documented by [START_REF] Creti | On the links between stock and commodity markets' volatility[END_REF].

There are only three periods of noteworthy higher or lower coherence between oil prices and stock markets for exporting countries. showing little difference 11 between importing and exporting countries in term of coherence pattern of oil and stock market prices.

The explanation of such findings can depend on the boom that the housing market experienced in 2000 creating a positive environment for world markets and at the same time a high demand for oil, driving the prices of both markets to higher levels. The 9/11 terrorist attack and the second war in Iraq also created significant uncertainty in all economies, causing similar movements in their stock markets and thus similar coherence with oil prices. In addition, the Chinese growth and its impact in the world trade caused euphoria in all stock markets regardless the country of origin. Similarly, the last world financial crisis influenced all stock market similarly and thus their co-movements.

Our analysis shows two main findings. Oil price shocks in periods of world turmoil or during fluctuations of the global business cycle (downturn or expansion) seem to have a significant impact on the relationship between oil and stock market prices, regardless the status of the market (i.e. belonging to an oil-importing or oil-exporting country). However, all other oil price shocks originated by OPEC's productions cuts, hurricanes etc., do not seem to have a significant impact on the coherence between oil and stock markets in importing countries.

Moreover, aggregate demand-side oil price shocks (housing market boom, Chinese economic growth, and the latest global financial crisis) cause a significant higher correlation between stock market prices and oil prices. Important precautionary demand side oil price shocks (i.e. second war in Iraq, terrorist attacks) tend to cause higher coherence but with a less magnitude compared to aggregate demand-side oil price shocks. The origin of the shock seems to be an important determinant of the correlation magnitude between oil prices and stock markets, as long as the oil shocks originate from major events of world turmoil, such as wars or changes in the phase of the global business cycle.

Overall, the results of the previous literature are confirmed concerning the impact of oil shocks on stock markets of oil-importing and oil-exporting countries, whereas for the case of supply shocks our findings show some aspects that have been neglected so far. In particular, we stress the role of crisis periods in the oil prices as drivers of the comovements between oil and stock markets. Moreover, distinguishing the pattern of interdependence in the short-term frame (10 months) with respect to the medium-term one (3 years and one quarter), we show 11

The coherence level of importing countries is smoother compared to those of exporting countries during 2003-2005 periods. 20 that oil price shocks mainly have medium-lived effects on stock markets, a result that complements the existing ones.

Conclusion

The paper investigates the issues of oil and stock market interdependence in oil exporting and importing countries by measuring the interaction between oil price and stock markets indices according the evolutionary co-spectral analysis as defined by [START_REF] Priestley | On the analysis of bivariate non-stationary processes[END_REF]. The dataset consists of monthly stock and oil prices from oil importing countries (US, Italy, Germany, Netherland and France) and oil exporting countries (Emirate Arab Units, Kuwait Saudi Arabia and Venezuela), from 03/09/2000 to 03/12/2010. Our analysis does not impose any kind of restrictions or pre-treatment of the data (as it is the case of volatility analysis, for instance, which requires the series to be stationary or cointegration techniques which can be only applied to time series data integrated of order one). Moreover, no future information is used, implied or required as in band-pass or trend projection methods. In addition, the evolutionary co-spectral analysis gives a robust frequency representation of non-stationary process. Finally, the most important advantage of frequency analysis consists of providing information about the time horizon of the interdependence between two series: the analysis deliver as result whether the variables under investigation present short, medium or long-term interdependence. This additional information allows understanding which cycles and periods are more synchronized than others.

For all studied markets studied, the interdependence between oil prices and stock market is shown to be a medium-term phenomenon. Moreover, while there is substantial homogeneity of the interdependence patterns within the two groups of countries under investigation, our analysis shows that high oil prices deriving from demand shocks move together with stock prices, especially in exporting countries. Supply shocks cause higher coherence only in exporting countries. Therefore, in terms of diversification potential, oil is not always countercyclical with respect to stock markets, as generally predicted by the previous literature. Oil can have this role in importing countries, when high oil prices originate from supply shocks. At the opposite, if the shock originates from demand, oil prices and stock market tend to move together, with a strength that varies demanding on the origin of the shock, in both importing and exporting countries. In such scenario, oil does not have a save heaven role to counteract changing returns of a portfolio of stocks in none of the countries studied.



  To respect the (ii) condition, we inspect instability in these frequencies;

Figure 1 Fig 1 .

 11 Fig 1. Brent crude oil price, in dollars, from 2000 to 2010

Fig

  Fig 2. Stock Market indices of Oil-Exporting Countries

  movement of oil prices fluctuation under the sub-period 2000-2005. We observe a period of oil price and stock market prices increase. However, for the sub-period 2005-2010, oil prices were increasing constantly. In addition, the period 2005 until mid-2007 is characterized mainly by a continuous oil price increase, as well as increased stock market prices. During mid-2006 until early 2007, when an oil price trough is observed, stock markets also exhibited a decrease in their price levels. Moreover, during 2007 until 2009, both oil prices and stock indexes are bullish. Finally, after the sub-period 2008-2009, both oil and stock market prices experienced a bearish performance. Venezuela exhibits a slightly different pattern, given a weaker development of its financial markets.

Figure

  Figure 4: the dynamic coherence functions between stock market index and Oil price of exporting countries

  These are the early 2000 until 2001 (aggregate demand-side oil price shocks -higher coherence), 2003-2005 higher coherence (aggregate demand-side oil price shocks -higher coherence), and 2007-2008 (aggregate demand-side oil price shock -positive correlation). The years 2003-2005 represent the sole period

  extend the above relation to the case of a non-stationary bivariate process where the amplitudes are time-dependent; correspondingly, the cross-

	spectrum is also time-dependent. Clearly,	, dH XY t	(	) w	is complex-valued, and, by virtue of the
	Cauchy-Schwarz equality, we have immediately that
	, dH XY t	(	w	)	2		dH	t,	xx	(w)	dH	t,	YY	(w),	for each t and w	(5)
	If the measure		XY	) (w	is absolutely continuous with respect to the Lebesgue measure, we
	can write, for each t:

A discrete process corresponds to a process of which the value of T is countable. Indeed, a time series is considered as a discrete process.

A continuous process is a process used to describe the physical signal.

This choice of values is justified by the fact that they respect the conditions (i) and (ii).

For more details see[START_REF] Ftiti | The macroeconomic performance of the inflation targeting policy: An approach based on the evolutionary co-spectral analysis (extension for the case of a multivariate process)[END_REF].

The series appears to have a non-stationary behavior in the sense that it does not converge towards its long term means while the series in first difference fluctuates around zero and seems to be stationary.

This phenomenon can be attenuated by the fact that oil-exporting countries depend on export revenues that decline smoothly, due to the low demand elasticity of oil demand[START_REF] Bjornland | Oil price shocks and stock market booms in a oil-exporting country[END_REF] Park and Ratti, 2011).

Appendix


Rustam Jamilov
email: jamilovrustam@gmail.com

Balázs Égert
email: balazs.egert@oecd.org.

Interest Rate Pass-Through and Monetary Policy Asymmetry: A Journey into the Caucasian Black Box 1

Keywords: Interest Rate Pass-Through, Asymmetric Adjustment, Caucasus JEL Classification: E43, E52, N25

This paper analyses the interest rate pass-through for five economies of the Caucasus -Armenia, Azerbaijan, Georgia, Kazakhstan, and Russia. Employing an autoregressive distributed lag (ARDL) specification to monthly data, we find that the interest rate passthrough is systematically incomplete and sluggish, probably due to macroeconomic instability and low banking sector competition. It is not clear whether pass-through has improved over time and asymmetric adjustment is found to characterize the pass-through only occasionally. Overall, our results show a considerable degree of cross-country heterogeneity in the size and speed of the pass-through.

