Does the volatility of commodity prices reect macroeconomic uncertainty?

Keywords: JEL Classication: Q02, E32, C32 macroeconomic uncertainty, commodity prices, threshold vector autoregressive model

While there exists numerous studies on the macroeconomic eects of oil and commodity shocks, the literature is quite silent on the impact of macroeconomic uncertainty on oil and commodity prices and, especially, on their volatility. This paper tackles this issue through the estimation of a structural threshold vector autoregressive (TVAR) model on a sample of 19 commodity markets. We aim at (i) assessing whether the eect of macroeconomic uncertainty shocks on commodity price returns depends on the degree of uncertainty, and (ii) investigating the transfer from macroeconomic uncertainty to price uncertainty using a newly developed measure of commodity price uncertainty. Our ndings show that both agricultural and industrial markets are highly sensitive to the variability and the level of macroeconomic uncertainty, while the impact on precious metals is more parsimonious given their well-identied safe-haven role in time of economic turmoil. In addition, we nd evidence that the recent 2007-09 recession has generated an unprecedented episode of high uncertainty in numerous commodity prices. Interestingly, our analysis further reveals that volatility and uncertainty in prices can be disconnected. This is especially true for the oil market as most important shocks in the 1990s and the beginning of the 2000s that lead to price volatility do not generate price uncertainty, highlighting the relevance of our uncertainty measure in linking uncertainty to predictability rather than to volatility.

A C C E P T E D M A N U S C R I P T 1 Introduction

The literature on oil and commodity markets during the last decade has shown the importance to identify and understand the causes and consequences of commodity price shocks. Some of the key insights (Kilian, 2008a) are that (i) most commodity prices are endogenous with respect to the global business cycle, and (ii) demand shocks cause delayed and sustained price movements while supply shocks have small and transitory eects. Whereas disentangling the supply and demand components in a structural model is now common in the empirical literature to understand commodity price dynamics (especially for crude oil price), some important concerns are missing regarding the eect of second-order moments (especially the one related to uncertainty).

So far, the previous literature is indeed silent on the eects of macroeconomic uncertainty on the volatility of commodity prices. This issue is nevertheless worthy of investigation since the impact of uncertainty on the global economy has been widely documented. For instance, theories of investment under uncertainty explain why under irreversibility condition or xed costs, uncertainty over future returns reduces current investment, hiring, and consumption through an option value to wait.

1 The starting point is that at a micro level, uncertainty may dimin- ish the willingness of rms to commit resources to irreversible investment and the readiness of consumers to spend or allocate their earning and wages. It follows that micro-level uncertainty may be transmitted to the macro level because uncertainty about the return to investment at a micro level may create cyclical uctuations in aggregate investment at a macro level (see [START_REF] Bernanke | Irreversibility, uncertainty, and cyclical investment[END_REF]). [START_REF] Alquist | Forecasting the Price of Oil[END_REF] The crucial point here is that there is no reason why such mechanism at both micro and macro levels should not be extended to commodity markets. Indeed, at a micro level, commodity rms in several sectors (especially in oil industry) are confronted to high costs and should be impacted by uncertainty about returns to investment. Similarly, at a macro level, as prices are related to global supply and demand conditions, uncertainty about macroeconomic aggregates should also aect commodity prices.

In the wake of the literature about the origins of commodity price movements, the aim of this paper is to investigate the impact of uncertainty on commodity price returns and volatility. Regarding the previous literature, the bulk of the studies have looked at the reverse relationship and highlighted the signicance of uncertainty in oil prices in explaining economic uctuations and in exacerbating asymmetry in the oil price-economic activity nexus.

3 All of them suggest that oil price uncertainty impacts output, investment, and consumption in the U.S. and G-7

countries in an asymmetric way. While providing interesting results, these studies suer from 1 See [START_REF] Bernanke | Irreversibility, uncertainty, and cyclical investment[END_REF], [START_REF] Gibson | Stochastic Convenience Yield and the Pricing of Oil Contingent Claims[END_REF], [START_REF] Bloom | Uncertainty and investment dynamics[END_REF], and [START_REF] Bloom | The impact of uncertainty shocks[END_REF] among others.

2 This could be due to two reasons: (i) the macroeconomy is insuciently diversied and cannot be pro- tected from micro-level uctuations; and (ii) under imperfect information, agents may confound transitory and permanent shocks, thereby converting a transitory shock into a persistent shock.