Introduction

An informal yet a universally accepted professional conventional wisdom has been reached: monetary policy can impact on the real economy, but we cannot always explain how exactly [START_REF] Mishkin | The Transmission Mechanism and the Role of Asset Prices in Monetary Policy[END_REF]. But considerable progress has been made in better understanding the monetary transmission mechanism. The main channels through which monetary policy innovations are transmitted to the real economy are i.) the change in the long-term real interest rate, which influences private households' consumption and saving decisions and through the cost of capital investment decisions through substition, income and wealth effects, ii) the credit channel, which either through bank lending or market financing affects access to external financing of private businesses, and iii) the exchange rate channel, which by altering relative prices influences inflation and, through balance sheet effects, investment and consumption decisions. Bank lending and long-term market interest rates have an important role to play in these three channels. Whether and to what extent monetary policy will be able to convey its impact through these transmission channels will depend crucially on the impact of monetary policy rate innovation on bank lending and market interest rates.

Obviously, a weak pass-through from the policy rate to market rates will weaken the influence of monetary policy on the real economy through these three channels. Empirical research suggests that the interest-rate pass-through is sluggish and exhibits strong asymmetries in advanced economies [START_REF] Debondt | Interest Rate Pass-Through: Empirical Results for the Euro Area[END_REF]Sander andKleimeier, 2004a).

There is a large body of literature focusing on advanced countries and the empirical literature on former communist countries has been also expanding over the past years. This literature suggests that i.) the pass-through in economies of the former soviet bloc grew stronger over time, even though its size has remained incomplete especially for bank lending and deposit rates, ii.) there are strong asymmetric effects and iii.) the characteristics of the interest rate pass-though varies to a large extent across countries. 1 There are good reasons to think that the interest-rate pass through is far to be complete in developing economies, because of shallow and illiquid security markets and an underdeveloped banking sector. But many developing and transition economies carried out financial market reforms, which, through a fast developing banking sector and capital markets, may have resulted in an improved transmission from the policy rate to banking and market interest rates. Indeed, [START_REF] Gigineishvili | Determinants of interest rate pass-through: do macroeconomic conditions and financial market structure matter? IMF working paper[END_REF] showed on a large sample of countries that heterogeneity regarding the size and speed of interest-rate pass-through may be explained to a considerable extent by differences in macroeconomic conditions and financial market development.

In this paper, we contribute to the ever growing literature on the interest-rate pass-through by evaluating its empirical importance for the Caucasus region, namely Armenia, Azerbaijan, Georgia, Kazakhstan, and Russia, placing a particular emphasis on the size of the passthrough in the long run, its short-term speed of adjustment and possible asymmetric responses. To our knowledge, this is the first attempt to model interest rate pass-through for so many countries in the Caucasus in a coherent manner. 2 It is indeed useful to bunch these countries together because they have a number of common features. In the aftermath of the disintegration of the Soviet Union in the early 1990s, weak rule of law, ineffective financial sector regulation, underdeveloped capital markets, little competition in the banking sectors, large informal sectors, dollarization, high levels of structural inflation, capital controls and fixed exchange rate regimes are all the vices that have plagued the Caucasian states for the past two decades. When analyzing interest rate pass-through in those countries, we might expect an incomplete yet functioning pass-through because of the progress made in many areas over the last two decades.

The remaining of this paper is structured as follows: Section 2 presents briefly the different stages of the interest rate pass-through and sets out our main research questions. Section 3 discusses estimation issues. Section 4 describes our dataset. Section 5 reports and discusses the estimation results. Section 6 finally provides some concluding remarks.

The interest rate pass-through

The interest rate pass-through can be decomposed into two stages. The first stage measures how changes in the monetary policy rate are transmitted to short-and long-term market rates.

The stability of the first stage depends to a large extent on the stability of the yield curve: If the term structure (whether negative or positive sloping), does not change over time, the passthrough from policy rates to market rates will remain unchanged, all things being equal. The second stage describes how changes in the market rates, through the costs of banks' funding, influence bank deposit and lending rates. For bank lending rates, if banks refinance themselves in the money markets, money market rates will have an impact of short-term loan rates. Similarly, to the extent that government bond yields are considered as opportunity costs for banks, they will serve as a useful benchmark for loan rates of longer maturity. For bank deposit rates, the connection between market rates and deposit rates is warranted by the possibility that households and non-financial businesses can hold their financial assets in government securities, rather than in bank deposits of comparable maturity. Positing a stable yield curve helps link monetary policy rates directly to retail (deposit and loan) rates.

In this paper, we test the first stage of the pass-through (the link between the policy rate and short-and long-term market rates) and the overarching pass-through (the relation between the policy rate and bank deposit and lending rates). By doing so, we assume a stable yield curve in the Caucasian countries. It should be noted that it is difficult to study the link between the policy rate and short-term money market rates for all countries because money markets practically do not exist in Armenia, Azerbaijan, Georgia, and for a lesser extent Kazakhstan:

banks mainly refinance themselves from the central bank rather than from the interbank money market.

There are indeed a number of reasons why the interest pass-through may not be perfect. First, macroeconomic conditions usually influence the degree of the pass-through in the longer run [START_REF] Egert | Interest rate pass-through in central and Eastern Europe: Reborn from ashes merely to pass away?[END_REF]. During periods of high economic growth, banks are quicker to adjust lending and deposit rates in response to changes in monetary policy rates. On the other hand, macroeconomic instability and high interest rate volatility weaken the pass-through, as banks may want to wait longer before adjusting their retail rates.

Second, if monetary policy is not credible enough, and if the central bank has a history of regular interventions in the market, one-time shifts in interest rates may not be able to convince economic agents that the change of the policy stance is permanent and not simply temporary. Banks will thus respond sluggishly to policy rate innovations, expecting the central bank to shift yet to a new position in the near future. In addition, which is particularly true for the case of Caucasus, large clients of commercial banks usually include other large banks themselves (cross-depositing between systemic banks is very common in this region). 3 The same method is observed in state institutes or state-connected institutes and wealthy individuals. In short, large and important clients are protected from abrupt changes in main retail interest rates, as banks attempt to smooth the effect of transition from the old interest rate regime onto the new one.

3 For general reference, this is not the same as the interbank money market. In fact, this is cross-depositing; no flow of credit or general financial activity. Certain banks may diffuse capital holdings by placing portions of the wealth portfolio in several locations, while providing deposit storage for other banks to do the same. If, for example, the two banks actually belong to the same set of owners, which is a common situation, the financial complication is really just an illusion; there is no interbank market, just cross-interconnection in order to diffuse real owners of the underlying capital and avoid over-concentration.

The response of retail interest rates may also overshoot the policy rate innovation, i.e. the pass-through is higher than unity. This could happen if banks decide to charge higher interest rates to offset risks associated from asymmetric information [START_REF] Debondt | Interest Rate Pass-Through: Empirical Results for the Euro Area[END_REF]. Also, smaller banks may be constrained by access to quality capital, and would thus demand higher risk premium on lending rates, thus compensating for their exclusion from external sources of funding. Banks may be also expecting the central bank to follow a trend in its cyclical policy stance of either expansion or contraction, given a macroeconomic condition. If the economy is on a rising trend, then banks can reasonably expect the central bank to raise benchmark interest rates in order to prevent overheating, so banks can adjust rates in anticipation of further rounds of interest rate hikes. In fact, interest rate overshooting happens because banks price in the expectations of a continuing monetary policy trend of either expansion or contraction.

The major questions asked in this paper can be summarized as follows:

(1) Is the long-run interest rate pass-through complete in the Caucasus?

(2) How fast do interest rates adjust to their long-run equilibrium?

(3) Is there evidence of asymmetric adjustment for any interest rate pairs in any of the countries under study?

(4) Is the pass-through different for different rates for a given country?

(5) Is the pass-through different for different maturities and for rates on assets denominated in domestic currency and the US dollar?

(6) Do the size and speed of adjustment of the pass-through change significantly over time, and if yes, do the empirically identified structural breaks help explain this?

(7) Can we observe any signs of regional convergence in the interest rate pass-through?

Estimation Issues

We check the presence of structural breaks in our series, which could seriously influence the results of unit root tests [START_REF] Kleimeier | Regionalisation versus globalisation in European financial market integration: Evidence from co-integration analyses[END_REF]. We employ the Quandt-Andrews test for structural break detection, which is based on an F-test that determines the exact timing of the structural break [START_REF] Quandt | Tests of the hypothesis that a linear regression system obeys two separate regimes[END_REF][START_REF] Andrews | Tests for parameter instability and structural change with an unknown change point[END_REF][START_REF] Diebold | Testing structural stability with endogenous breakpoint -A size comparison of analytic and bootstrap procedures[END_REF][START_REF] Hansen | Tests for parameter instability in regressions with I(1) processes[END_REF].

For all interest rates we perform the Augmented Dickey Fuller test (ADF) in order to test for the presence of a unit root, for the full sample period and for the pre-break and the post-break periods. This helps understand whether our series are non-stationary in either of the two sub-periods and how stationarity has evolved over time. The structural breaks are also used for measuring the interest rate pass-through: estimations are carried out for the full sample and the subsamples separated by structural breaks.

As the series turn out to have a stochastic trend, cointegration is used to test for long-term relationships between policy and market/retail rates. For this purpose, we use the bounds testing approach developed by [START_REF] Pesaran | Bounds Testing Approaches to the Analysis of Level Relationships[END_REF]. We estimate the following autoregressive distributed lag (ARDL(p,q)) model:

∑ ∑ (1)
where is the market or retail rate (interbank rate, government bill/bond rate, bank deposit or lending rate) and is the policy rate (repo rate or the refinancing rate, depending the country).

The test of cointegration is based on F-statistic, which tests the null hypothesis of no cointegration against the alternative hypothesis of . For every significance level there are two sets of critical values. If the F-statistic exceeds the upperbound critical value, then the null hypothesis is rejected. If the F-statistic is below the lowerbound, the null hypothesis cannot be rejected, implying no long-term relation linking the interest series. Finally, if the F-statistic is between the two bounds, the test has no conclusive

result. An alternative way to check for cointegration is to look at the sign and the size of the error correction term from the error correction model ( ). A statistically significant and negative error correction term implies that the variables are linked via a long-run relation [START_REF] Kremers | The Power of Cointegration Tests[END_REF].

The question of asymmetric adjustment has been a focal point of the literature for the past several years. 4 We therefore also test for the presence of several types of asymmetries.

Asymmetric effects can arise in response to decreasing or increase policy rates in the following parameters: a) the speed of adjustment (error correction term) to the long-run relationship, and b) the short-term dynamics of the first-differenced lagged market/retail rates. Equation ( 2) can be extended along these lines as follows:

(

) [ ( ) ∑ ∑ ] [ ( )] [ ( ) ∑ ∑ ]
where I(•) is a Heavyside function which takes on the value of one if the argument is true and zero otherwise. Specification (3) allows for asymmetric short-run adjustment to the long-run equilibrium, represented by coefficients and . and for asymmetries in the short-run dynamics and for decreasing policy rates and and for rising policy rates. Having estimated equation ( 3), an F-test can be used to see whether the different coefficients estimated in the two regimes are statistically significant. A rejection of the null hypothesis ( , , ) indicates that there is asymmetry in the speed of adjustment and/or in short-term dynamics depending on the direction of the change in the policy rate. We test both separately and jointly for the adjustment and short-run dynamics asymmetries.

Finally, the cumulative sum (CUSUM) and the cumulative sum of squares (CUSUMSQ) tests are used on the recursive regression residuals. Stability of the regression coefficients is proven if the plot of the statistics falls within the 5% significance bounds. These tests are relevant to our case due to the peculiar nature of Caucasian economies. All countries in our sample are still in transition characterized by a highly volatile macroeconomic environment.

It is therefore crucial to study parameter stability.

Data Issues

Empirical research on the interest rate pass-through can be split into two broad categories depending on the type of data used. The first category uses country-level data either in a time or country panel context. 5 The second category employs bank-specific data series to measure the interest rate pass-through to bank deposit and retail rates. 6 In this paper, we use countrylevel interest rate series. All data series used are in monthly frequency. They were collected from the websites of the central banks of the respective countries. Time spans vary considerably across countries, since some central banks still do not publish interest rate data for early 2000s in electronic formats. We have used the official policy rate of the respective central banks as the independent variable and market and bank retail rates as the dependent variable. The coverage of market and retail rates varies for different countries. For some countries, interest rates are also available for both home currency and dollar denominated deposit and lending instruments. The time span of the data also differs across countries. Table 1 summarizes the data and time coverage of our dataset.

Two observations merit further attention. The first one is the stability of the yield curve.

Subject to data availability, we measure curve stability by the ratio of long-term rates to short-term rates. We compute the ratio for every month, and then calculate the standard deviation for the respective instrument's time-span. We repeat the same procedure for the full sample and the pre-and post-break periods. If standard deviation is reasonably timeinvariant, i.e. is not affected by the structural break, we conclude that the curve is stable. The second preliminary observation relates to the difference between rates on domestic and foreign-currency (US dollar) denominated assets (Table 3). In almost all cases, assets denominated in home-country currency have systematically higher interest rates than those in US dollar. This is a natural observation because of the still present dollarization and domination of the dollar in the region's financial and trade activities, shaky trust in the local currencies. In addition, there exists a special risk premium that economic agents assign to any assets denominated in the domestic currency due to illiquidity, high and volatile inflation rates and the inability to forecast domestic interest rates with great certainty (forward-looking myopia).

Estimation Results

Structural Break, Unit Root, and Cointegration Test Results

The results of the Quandt-Andrews test for structural breaks, reported in Table 4, show that while many series, particularly in the case of Kazakhstan, break in as early as 2001, a large majority of the breaks occur after the outbreak of the financial crisis in late 2007.

Nevertheless, the break dates do not fully overlap, since the specific month of individual country structural breaks are quite spread out from 2007:12 to 2009:12. There is also quite noticeable within-country heterogeneity in structural break dates for different domestic currency denominated rates. Table 4 also reports the ADF unit root test results for the full sample and for the pre-and post-structural break periods. Most of the series follow the I(1) process7 . It is not clear whether the structural break has had any substantial effect on the stationarity of the series, as no apparent pattern can be identified across all countries.

Table 5 reports the results of our cointegration analysis. The interest rate pairs including the policy rate on the one hand and the market/deposit/lending rates on the other hand appear to be linked via a long-term cointegrating vector: the F-statistics of the bounds testing approach comfortably reject the null hypothesis of no cointegrataion and the error correction terms are negative and statistically significant at conventional significance levels of significance.