3 See Kilian (2014) for a review. The reader may also refer to the theoretical papers of [START_REF] Bernanke | Irreversibility, uncertainty, and cyclical investment[END_REF] and [START_REF] Pindyck | Irreversibility, uncertainty and investment[END_REF], and to the empirical studies of [START_REF] Elder | Oil price uncertainty[END_REF] and [START_REF] Jo | The Eect of Oil Price Uncertainty on Global Real Economic Activity[END_REF] to name few. Note that, as underlined by [START_REF] Kilian | Oil Price Shocks: Causes and Consequences[END_REF], Elder and Serletis do not directly test for symmetry in the eect of oil prices, but rather test the null of no feedback eect from the conditional variance of oil prices to the conditional mean.

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an important drawback since it is well known that the eect of uncertainty in the oil priceand commodity prices in generalon the macroeconomy should be small, by construction of the underlying economic models. As argued by [START_REF] Kilian | Oil Price Shocks: Causes and Consequences[END_REF], energy is indeed not necessarily a key component of the cash ow of investment projects, making the eect of oil price uncertainty on output not plausible. It follows that looking at the reverse transmission from macroeconomic uncertainty to the volatility of commodity prices is more relevant with regard to the modern view of the endogenous component of prices. As stressed above, this question has not been widely addressed in the literature. Some limited exceptions can, however, be mentioned.

First, regarding theoretical papers, (i) [START_REF] Pindyck | Uncertainty and exhaustible resource markets[END_REF] discusses the implications of uncertainty associated with oil demand and reserves on the oil price behavior; (ii) Litzenberger and Rabinowitz (1995) analyze backwardation behavior in oil futures contracts; and (iii) Alquist and Kilian (2010) allow for endogenous convenience yield and endogenous inventories, and stress that it is uncertainty about the shortfall of supply relative to demand that matters. Second, among empirical papers, one may refer to (i) [START_REF] Kilian | Not all oil price shocks are alike: Disentangling Demand and Supply Shocks in the Crude Oil Market[END_REF] and [START_REF] Kilian | The role of inventories and speculative trading in the global market for crude oil[END_REF] who design as a precautionary demand shock a shock that reects shifts in uncertainty and treat macroeconomic uncertainty as unobserved; and (ii) Van Robays (2013) who investigates whether observed macroeconomic uncertainty changes the responsiveness of the oil price to shocks in oil demand and supply.

Our paper extends this literature on the relationship between macroeconomic uncertainty and commodity prices in several ways. Specically, we address this question by examining the inuence of macroeconomic uncertainty on a large panel of commodity markets. More precisely, we consider four groups of 19 raw materials (energy, precious metals, agriculture, and industry) to investigate whether the impact of macroeconomic uncertainty on commodity prices diers depending on the type of market. In addition, with uncertainty evolving through time, we account for this feature by assessing its impact at various horizonsnamely short-, medium-, and long-run horizonsto test whether its eect varies according to its maturity. From a theoretical viewpoint, uctuations in uncertainty may indeed have dierent impacts through time on the economy [START_REF] Bloom | Fluctuations in Uncertainty[END_REF], with usually two negative channels in the short run (the real option and risk aversion/risk premium eects 4 ) and two positive channels in the long run (the growth option and Oi-Hartman-Abel eects 5 ). [START_REF] Balke | Credit and economic activity: Credit regimes and nonlinear propagation of shocks[END_REF] Turning to methodological issues, our contribution is threefold. First, we retain a nonlinear structural threshold vector autoregressive (TVAR) specication that allows us to identify dierent uncertainty states, and we investigate whether the impact of uncertainty varies according to its level and variability. Second, because [START_REF] Bachmeier | Should Oil Prices Receive So Much Attention? An Evaluation of the Predictive Power of Oil Prices for the U.S. Economy[END_REF] The real option hypothesis refers to an option value to wait and suggests that uncertainty encourages rms to become cautious about adjustment costs. The risk aversion/risk premium eect refers to the cost of nance, which may increase in times of greater uncertainty.

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ACCEPTED MANUSCRIPT macroeconomic uncertainty is unobservable, assessing its eect on commodity markets obviously requires us to nd an adequate proxy. To this end, we rely on [START_REF] Jurado | Measuring Uncertainty[END_REF] and consider a robust approach to measuring macroeconomic uncertainty. The retained proxy uses a wide range of monthly macroeconomic and nancial indicators and is based on the underlying idea of a link between uncertainty and predictability. In this sense, we go further than the previous literatureparticularly compared with Van Robays (2013)'s paper, which is the closest to ourswhich generally relies on dispersion measures such as conditional volatility (e.g. conditional variance of world industrial production growth or of U.S. GDP growth estimated from a GARCH(1,1) process) or the VXO index of [START_REF] Bloom | The impact of uncertainty shocks[END_REF]. As we will see, an important drawback in using GARCH-type models to proxy uncertainty is that they are inherently backward-looking, whereas investors' expectations tend to be forward-looking. More generally, and thanks to a detailed robustness analysis, we show that the choice of the uncertainty measure is crucial to avoid erroneously attributing to uncertainty uctuations that are actually predictable. Third, for the sake of completeness, we also investigate how macroeconomic uncertainty can generate uncertainty in commodity prices, looking at second-order moments transfer. To this end, we construct a robust proxy of commodity market uncertainty based on macroeconomic uncertainty, and provide a historical decomposition that allows us to determine the contribution of macroeconomic uncertainty to commodity price uncertainty. For the specic case of oil, we further disentangle supply and demand components of oil price uncertainty to assess their respective contribution during periods of uncertainty in the market.