Overall, it is fair to conclude that almost all pairs of series are cointegrated. In addition, the absolute values of the error correction terms are often considerably lower than unity, implying a slow speed of adjustment to the cointegration vector8 . In other words, the passthrough (regardless of whether it is complete or not) approaches its long-run equilibrating state in a sluggish manner. This is true for all countries and instrument types in the sample.

Interest Rate Pass-Through Estimates

Let us now take a look at the interest rate pass-through (IRPT) estimates, summarized in Table 6, for the full sample as well as the pre-and post-break periods. For Armenia, the size of the long-run interest rate pass-through varies from unity to as low as 0.29. Pass-through to deposit interest rates seems to be the strongest. Interest rates on government assets tend to overshoot in response to a monetary policy innovation. Pass-through has more or less improved over time, i.e. after the break. Pass-through for interest rates denominated in the local currency is higher than for those in USD. The maturity does not affect the size of the pass-through. For Azerbaijan, while the estimates differ a lot, they point to a generally incomplete pass-through9 . It is difficult to say whether pass-through has improved or changed at all over time. Short-term rates tend to react stronger to monetary policy rate movements than longer rates. The currency denomination does not seem to alter the size of the IRPT.

Interbank and government t-bill interest rates display a marginally higher pass-through. T-bill rates overshoot monetary innovations after the structural break of April 2008.

For Georgia, the government t-bill rate overshoots the monetary policy rate for the full sample. Apart from T-bill rates, the estimated size of the long-run pass-through varies from 0.31 to 0.92. The structural break has improved pass-through, although estimates are rarely consistently significant in statistical terms. Interbank rates react almost fully to monetary impulses. Deposit rate pass-through is noticeably better than for lending rates. In Kazakhstan,

government T-bill rates, apart from some rare cases for other rates, substantially overshoot in response to changes in the key monetary policy rate. Interest rates denominated in domestic currency exhibit greater pass-through than those in the US dollar. Pass-through does not vary with maturity and it has also surprisingly weakened after the structural break. Overall, the IRPT is stronger in Kazakhstan than in Armenia, Azerbaijan or Georgia. Finally, there is very little heterogeneity in the pass-through estimates across domestic rates in Russia. Passthrough is remarkably complete, and is in fact higher than unity. All series, and in particular government T-bill rates, tend to overshoot the monetary policy rate considerably. The structural break seems to have had no systematic effect on the size of the pass-through.

To summarize, pass-through estimates exhibit higher variability and less completeness in Armenia, Azerbaijan, Georgia than in Kazakhstan or Russia. It is not clear whether the passthrough improved after the structural break, i.e. over time. The generally incomplete passthrough (except for Russia) is a clear signal of macroeconomic instability, general interest rate volatility, and a low level of competition in the bank sectors. Indeed, a systematically incomplete interest rate pass-through for all types of interest rates could signal weak banking competition. 10The most substantial recurring observation is that instruments of various maturities, across different countries, and of both domestic and foreign currency of denomination, tend to overshoot considerably policy rate innovations. Overshooting, which is particularly strong for government T-bill rates, could point to the presence of information asymmetries in Caucasian financial markets [START_REF] Stiglitz | Credit rationing in markets with imperfect information[END_REF][START_REF] Debondt | Interest Rate Pass-Through: Empirical Results for the Euro Area[END_REF] and could also show poor access to capital markets by small institutions. Smaller financial institutions in the region may be facing barriers to access quality capital. This in turn could force them to charge higher interest rates to compensate for the lack of financing options. There is also a possibility that markets believe in monetary policy inertia, i.e. that any given intervention in the policy rate market would initiate a new trend in the monetary policy stance and would increase the likelihood of future policy interventions of the same direction. In other words, a one-time increase in the policy rate raises the odds that the rate will be increased again in the future as part of the new monetary policy stance. Commercial banks thus price in the expectations of future policy changes by purposefully overshooting today's interest rates.

The considerable cross-country heterogeneity of the estimates suggests that there really is a limited set of common factors that unite Caucasian countries regarding the interest rate passthrough. While the Caucasus region does exhibit a common structural break after 2007, it is difficult to say that this is overwhelming evidence in support of regional convergence. There is indeed much more evidence that the region is not integrating, at least not in terms of the interest rate pass-through. It is also true that another homogeneous factor across the Caucasus is the presence of overshooting and its related underlying causes. It's tough to argue though that crippling information asymmetry is the kind of element that Caucasian policy makers would wish to have as a common factor.

Asymmetric Responses to Monetary Policy Changes

Table 7 reports estimates of asymmetries of market rates to positive and negative monetary policy rate innovations along three dimensions: asymmetry in the speed of adjustment, shortrun dynamics, and the joint asymmetry of the two components. First, all estimates of shortterm adjustment are considerably smaller than unity implying a sluggish pass-through (in addition to the incomplete long-term pass-through discussed above). Systematically sluggish adjustment can be thought of reflecting high switching costs and, in the case of bank deposit rates, the unwillingness of banks to lose customers due to frequent retail rate adjustments.

For Armenia, Azerbaijan, and Russia, there is practically no difference in the response of market rates to monetary expansions or contractions 11 . For Kazakhstan, market rates are slightly more sluggish in response to contractionary policy stance, and rates are more sluggish moving upwards in Georgia. The tests for asymmetry suggest that asymmetric effects are there but their size is not significant. Azerbaijan and Russia have just one statistically significant case of asymmetry. For the series that do exhibit asymmetric behavior, in either of the three parameters, asymmetry is stronger in the instruments of long-term maturity, regardless of the currency denomination or the instrument type.

These estimations results can provide us some insights regarding the consumer reaction hypothesis and/or the collusive pricing theory. The former refers to the occasion when deposit rates are sluggish going downwards reflecting the desire of banks to smooth negative deposit rate shocks. Collusive pricing theory assumes market collusion among banks, which would collectively refuse to lower lending rates despite a decrease in the policy rate. Such a behavior would imply downward lending rate rigidity. Our results provide no convincing evidence in favor of either of the two theories.

Concluding Remarks

Drawing on a standard analytical framework and empirical strategies, we analyzed the interest rate pass-through for Armenia, Azerbaijan, Georgia, Kazakhstan, and Russia. Our results can be summarized using the research questions we asked at the beginning of this paper. First, the pass-through is found to be incomplete. Incomplete pass-through may be a result of macroeconomic instability in the region, interest rate and inflation volatility, and the lack of competition in the banking sector. Second, adjustment to the long-term equilibrium seems sluggish, indicating high menu switching costs. Third, Asymmetric adjustment is not very important and barely present in some countries such as Armenia, Azerbaijan and Russia.

Our results lend no support to the consumer reaction hypothesis or collusive pricing agreements in the banking sector. Fourth, the size of the pass-through differs for different rates within specific countries. Fifth, the size of the long-run pass-through and the short-run adjustment vary quite significantly across maturity and currency denomination for the same country. Sixth, there is no clear empirical evidence that the size and speed of adjustment of the pass-through change significantly over time. Finally, while the 2008 Financial Crisis seems to have been the common cause of structural breaks in most countries' interest rate series, there is not enough evidence in favor of regional convergence in the Caucasus. Interest rate pass-through is driven much more by domestic factors than by regional integrating forces. Note: Differential refers to the arithmetic difference between interest rates of instruments denominated in domestic and foreign currencies. Yellow formatting indicates that domestic currency rates are higher (positive differential).

Table 2

 2

illustrates that yield curves are reasonably stable in Russia and Kazakhstan, while they are unstable in Armenia and Azerbaijan. Remarkably, yield curves of dollar denominated credit (lending_for) instruments are uniformally stable across countries.

Table 2 : Yield Curve Structure Stability

 2 Yellow highlight formatting indicates presence of reasonable stability in the yield-curve structure. Stability is measured by the standard deviation of the monthly ratios of long-term rates to short-term rates. Georgia is not included in the table due to data limitations

	Armenia	Full Sample	Pre-Break	Post-Break	Kazakhstan	Full Sample	Pre-Break	Post-Break
	gov	1.78	2.08	0.96 gov	0.65	0.40	0.69
	deposit_do	2.32	2.24	2.21 deposit_do	1.92	0.74	2.05
	m				m
	deposit_for	0.28	0.30	0.17 deposit_for	2.62	0.98	2.82
	lending_do	0.83	0.52	1.25 lending_do	0.15	0.15	0.14
	m				m
	lending_for	0.45	0.46	0.43 lending_for	0.29	0.28	0.26
	Azerbaijan Full	Pre-	Post-	Russia	Full	Pre-	Post-
		Sample	Break	Break		Sample	Break	Break
	deposit_do	0.46	0.29	0.61 gov	0.33	0.35	0.25
	m
	deposit_for	0.72	0.24	0.78
	lending_do	0.51	0.64	0.28
	m
	lending_for	0.14	0.12	0.10
	Note:

Table 3 : Interest Rate Differential Based on Currency of Denomination Armenia Full Sample Pre-Break Post-Break Kazakhstan Full Sample Pre-Break Post-Break deposit_st_dom

 3

		6,34	6,57	5,82 deposit_st_dom	4,88	6,25	4,62
	deposit_st_for	5,21	6,01	3,38 deposit_st_for	3,54	4,60	3,33
	Differential	1,13	0,56	2,44	Differential	1,34	1,64	1,28
	deposit_mt_dom	8,83	9,10	8,37 deposit_mt_dom	5,88	9,13	5,26
	deposit_mt_for	6,46	6,81	5,76 deposit_mt_for	4,31	6,00	3,99
	Differential	2,38	2,29	2,61	Differential	1,57	3,13	1,27
	deposit_lt_dom	9,12	8,66	10,05 deposit_lt_dom	4,99	4,74	5,04
	deposit_lt_for	7,69	7,49	8,13 deposit_lt_for	4,07	5,96	3,71
	Differential	1,42	1,17	1,92	Differential	0,92	-1,22	1,33
	lending_st_dom	19,01	21,04	14,72 lending_st_dom	14,94	18,89	14,19
	lending_st_for	15,96	17,92	11,64 lending_st_for	11,17	15,75	10,29
	Differential	3,05	3,12	3,08	Differential	3,77	3,14	3,89
	lending_mt_dom	20,30	21,26	18,28 lending_mt_dom	15,50	19,49	14,74
	lending_mt_for	18,34	20,69	13,16 lending_mt_for	12,84	16,29	12,18
	Differential	1,96	0,56	5,12	Differential	2,66	3,20	2,56
	lending_lt_dom	18,72	19,46	17,17 lending_lt_dom	13,75	14,23	12,18
	lending_lt_for	16,91	18,07	14,36 lending_lt_for	12,62	13,10	11,07
	Differential	1,81	1,39	2,81	Differential	1,13	1,13	1,11
	Azerbaijan	Full Sample Pre-Break Post-Break interbank_dom	5,81	5,35	7,13
	deposit_st_dom	7,46	7,65	7,16 interbank_for	5,99	5,56	7,68
	deposit_st_for	6,25	7,07	4,72	Differential	-0,17	-0,21	-0,55
	Differential	1,21	0,59	2,44	Russia	Full Sample Pre-Break Post-Break
	deposit_lt_dom	12,46	12,42	12,55 lending_dom	13,19	13,97	9,25
	deposit_lt_for	12,67	12,54	12,93 lending_for	9,08	9,62	7,48
	Differential	-0,21	-0,12	-0,38	Differential	4,11	4,35	1,78
	lending_st_dom	15,80	16,61	14,73	Georgia	Full Sample Pre-Break Post-Break
	lending_st_for	16,72	15,39	18,46 deposit_dom	22,68	22,58	22,89
	Differential	-0,93	1,22	-3,74 deposit_for	18,64	18,97	17,96
	lending_lt_dom	16,93	17,70	15,94	Differential	4,04	3,62	4,92
	lending_lt_for	15,48	15,48	15,49 lending_dom	9,80	9,35	10,87
	Differential	1,45	2,22	0,44 lending_for	8,30	8,24	8,45
	interbank_dom	16,38	16,91	15,39	Differential	1,50	1,11	2,42
	interbank_for	12,97	13,45	12,26
	Differential	3,40	3,46	3,13

Table 4 : Structural Break and Unit Root Test Results

 4 Note: All structural break F-statistics reject the null of no structural break at the 1% level of significance. Bold formatting indicates I(1) process, i.e. non-stationarity in levels and stationarity in first differences.

		Structural	Unit Root		Structural Break	Unit Root
		Break
	Armenia	F-Test	Break Point	Pre-Break	Post-Break	Kazakhstan	F-Test	Break Point	Pre-Break	Post-Break
	Policy Rate	31.67 Mar-08	0.00	0.20 Policy Rate	95.56 Feb-09	0.56	0.23
	gov_st	10.71	Sep-10	0.55	0.18 gov_st	118.70	Jul-07	0.50	0.08
	gov_mt	13.14	Jan-09	0.35	0.02 gov_lt	80.88 Mar-02	0.00	0.28
	gov_lt	14.65	Apr-05	0.15	0.39 deposit_st_dom	281.69	Nov-	0.04	0.03
								01
	deposit_st_dom	22.82 Dec-07	0.00	0.70 deposit_st_for	240.69	Nov-	0.68	0.01
								01
	deposit_st_for	16.97	Apr-08	0.00	0.18 deposit_mt_dom	176.68	Nov-	0.73	0.00
								01
	deposit_mt_dom	29.22 Dec-07	0.77	0.63 deposit_mt_for	245.31	Nov-	0.00	0.24
								01
	deposit_mt_for	27.25 Dec-07	0.18	0.03 deposit_lt_dom	44.10	Nov-	0.00	0.14
								01
	deposit_lt_dom	31.82	Jan-08	0.07	0.07 deposit_lt_for	149.77	Nov-	0.02	0.00
								01
	deposit_lt_for	24.50 Dec-07	0.44	0.00 lending_lt_dom	92.93	Nov-	0.21	0.00
								01
	lending_st_dom	33.97 Mar-08	0.00	0.00 lending_st_for	107.33	Nov-	0.20	0.43
								01
	lending_st_for	20.85 Mar-08	0.00	0.00 lending_mt_dom	92.46	Nov-	0.96	0.36
								01
	lending_mt_dom	37.47 Mar-08	0.26	0.24 lending_mt_for	95.39	Nov-	0.00	0.63
								01
	lending_mt_for	34.60 Mar-08	0.76	0.69 lending_lt_dom	77.28 Feb-09	0.72	0.06
	lending_lt_dom	42.62 Mar-08	0.89	0.10 lending_lt_for	84.11 Feb-09	0.30	0.03
	lending_lt_for	33.33 Mar-08	0.67	0.86 interbank_dom	36.41	Jul-09	0.00	0.79
						interbank_for	67.60	Jul-09	0.33	0.87
	Azerbaijan	F-Test	Break Point	Pre-Break	Post-Break	Russia	F-Test	Break Point	Pre-Break	Post-Break
	Policy Rate	72.60	Oct-08	0.57	0.00 Policy Rate	33.57 Dec-08	0.07	0.11
	gov	59.51	Apr-08	0.00	0.53 gov_st	164.78	Jul-09	0.15	0.09
	deposit_st_dom	118.57 Dec-08	0.96	0.41 gov_mt	157.14	Jul-09	0.88	0.30
	deposit_st_for	156.39 Mar-09	0.00	0.02 deposit	85.29 Sep-09	0.04	0.35
	deposit_lr_dom	185.05 Mar-09	0.24	0.29 lending_dom	16.83 Oct-09	0.01	0.00
	deposit_lr_for	170.77 Mar-09	0.14	0.05 lending_for	25.16 Dec-08	0.00	0.43
	lending_st_dom	62.07	Oct-08	0.35	0.39 interbank	39.16 Apr-10	0.48	0.13
	lending_st_for	89.51	Oct-08	0.78	0.05	Georgia	F-Test	Break Point	Pre-Break	Post-Break
	lending_lt_dom	84.66	Oct-08	0.42	0.37 Policy Rate	67.53	Jan-09	0.06	0.29
	lending_lt_for	110.23	Oct-08	0.82	0.07 gov	24.52	Nov-	0.60	0.24
								07
	interbank_dom	208.46 Mar-09	0.11	0.82 deposit_dom	75.57 Apr-09	0.55	0.77
	interbank_for	238.56 Dec-08	0.17	0.67 deposit_for	72.24 Apr-09	0.09	0.10
						lending_dom	23.36	Jan-09	0.03	0.08
						lending_for	30.56	Jan-09	0.64	0.24
						interbank	69.97 Apr-09	0.27	0.35