The central message of the paper is that most of commodity price returns are aected by macroeconomic uncertainty, the eect being more important for commodities that are strongly related to the global business cycle (such as oil, agricultural, and industrial markets). Then, in addition to traditional supply and demand shocks, macroeconomic uncertainty constitutes another key channel through which economic fundamentals may impact commodity prices. Maturity seems to matter depending on the commodity type; long-run uncertainty having more eect on agricultural and industrial cumulative price dynamics, while other markets being more sensitive to short-run impacts. Furthermore, our new commodity price uncertainty indicator shows for most considered series a transfer from macroeconomic uncertainty to price uncertainty during the 2007-09 great recession period. It further reveals that volatility and uncertainty in prices are disconnected. It is especially the case for oil as we nd that most important shocks that occurred in the 1990s and the beginning of the 2000s that lead to price volatility do not generate systematic price uncertainty, meaning that most of these movements were predictable at this period and emphasizing the relevance of our uncertainty measure in linking uncertainty to predictability rather than to volatility.

The rest of the paper is organized as follows. Section 2 presents the uncertainty measure and discusses the corresponding theoretical framework. Section 3 addresses methodological issues by presenting the TVAR specication as well as our data. Section 4 displays the results regarding the impact of macroeconomic uncertainty on commodity markets. Section 5 is devoted to the link between macroeconomic uncertainty and the uncertainty of each commodity market, and

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disentangles the respective contribution of supply and demand shocks to oil price uncertainty.

Section 6 provides some robustness checks regarding the choice of both the macroeconomic uncertainty measure and our measure of price uncertainty. Finally, Section 7 concludes the paper.

2 Uncertainty measure

U y jt (h) ≡ E (y jt+h -E [y jt+h |J t ]) 2 |J t , (1)
where j = 1, ..., N y , E (. |J t ) is the conditional expectation of the considered variable and J t denotes the information set available at time t. Uncertainty related to the variable y jt+h is therefore dened as the expectation of the squared error forecast. Aggregating over j individ-

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ual uncertainty measures U y jt (h) equally weighted by w j leads to the following expression of aggregate or macroeconomic uncertainty:

U y t (h) ≡ plim Ny→∞ Ny j=1 w j U y jt (h) ≡ E w U y jt (h) . (2)
As discussed by [START_REF] Jurado | Measuring Uncertainty[END_REF], the estimation of Equations ( 1) and ( 2) requires three fundamental steps. The rst step is to replace the conditional expectation E [y jt+h |J t ] in Equation ( 1) by a forecast in order to compute forecast errors. It is a crucial step since the forecastable component should be then removed from the conditional volatility computation.

To do so, an as rich as possible predictive model based on factors from a large set of N predictors {X it }, i = 1, ..., N , is considered, taking the following approximated form:

X it = Λ F i F t + e X it , (3)
where F t is a r f × 1 vector of latent common factors, Λ F i is the vector of latent factor loadings, and e X it is a vector of idiosyncratic errors which allows for some cross-sectional correlations. The underlying idea is that anyone who expects the price to increase in the future will be prompted to store oil now for future use leading to a shock from the demand of oil inventories. 12

The aggregate specication of our proxy has the particularity to be global, accounting for a lot of information regarding uncertainty in the supply and demand channels. While it is quite dicult in this framework to identify the proportion of unanticipated demand or supply, some reasonable assumptions about the eect of demand and supply shocks on prices may give us some insight about the mechanisms behind the relationship between macroeconomic uncertainty and commodity prices. In our analysis, we follow the dominant view about the endogenous nature of commodity prices with respect to macroeconomic conditions, considering the aggregate demand channel as a primary source of price uctuations (see [START_REF] Mabro | The Oil Price Crisis of 1998[END_REF]; Barsky andKilian, 2002, 2004;Kilian, 2008a;[START_REF] Hamilton | Causes and Consequences of the Oil Shock of 2007-08[END_REF]. In line with the previous literature, we therefore assume that exogenous events coming from the supply channelsuch as cartel decisions, oil embargoes or the eects of political uncertainty from the Middle Eastare secondary, being mainly an indirect consequence of the macroeconomic environment. By construction, our approach accounts for both channels, the demand channel being a direct eect of macroeconomic aggregate and the supply channel an indirect eect of macroeconomic conditions on exogenous events. In other words, our macroeconomic uncertainty proxy primarily reects uncertainty about the demand side. [START_REF] Bloom | The impact of uncertainty shocks[END_REF] 11 One exception for the case of oil is the 1990s, where the ow supply shocks have played an important role (see [START_REF] Kilian | The role of inventories and speculative trading in the global market for crude oil[END_REF].

12 This shock is also often called speculative demand shock since it involves a forward-looking strategy (see [START_REF] Kilian | The role of inventories and speculative trading in the global market for crude oil[END_REF].

13 Recall that assuming small eects for supply disruptions does not mean that exogenous political events do not matter. Indeed, they also aect prices by shifting expectations about future shortfalls of supply relative to

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Backward-looking vs. forward-looking uncertainty

As previously discussed, it is quite dicult to approximate uncertainty because it is not observable. One important issue regarding usual proxies based on conditional volatility is that they are often backward-looking, whereas investors' expectations tend to be forward-looking [START_REF] Kilian | Nonlinearities in the Oil Price-Output Relationship[END_REF]. As an illustrative purpose, Figure 1 provides a comparison between three uncertainty proxies. Specically, the green line is the conditional volatility estimated from a one-month GARCH ( with longer uncertainty horizons.

16

In the context of commodity prices and especially oil prices, the discussion is of primary importance. The ongoing debate in the literature about the eect of maturity has been mainly oriented toward the consequences of oil price uncertainty on economic activity. Kilian and Vigfusson (2011) provide an interesting comparison between the long-run theoretical uncertainty measure of [START_REF] Bernanke | Irreversibility, uncertainty, and cyclical investment[END_REF] and [START_REF] Pindyck | Irreversibility, uncertainty and investment[END_REF], and the empirical short-run uncertainty proxy of [START_REF] Elder | Oil price uncertainty[END_REF]. According to the authors, the former denition is related to uncertainty at horizons relevant to investment decisions, while the latter is more appropriate to capture the reallocation eect of [START_REF] Hamilton | A Neoclassical Model of Unemployment and the Business Cycle[END_REF] since it measures the current real oil price volatility. Clearly, the eect of oil price uncertainty on activity depends on the impact of the oil price on investment, but also on how companies face to uncertainty regarding their investment decision plans. Indeed, depending on the sectors, the delay from the investment decision to the beginning of the production can dier, and short-run uncertainty can thus have no impact for long-run investment strategies. All in all, accounting for the eect of maturity when investigating the impact of uncertainty is thus of crucial importance.

3 Data and model specication

Data

We consider a large dataset of 19 principal commodity markets, classied into four categories: energy, precious metals, agriculture, and industry. Related price series are monthly, starting 16 More precisely, the authors show that when h = 1, common macroeconomic uncertainty shocks account for between 3.59% and 15.13% of the forecast error variance in industrial production, compared to 0.52% and 10.56% for h = 12. For a review on the eects of maturity on uncertainty, see [START_REF] Bloom | Fluctuations in Uncertainty[END_REF]. Overall, our considered macroeconomic uncertainty proxy identies fewer uncertainty episodes than usual volatility-based measures, but when such periods occur, they display higher and more persistent correlation with real activity. These developments highlight the relevance of considering predictability rather than volatility when assessing the uncertainty -macroeconomy relationship.

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Structural threshold VAR model

To analyze whether uncertainty aects commodity prices' behavior depending on the level of uncertainty, we consider that uncertainty may be a nonlinear propagator of shocks across markets, captured by a structural threshold vector autoregressive model. 21 A threshold VAR model is a nonlinear multivariate system of equations that models nonlinearity additively and can be estimated by OLS. In addition to providing an intuitive way to capture the nonlinear eects of uncertainty on markets, the TVAR model has the advantage of endogenously identifying different uncertainty states. Indeed, according to this specication, observations can be divided, for example, into two states delimited by a threshold reached by uncertainty, with estimated coecients that vary depending on the considered state (low-and high-uncertainty states). In other words, the TVAR specication allows uncertainty states to switch as a result of shocks to commodity markets.