Table 5 : Cointegration Test Results

 5

		Full Sample	Pre-Break	Post-Break		Full Sample	Pre-Break	Post-Break
	Armenia	F-Statistic	ECT	F-Statistic	ECT	F-Statistic	ECT	Kazakhstan	F-Statistic	ECT	F-Statistic	ECT	F-Statistic	ECT
	gov_st	476.56	-0.06	311.01	-0.06	4.00	-0.39 gov_st	2131.40	-0.07	1231.10	-0.07	1183.90	-0.12
	gov_mt	121.1	-0.20	9.43	-0.20	4.62	-0.41 gov_lt	4178.80	-0.04	6436.40	-0.03	765.61	0.03
	gov_lt	394.96	-0.24	363.53	-0.19	5.84	-0.68 deposit_st_dom	1176.20	-0.04	87.15	-0.25	54.46	-0.20
	deposit_st_dom	5.07	-0.54	0.58	-0.85	7.39	-1.00 deposit_st_for	125.76	-0.11	15.51	-0.42	15.23	-0.28
	deposit_st_for	142.95	-0.12	38.17	-0.21	16.79	-0.67 deposit_mt_dom	33.97	-0.17	9.71	-0.54	29.76	-0.31
	deposit_mt_dom	38.36	-0.29	16.73	-0.53	14.43	-2.52 deposit_mt_for	15.46	-0.30	16.87	-0.62	9.04	-0.71
	deposit_mt_for	10.53	-1.00	10.03	-0.94	0.66	-1.00 deposit_lt_dom	17.20	-0.57	5.08	-0.86	2.40	-0.73
	deposit_lt_dom	21.95	-0.20	8.09	-0.52	8.08	-0.39 deposit_lt_for	5.47	-0.28	3.06	-0.60	0.79	-1.00
	deposit_lt_for	19.03	-0.16	19.35	-0.23	2.13	-0.60 lending_lt_dom	8.72	-0.25	49.08	-0.27	11.93	-0.13
	lending_st_dom	5.66	-0.45	10.01	-0.59	3.09	-1.45 lending_st_for	68.01	-0.24	76.20	-0.34	32.67	-0.32
	lending_st_for	50.21	-0.27	22.62	-0.39	2.07	-1.00 lending_mt_dom	51.44	-0.30	269.92	-0.10	47.82	-0.06
	lending_mt_dom	14.93	-0.19	30.89	-0.45	23.66	-1.00 lending_mt_for	392.81	-0.07	60.53	-0.40	17.83	-0.07
	lending_mt_for	3.82	-0.74	5.38	-0.58	1.17	-1.00 lending_lt_dom	54.74	-0.22	11.10	-0.36	7.94	-0.35
	lending_lt_dom	83.35	-0.18	29.17	-0.21	38.64	-0.15 lending_lt_for	10.06	-0.19	14.50	-0.38	3.38	0.15
	lending_lt_for	164.07	-0.08	23.16	-0.23	24.85	-0.07 interbank_dom	5.10	-0.61	4.94	-0.73	4.26	-0.53
								interbank_for	12.51	-0.54	18.38	-0.50	3.68	-1.00
	Azerbaijan	F-Statistic	ECT	F-Statistic	ECT	F-Statistic	ECT	Russia	F-Statistic	ECT	F-Statistic	ECT	F-Statistic	ECT
	gov	301.09	-0.29	3.00	-2.13	88.09	-0.41 gov_st	345.83	-0.15	166.57	-0.14	217.84	-0.76
	deposit_st_dom	15.68	-0.41	9.15	-0.30	24.29	-0.23 gov_mt	245.00	-0.07	208.28	-0.16	108.63	-0.31
	deposit_st_for	33.21	-0.23	27.99	-0.21	31.02	-0.39 deposit	1179.40	-0.10	660.30	-0.05	223.04	-0.25
	deposit_lr_dom	20.64	-0.64	2.81	-0.54	6.42	-1.00 lending_dom	284.05	-0.13	136.22	-0.17	1750.80	-0.38
	deposit_lr_for	9.75	-0.23	12.93	-0.15	14.96	-0.74 lending_for	97.89	-0.09	58.20	-0.46	21.15	-0.23
	lending_st_dom	53.38	-0.22	21.45	-0.21	15.95	0.27	interbank	35.70	-0.25	40.25	-0.38	483.60	-0.20
	lending_st_for	17.83	-1.27	64.92	-0.17	12.05	-0.13	Georgia	F-Statistic	ECT	F-Statistic	ECT	F-Statistic	ECT
	lending_lt_dom	42.24	-0.47	27.79	-1.00	7.15	-1.96 gov	92.69	-0.19	83.91	-0.11	61.51	-0.17
	lending_lt_for	17.38	-0.39	41.36	-0.20	7.16	-1.12 deposit_dom	18.85	-0.32	8.01	-0.68	46.59	-0.43
	interbank_dom	14.22	-0.25	8.74	-0.41	9.04	-1.63 deposit_for	13.00	-0.27	3.36	-1.00	52.85	-0.14
	interbank_for	9.63	-0.57	14.21	-0.39	41.83	0.10	lending_dom	21.68	-0.27	6.21	-0.20	13.49	-0.28
								lending_for	124.46	-0.04	36.30	-0.15	76.82	-0.09
								interbank	22.77	-0.15	39.44	-0.34	NA	NA

Note: ECT refers to the error correction term. Bold and underlined formattings indicate statistical significance at the 1% and 5% levels, respectively.