For each of our four groups of commodity markets, we consider a structural TVAR model of

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Y t = µ 1 + A 1 Y t + B 1 (L) Y t-1 + µ 2 + A 2 Y t + B 2 (L) Y t-1 I t (c t-d ≥ γ) + u t , (4)
where Y t is a vector of endogenous variables containing both the price returns of all commodities belonging to the considered group (energy, precious metals, industry, and agriculture) and the measure of macroeconomic uncertainty, 22 A 1 and A 2 reect the structural contemporaneous relationships in the two states, respectively, B 1 (L) and B 2 (L) are lag polynomial matrices, µ 1 and µ 2 are vectors of constants, and u t denotes the vector of orthogonalized error terms. c t-d is the d-lagged threshold variable, which is a function of uncertainty (this hypothesis is discussed and tested below, as well as the choice of the optimal value of d). I t (c t-d ≥ γ) is an indicator function that equals 1 when c t-d ≥ γ and 0 otherwise, where γ denotes the threshold uncertainty critical value that has to be endogenously estimated. In other words, two states are identied: the low-uncertainty state corresponding to a weak degree of uncertainty (I t (.) = 0) and the high-uncertainty state related to a high degree of uncertainty (I t (.) = 1). As shown by Equation ( 4), the coecients of the TVAR model are allowed to change across states depending on the level of uncertainty.

An essential step in TVAR approach is to test for the existence of a threshold eect with respect to uncertainty. However, the testing procedure is not straightforward because under the null hypothesis of no threshold eect, the threshold value is not known a priori and has to be estimated. [START_REF] Elder | Oil price uncertainty[END_REF] Here, the threshold is determined endogenously by a grid search over all possible values of the threshold variable where the grid is trimmed at a lower and upper bound to ensure a sucient number of data points for the estimation procedure in each regime. 23 If the threshold value was known, it would be possible to simply test µ2 = A 2 = B 2 (L) = 0.

24 The level of trimming is 15% as it is common in the literature.

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forecasting horizon). Unlike the standard framework, in our case the reaction of the endogenous variable to a shock depends on the past history, the state of the economy and the size and the sign of all the shocks hitting the economy within the period of interest. In order to average out the inuences of shocks on the system, we compute generalized impulse-response functions (GIRFs, see [START_REF] Koop | Impulse response analysis in nonlinear multivariate models[END_REF]. By relying on data simulation depending on the regime of the system at the time of the shock hits the variables, this approach has the advantage to allow for regime-dependent responses, as well as to look at the eects of shocks of dierent sizes and directions.

A GIRF can be dened by the following expression:

GIRF y (h, Ω t-1 , u t ) = E [y t+h | Ω t-1 , u t ] -E [y t+h | Ω t-1 ] (5)
where Ω t-1 is the information set at time t -1 and u t is a particular realization of exogenous shocks. The response of the variable y at horizon h is calculated by simulating the evolution of the model conditionally on the initial condition Ω t-1 and a given realization of u t . The general idea is to simulate the model for any possible starting point in the time horizon. We do this by drawing vectors of shocks u t+j with j = 1, ..., k, and simulating the model conditionally on the initial condition and given realization. We repeat the procedure for -u t+j in order to eliminate any asymmetry that may arise from sampling variation in the draws of u t+j . The approach is repeated 500 times. In order to generate IRFs conditionally to the location of the system with respect to the threshold variable, the simulations are repeated for several histories depending on the considered regime. Finally, the response to shocks specic to a particular regime is the estimated conditional expectation based upon the average of the simulation results. [START_REF] Fernández-Villaverde | Rubio-Ramírez, Pablo Guerrón-Quintana, and Martin Uribe[END_REF]

Endogeneity and strict exogeneity between macroeconomic and commodity prices

As mentioned above, another complication when estimating TVAR models concerns the endogeneity issue. Endogeneity may come from the fact that the threshold variable (our uncertainty measure) is allowed to endogenously respond to commodity price shocks. One way to address this problem is to assume strict exogeneity between variables. However, as discussed by Van Robays (2013) among others, assuming strict exogeneity between oil prices (and commodity prices in general) and macroeconomic uncertainty is not realistic because it is well known that a strong relationship exists between the oil market and economic activity. Therefore, a common approach is to impose the threshold variable to switch across states with a delay. This implies that we only evaluate commodity price shocks that occur within a certain state. Another common assumption in the TVAR literature is to dene the threshold variable as a moving average process that requires some persistence in the variation of the threshold variable before shocks cause the regime switching. To fully address the issue of endogeneity, we combine 25 The algorithm used to derive the GIRFs is described in Appendix A.

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these two approaches in the present paper by considering a three-period moving average of our one-period-lagged threshold variable. [START_REF] Gibson | Stochastic Convenience Yield and the Pricing of Oil Contingent Claims[END_REF] 4 Uncertainty transmission across states

We now use the TVAR approach to apprehend the impact of macroeconomic uncertainty on commodity prices. We rst present threshold test results and related comments. We then complement the analysis by conducting nonlinear IRF analysis under high-and low-uncertainty states to investigate whether the transmission of macroeconomic uncertainty shocks into commodity markets diers across states. Finally, we assess the inuence of the considered maturity.

Threshold test results

Tables 2 to 5 in Appendix A report the three threshold test results for each group of commodity markets at dierent maturities together with the percentage of high uncertainty corresponding to the percentage of observations such that the threshold variable is above the estimated threshold critical value. [START_REF] Gilchrist | Uncertainty, Financial Frictions, and Investment Dynamics[END_REF] Our main results can be summarized as follows.

First, the null hypothesis is always rejected in favor of the presence of threshold eects for each group of markets and the maturity considered. The TVAR specication is thus appropriate because it accounts for varying eects of uncertainty depending on the degree of uncertainty.

Second, the estimated threshold values increase with the maturity, meaning that as h increases, the switching mechanism between low-and high-uncertainty states is delayed. This nding reects a dierent perception of the level of uncertainty as the horizon increases [START_REF] Bloom | Fluctuations in Uncertainty[END_REF] and is also consistent with

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eect is limited by the so-called rebound eect: an improvement in energy eciency leads to a decline in the cost of energy use, canceling the substitution eect. Turning to agricultural markets, this sector is highly sensitive to the unexpected changes in economy [START_REF] Baumeister | Do Oil Price Increases Cause Higher Food Prices?[END_REF], explaining why the high-uncertainty percentage value is higher at the short-run, one-month maturity. In the case of agricultural and energy markets, the role of storage could be emphasized. Indeed, storage capacities are somewhat more exible on energy markets than on agricultural markets in the sense that a large part of agricultural products needs to be extracted at xed dates. This may explain why highest percentages of uncertainty are observed for agricultural markets compared to energy ones. Finally, the observation that the percentage of high uncertainty increases with the maturity in the case of industrial markets results from the sensitivity of this sector to structural factors that are more at play in the long run. Indeed, to design proper R&D investment strategies, rms usually have to focus on longer horizons.

Fourth, the evolution of the percentage of high uncertainty according to the value of h also provides useful information regarding the sensitivity of commodity markets to the level and variability of uncertainty. Indeed, as shown by [START_REF] Jurado | Measuring Uncertainty[END_REF], the level of uncertainty increases with h, whereas its variability is a decreasing function of h because the forecast value tends to the unconditional mean as the forecast horizon tends to innity. Consequently, the percentage of high uncertainty in agricultural markets seems to be more sensitive to the variability of uncertainty (short-run uncertainty), but for industrial markets, this percentage increases with h and seems to be more sensitive to the level (long-run uncertainty). At a more detailed level, considering the cases of energy and precious metals, the responses of oil, gold, platinum, and silver markets are unequivocal because price returns strongly react to macroeconomic uncertainty shocks in high-uncertainty states (between 4% to 7% for oil, gold and platinum and approximately 10% for silver). Moreover, though the responses to positive and negative shocks are quite persistent (eight months) and symmetric for oil and platinum, the responses of gold and, to a lesser extent, silver are more transient (approximately four months) and asymmetric. The safe-haven role of gold and silver markets during periods of economic downturn could be one plausible explanation about the rapid overshooting reaction of both prices. Recall that the proxy is countercyclical meaning that increases in uncertainty correspond to recessionary periods. Therefore, for gold and silver prices two eects seem to be at play: (i) macroeconomic uncertainty decreases the general price level since the economy is in recession, and (ii) after a certain delay, the safe-haven role of precious metals leads to rapid strong overshooting behaviors. The main drivers of oil and platinum markets are sensitive to macroeconomic uncertainty shocks because they are strongly related to economic activity through industry purposes. A dierent pattern is observed for the gas market, for which the response to macroeconomic uncertainty shocks is more important in low-uncertainty states.

The regional organization of this market may explain its relatively weak sensitivity to high macroeconomic uncertainty. Turning to the industrial and agricultural sectors, all industrial markets but the zinc follow quite similar patterns signicantly reacting to uncertainty, as do agricultural markets with the exception of coeewhich is a highly volatile marketand, to a lesser extent, sugar and wheat.

Does maturity matter?

Examining the inuence of maturity when investigating the impact of uncertainty on prices is of particular importance given the multiple channels and dierent eects at play at short-, medium-, and long-run horizons (see Bloom, 2014, for a review). Understanding the maturity perception in assessing uncertainty is also of primary importance for commodity prices since maturity may capture dierent behaviors. For instance, short-and medium-run uncertainty may capture a kind of reallocation eect [START_REF] Hamilton | A Neoclassical Model of Unemployment and the Business Cycle[END_REF]) since such maturities focus on current behaviors, while a long-run perception of uncertainty may be more related to Bernanke (1983)'s framework because it corresponds to horizons relevant to purchase and investment decisions.

28

Our results in Section 4.1 give us a rst insight about the static sensitivity of prices to maturity.