Table 6 : Long-Run Interest Rate Pass-Through Estimates Armenia Full Sample Pre Break Post Break Kazakhstan Full Sample Pre-Break Post-Break gov_st

 6

		-0.80	-2.40	1.19	gov_st	1.05	0.83	1.40
	gov_mt	1.30	1.50	0.36	gov_lt	1.31	1.78	-1.21
	gov_lt	1.36	1.32	0.46	deposit_st_dom	0.70	0.27	-1.38
	deposit_st_dom	0.52	-0.15	0.94	deposit_st_for	0.60	0.36	-1.41
	deposit_st_for	1.01	0.59	0.37	deposit_mt_dom	0.70	0.19	-0.57
	deposit_mt_dom	1.02	0.69	0.29	deposit_mt_for	0.24	0.36	-1.50
	deposit_mt_for	-0.54	-0.90	0.26	deposit_lt_dom	0.41	0.51	-2.35
	deposit_lt_dom	0.33	0.47	-0.81	deposit_lt_for	-0.25	0.34	-1.35
	deposit_lt_for	0.15	-0.13	0.20	lending_lt_dom	0.02	0.68	0.96
	lending_st_dom	-0.36	-0.50	-1.85	lending_st_for	0.70	1.04	1.89
	lending_st_for	0.37	0.28	-0.13	lending_mt_dom	0.83	0.81	2.92
	lending_mt_dom	0.28	0.53	-0.50	lending_mt_for	1.05	0.58	0.24
	lending_mt_for	-0.08	-0.10	-0.66	lending_lt_dom	0.63	0.44	0.27
	lending_lt_dom	-1.06	-0.88	-0.64	lending_lt_for	0.90	0.53	3.19
	lending_lt_for	-0.45	-0.06	0.47	interbank_dom	0.41	0.36	-3.59
					interbank_for	-0.64	0.52	-2.63
	Azerbaijan	Full Sample Pre Break Post Break	Russia	Full Sample Pre Break Post Break
	gov	0.77	-0.15	3.44	gov_st	1.02	1.23	1.25
	deposit_st_dom	0.25	-0.61	1.18	gov_mt	0.54	1.53	0.92
	deposit_st_for	0.00	0.16	0.50	deposit	1.45	2.68	1.96
	deposit_lr_dom	0.27	0.27	0.75	lending_dom	1.10	1.27	2.99
	deposit_lr_for	0.00	-0.39	0.11	lending_for	1.16	0.47	1.37
	lending_st_dom	0.47	-0.11	-1.28	interbank	0.70	1.43	0.72
	lending_st_for	0.36	-0.08	3.33	Georgia	Full Sample Pre Break Post Break
	lending_lt_dom	-0.28	-1.06	-0.89	gov	1.76	2.38	0.94
	lending_lt_for	-0.13	-1.04	-0.83	deposit_dom	0.31	0.31	0.72
	interbank_dom	0.22	-0.18	-0.64	deposit_for	0.43	0.00	0.52
	interbank_for	0.32	-0.61	-3.86	lending_dom	0.36	0.36	0.03
					lending_for	5.22	1.08	0.04
					interbank	-0.30	0.92	0.69
	Note: Bold and Underline formatting indicate statistical significance at the 1% and 5% levels, respectively.

Table 7 : Asymmetric Pass-Through Coefficients and Tests

 7 Note: Bold formatting indicates statistical significance at the 5% level, thus the rejection of the null hypothesis of no asymmetry

	Armenia	Speed of Adjustment	Short-Run Dynamic	Joint Asymmetry	Test A Test B Test C	Kazakhstan	Speed of Adjustment	Short-Run Dynamics	Joint Asymmetry	Test A Test B Test C
		Repo+ Repo-Repo+ Repo-Repo+ Repo-					Repo+ Repo-	Repo+	Repo-Repo+	Repo-
	gov_st	-0.39	-0.22	0.11	0.64	-0.39	-0.21	1.34	0.10	0.71	gov_st	-0.07	0.04	0.09	0.03	-0.07	0.02	1.79	0.49	0.6
	gov_mt	-0.26	-0.26	0.96	1.10	-0.27	-0.26	0.01	0.01	0.01	gov_lt	-0.03	-0.02	0.10	0.01	-0.03	-0.01	0.46	0.21	0.3
	gov_lt	-0.32	-0.34	0.45	1.16	-0.33	-0.34	0.04	0.36	0.20	deposit_st_dom	-0.40	-0.53	0.15	0.72	-0.39	-0.54	3.68	3.26	3.1
	deposit_st_dom	-0.66	-0.91	-1.83	-0.89	-0.63	-0.94	0.68	0.33	0.66	deposit_st_for	-0.38	-0.23	-0.13	0.28	-0.40	-0.27	1.54	0.64	0.7
	deposit_st_for	-0.81	-0.98	1.57	1.05	-0.83	-1.00	2.90	0.04	1.55	deposit_mt_dom	-0.50	-0.50	-0.94	1.34	-0.46	-0.58	0.00	2.79	2.3
	deposit_mt_dom	-0.17	-0.22	0.12	-0.40	-0.17	-0.22	2.51	1.00	1.82	deposit_mt_for	-0.64	-0.36	0.39	0.35	-0.64	-0.31	2.82	0.70	1.6
	deposit_mt_for	-0.74	-0.70	-0.06	-0.06	-0.79	-0.74	1.81	0.25	0.87	deposit_lt_dom	-0.77	-1.03	-0.32	2.76	-0.74	-0.99	3.09	4.31	2.7
	deposit_lt_dom	-0.45	-0.45	0.17	-0.45	-0.38	-0.39	0.05	8.16	6.02	deposit_lt_for	-0.99	-0.71	1.38	1.06	-0.99	-0.68	1.24	0.09	0.7
	deposit_lt_for	-0.26	-0.25	-1.14	0.89	-0.25	-0.19	0.03	7.76	6.55	lending_lt_dom	-0.26	-0.26	0.25	0.38	-0.27	-0.28	0.00	0.67	0.5
	lending_st_dom	-0.71	-0.61	2.27	-0.11	-0.71	-0.65	0.65	0.94	0.68	lending_st_for	-0.40	-0.32	-0.24	-0.38	-0.41	-0.27	0.27	0.47	0.6
	lending_st_for	-1.06	-0.97	-1.45	-0.64	-1.07	-0.98	1.26	0.05	0.67	lending_mt_dom	-0.07	-0.08	-0.02	-0.07	-0.05	-0.10	0.27	0.68	0.9
	lending_mt_dom	-0.02	-0.02	-0.45	-0.10	-0.03	-0.03	0.03	0.40	0.21	lending_mt_for	-0.28	-0.26	-0.05	-0.26	-0.26	-0.25	0.45	1.54	1.3
	lending_mt_for	0.02	0.02	0.41	1.06	0.01	0.01	0.00	0.99	0.86	lending_lt_dom	-0.29	-0.38	-0.43	-0.29	-0.30	-0.21	2.14	3.42	3.1
	lending_lt_dom	-0.31	-0.30	-0.18	0.34	-0.30	-0.29	0.30	0.82	0.55	lending_lt_for	-0.37	-0.38	-0.15	-0.73	-0.33	-0.41	0.01	0.79	0.8
	lending_lt_for	-0.08	-0.02	-0.71	0.77	-0.04	-0.01	1.60	2.79	0.46	interbank_dom	-0.60	-0.59	-0.28	-0.07	-0.58	-0.60	0.00	0.03	0.0
											interbank_for	-0.63	-0.45	0.82	0.73	-0.67	-0.51	0.59	0.40	0.3
	Azerbaijan	Repo+ Repo-Repo+ Repo-Repo+ Repo-Test A Test B Test C	Russia	Repo+ Repo-	Repo+ Repo-Repo+	Repo-Test A Test B Test C
	gov	-0.23	-0.43	0.10	0.09	-0.24	-1.08	0.45	0.06	0.23	gov_st	-0.16	-0.12	0.74	0.51	-0.17	-0.13	0.43	0.71	0.68
	deposit_st_dom	-0.45	-0.52	-0.03	-0.02	-0.65	-0.58	0.37	0.02	0.37	gov_mt	-0.08	-0.08	0.13	0.15	-0.07	-0.08	0.00	0.90	0.64
	deposit_st_for	-0.27	-0.24	-0.01	-0.01	-0.27	-0.25	1.54	0.29	0.32	deposit	-0.05	-0.05	0.00	0.24	-0.06	-0.06	0.03	2.64	1.40
	deposit_lr_dom	-0.65	-0.58	0.14	0.14	-0.44	-0.85	1.83	1.29	0.91	lending_dom	-0.10	-0.22	0.48	0.37	-0.02	-0.28	1.98	0.63	1.50
	deposit_lr_for	-0.25	-0.24	-0.05	-0.05	-0.25	-0.24	0.94	0.62	0.47	lending_for	-0.03	-0.15	0.01	0.38	-0.05	-0.16	4.83	1.13	1.77
	lending_st_dom	-0.25	-0.26	-0.25	-0.25	-0.25	-0.26	0.47	0.47	0.23	interbank	-0.48	-0.34	1.29	0.50	-0.51	-0.35	1.04	0.53	0.69
	lending_st_for	-0.66	-0.60	-0.13	-0.18	-0.70	-0.59	2.50	0.58	2.61	Georgia	Repo+	Repo-	Repo+	Repo-Repo+ Repo-Test A Test B Test C
	lending_lt_dom	-0.45	-0.49	-0.30	-0.31	-0.43	-0.41	1.07	1.56	1.05	gov	-0.05	-0.23	1.62	0.61	-0.04	-0.26	9.44	0.81	3.73
	lending_lt_for	-0.36	-0.41	-0.06	-0.05	-0.36	-0.41	2.68	0.88	1.34	deposit_dom	-0.41	-0.42	0.36	-0.04	-0.35	-0.36	0.28	3.54	1.98
	interbank_dom	-0.14	-0.20	-0.13	-0.12	-0.13	-0.19	1.56	0.53	0.54	deposit_for	-0.13	-0.10	-0.18	0.01	-0.16	-0.11	1.70	0.59	1.18
	interbank_for	-0.32	-0.22	-0.10	-0.10	-0.32	-0.34	2.15	1.00	0.81	lending_dom	-0.15	-0.13	-0.23	-0.01	-0.16	-0.14	2.02	0.48	1.19
											lending_for	-0.04	-0.05	0.04	0.23	-0.04	-0.05	0.82	1.09	1.32
											interbank	-0.34	-0.78	0.65	0.41	-0.34	-1.41	3.36	0.24	2.62