28 Recall that, by construction, the level and the variability of our proxy of uncertainty respectively increases and decreases with h. This property allows us to apprehend the sensitivity of commodity prices to both the level and the variability of uncertainty.

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Turning now to the dynamic analysis, Figure 1D in Appendix D reports the average (per group of markets) cumulative responses of prices to positive high uncertainty two-standard deviations shocks at maturity h. As before, three values of h are considerednamely, 1, 3 and 12 months. The results are consistent with those of Section 4.1 revealing that maturity matters for some markets. Though the percentage of high uncertainty is quite stable across maturity for energy and precious metals markets, the responses of both groups to a positive shock are quite similar giving more importance to the short-run eect. While the impact of short-run uncertainty is clear for energy markets (in line with the close relationship that exists between energy and economic activity in the short run), the response of precious metals markets seems to be more sensitive to long-run uncertainty after a certain delay. [START_REF] Hamilton | A Neoclassical Model of Unemployment and the Business Cycle[END_REF] The specic behavior of precious metals markets may come from the well-known safe-haven property transferring uncertainty at 1 month to longer horizons (12 months). For industrial markets the conclusion is more straightforward, uncertainty at longer maturity (h = 12) has a greater immediate impact on price returns, conrming the high sensitivity of such markets to structural factors and the reactiveness of industrial rms to horizons relevant to long-run investments. Regarding the agricultural sector, markets also appear to be more sensitive to long-run uncertainty, which is quite logical since agricultural rms may face longer horizons with respect to agricultural investment decisions. On the whole, the important point regarding the impact on commodity prices is not only the percentage of high uncertainty but also how shocks are incorporated into the system and, in turn, their implications for economic behaviors.

Does macroeconomic uncertainty generate commodity uncertainty?

To provide a complete description of the links between macroeconomic uncertainty and commodity markets, we go a step further and investigate how macroeconomic uncertainty can aect each commodity market uncertainty. To this end, we must dene an uncertainty measure for each market and then assess the transmission mechanism of macroeconomic uncertainty to each specic commodity market uncertainty.

Measuring commodity market uncertainty

Let us rst consider the determination of the commodity market uncertainty proxy. We rely on Equation (1) and proceed in two steps. In a rst step, we use the previously estimated TVAR model to generate the h-period-ahead forecast of the considered price return series, accounting 29 Given the importance of the oil market in the literature, Figure 2D in Appendix D reports the specic response of the oil price to positive (left side) and negative (right side) macroeconomic shocks across maturity.

As for the whole energy markets group, the oil price is more sensitive to short-run uncertainty after a positive shock. Looking at negative shocks (i.e., decrease in macroeconomic uncertainty), the response is quite symmetric to the positive one, highlighting the importance of short-run uncertainty.

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for the information about macroeconomic uncertainty. Let E [y t+h /J t , u u t ] be the obtained forecast, where y is the considered commodity price return series, J t the information set available at time t, and u u t the macroeconomic uncertainty shock at time t. As seen, our forecast value accounts for information about macroeconomic uncertainty. Given this forecast, we dene in a second step the h-period-ahead forecast error as the dierence between y t+h and E [y t+h /J t , u u t ],

the forecast that accounts for information about macroeconomic uncertainty. The underlying idea is that a way to understand the transmission mechanism of macroeconomic uncertainty to commodity markets is to assess how the forecast of our considered variable changes if we add information about macroeconomic uncertainty. The commodity market uncertainty measure is then given by the volatility of this forecast error.

To account for the volatility-clustering phenomenon, which is a typical feature of commodity markets, we rely on time-varying volatility specications and consider the moving average stochastic volatility model developed by [START_REF] Chan | Ecient Simulation and Integrated Likelihood Estimation in State Space Models[END_REF] and [START_REF] Chan | Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence[END_REF] given by: 30

x t = λ + v t , (6)
where x t denotes the forecast error, i.e., the dierence between the forecast of y that does not account for information about macroeconomic uncertainty and the forecast that accounts for such information. The error term v t is assumed to be serially dependent, following a MA(q) process of the form:

v t = ε t + ψ 1 ε t-1 + ... + ψ q ε t-q , (7)
h t = λ h + φ h (h t-1 -λ h ) + ζ t , (8)
where ε t ∼ N 0, e ht and ζ t ∼ N 0, σ 2 h are independent of each other, ε 0 = ε -1 = ... = ε -q+1 = 0, and the roots of the polynomial associated with the MA coecients ψ = (ψ 1 , ..., ψ q ) are assumed to be outside the unit circle. h t is the log-volatility evolving as a stationary AR [START_REF] Alquist | What do We Learn form the Price of Crude Oil Futures[END_REF] process. Following Chan and Hsiao (2013), under the moving average extension, the conditional variance of the series x t is given by:

V (x t | λ, ψ, h) = e ht + ψ 2 1 e ht-1 + ... + ψ 2 q e ht-q . ( 9
)
This specication allows us to capture two nonlinear channels of macroeconomic uncertainty:

(i) the one coming from the moving average of the q + 1 most recent variances e ht + ... + e ht-q , and (ii) the other from the AR(1) log-volatility stationary process given by Equation [START_REF] Barsky | Oil and the Macroeconomy since the 1970s[END_REF].