Examples are[START_REF] Horváth | Interest Rate Pass-Through: The Case of Hungary[END_REF] for Hungary;[START_REF] Opiela | The Responsiveness of Loan Rates to Monetary Policy in Poland: The Effects of Bank Structure[END_REF],[START_REF] Chmielewski | Interest rate pass-through in the Polish banking sector and bank-specific financial disturbances[END_REF] andWróbel and Pawlowska (2002) for Poland;Égert et al. (2006),Sander and Kleimeier (2004a) and[START_REF] Tieman | Interest Rate Pass-Through in Romania and Other Central European Economies[END_REF] for a number of CEE countries, and[START_REF] Petrevski | Interest rate pass-through in South East Europe: An empirical analysis[END_REF] for South-Eastern European countries.

For instance,[START_REF] Deger | A comparative study of nonlinear structure of the interest rate pass-through[END_REF] analyses interest rate pass-through for Armenia,[START_REF] Jamilov | Lending Rate Pass-Through, Sectoral Analysis, Asymmetric Stickiness, and Bank Heterogeneity in a Small Transition Economy[END_REF] studies lending rate pass-through and bank heterogeneity in Azerbaijan, and[START_REF] Samkharadze | Monetary transmission mechanism in Georgia: analyzing pass-through of different channels[END_REF] looks at the case of Georgia.

The pioneering studies of[START_REF] Hannan | The Rigidity of Prices: Evidence from the Banking Industry[END_REF] and[START_REF] Neumark | Market Structure and the Nature of Price Rigidity[END_REF] were followed by the seminal contribution from Scholnic(1996). More recent contributions include[START_REF] Egert | Interest rate pass-through in central and Eastern Europe: Reborn from ashes merely to pass away?[END_REF],Sander and[START_REF] Sander | Interest Rate Pass-Through in an Enlarged Europe: The Role of Banking Market Structure for Monetary Policy Transmission in Transition Economies[END_REF][START_REF] Tieman | Interest Rate Pass-Through in Romania and Other Central European Economies[END_REF]. Little, if any, evidence has been provided for the CIS or countries in the Caucasus.

Examples include[START_REF] Cotarelli | Financial Structure, Bank Lending Rates and the Transmission of Monetary Policy[END_REF],[START_REF] Borio | The Response of Short-Term Bank Lending Rates to Policy Rates: A Cross-Country Perspective[END_REF],[START_REF] Mojon | Financial Structure and the Interest Rate Channel of ECB Monetary Policy[END_REF],[START_REF] Bredin | Retail Interest Rate Pass-Through: The Irish Experience[END_REF],[START_REF] Sander | Convergence of interest rate pass-through in a wider Euro zone?[END_REF],Mizen and Hoffman (2002),[START_REF] Ozdemir | Retail bank interest rate pass-through: The Turkish experience[END_REF].

See e.g. De Graeve et al. (2007),[START_REF] Sorensen | Bank interest rate pass-through in the euro area: a cross country comparison[END_REF][START_REF] Sorensen | Bank interest rate pass-through in the euro area: a cross country comparison[END_REF][START_REF] Horváth | Interest Rate Pass-Through: The Case of Hungary[END_REF]

The peculiarity of the ARDL methodology allows for estimation of regressions where not all covariates are non-stationary[START_REF] Narayan | The saving and investment nexus for China: evidence from cointegration tests[END_REF].

We have also calculated cointegration half-lives which are defined as ln(0.5)/ln(1+α), where α is the speed of adjustment derived from the error correction model, and show the time period required to achieve a 50 per cent adjustment to shocks. For full-sample data of Armenia, Azerbaijan, Georgia, Kazakhstan, and Russia the average half-lives are 4.2, 2.3, 5.9, 6, and 6.6 months, respectively. Full set of results is available upon request.

This is very much in line with[START_REF] Jamilov | Lending Rate Pass-Through, Sectoral Analysis, Asymmetric Stickiness, and Bank Heterogeneity in a Small Transition Economy[END_REF] who, on an industry-specific sample of Azerbaijan's commercial banks, shows that pass-through to lending rates is incomplete and sluggish.

In order to check our results for robustness, we have performed the CUSUM and CUSUMSQ tests for parameter stability. All pass-through estimates appear to be stable according to at least one of the test outcomes. Stability test results are omitted for brevity but are available from the authors upon request.

Appendix