Given the challenge of estimating this kind of nonlinear model due to high-dimensional and nonstandard datawith the conditional density of the states being non-Gaussiana Bayesian 30 For robustness checks, the GARCH, standard stochastic, and heavy-tailed stochastic volatility models have also been considered to estimate the volatility of forecast errors. The corresponding results, available upon request to the authors, are quite similar between models. 32 Otherwise, uncertainty is attributable to the own characteristics of the considered raw materials market.

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Consider rst the case of energy markets. As shown in Figure 1E (Appendix E), the sensitivity of oil price uncertainty to macroeconomic uncertainty diers depending on the retained period, highlighting that oil shocks do not all follow the same pattern. For example, the period 34 See Barksy and [START_REF] Barsky | Do We Really Know that Oil Caused the Great Stagation? A Monetary Alternative[END_REF][START_REF] Barsky | Oil and the Macroeconomy since the 1970s[END_REF], Kilian (2008aKilian ( ,b, 2009)), [START_REF] Kilian | The role of inventories and speculative trading in the global market for crude oil[END_REF] to name a few.

35 According to the U.S. Energy Information Administration, the total Saudi Arabia crude oil production signicantly decreases from 9,550.136 thousand barrels per day in 2005 to 8,721.5068 thousand barrels per day

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is reected in oil market uncertainty by an unprecedented oil price increase. The 1984-86 period is also characterized by heightened oil price uncertainty, but it does not coincide with macroeconomic uncertainty. This episode seems to be related to the conjunction of two events: (2016) suggest that more than half of the observed decline was predictable and must reect the cumulative eect of earlier oil demand and supply shocks. Looking at our oil price uncertainty indicator reveals that, whatever the origin of the oil price slump, most of the decline was predictable until the end of our sample (April 2015) with no particular episode of uncertainty.

Turning to gas, the conclusion is somewhat dierent given the well-known regional organization of the market which may exempt the price from several international shocks. Results indeed show that macroeconomic uncertainty does not generate uncertainty in the gas market. In particular, we do not observe heightened movements during the 2007-09 recession conrming that the gas price should be more inuenced by microeconomic events related to industrial considerations. Some exceptions can be highlighted in 1996, 2001, and 2005, where the gas market experienced short-lived periods of price increase and strong volatility. These events are however more related to extreme climatic events (unusually cold weather in 1996 and the Katrina/Rita hurricanes in 2005) and do not lead to episodes of extreme uncertainty [START_REF] Kilian | Explaining Fluctuations in Gasoline Prices: A Joint Model of the Global Crude Oil Market and the U.S. Retail Gasoline Market[END_REF].

As discussed by [START_REF] Jurado | Measuring Uncertainty[END_REF] and conrmed by our previous analysis, macroeconomic uncertainty is strongly countercyclical, 37 roughly doubling in importance during recessions.

Given the role of precious metals (especially gold and silver) as safe havens and hedging in- 36 At the beginning of the 1980s, the strategy of Saudi Arabia to shut down production (compensating higher oil production elsewhere in the world) was initiated to prevent an oil price decline, without success. Saudi Arabia nally decided to ramp production back up in 1986, causing an oil shock from $27/barrel in 1985 to $12/barrel in 1986 (see [START_REF] Kilian | The role of inventories and speculative trading in the global market for crude oil[END_REF]. [START_REF] Jurado | Measuring Uncertainty[END_REF] One reason is that exogenous shocks that often cause recessionssuch as wars or nancial panicsalso directly increase uncertainty (see [START_REF] Bloom | Fluctuations in Uncertainty[END_REF].

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largely anticipated because the related uncertainty remained relatively low. In other words, macroeconomic uncertainty did not spread to precious metals markets. This nding conrms the safe-heaven role of gold and silver given that macroeconomic uncertainty surrounding the global crisis pushed up their prices, but did not induce uncertainty in their respective markets.

However, at the beginning of the 1980s, the high-uncertainty period associated with gold and silver prices coincided with the 1981-82 recession. The gold market, which is often used as a hedging instrument against ination pressures, experienced huge movements in 1980 due to the continued stop-and-go monetary policy of the Fed raising its rate to 20%, then lowering it to 8%, and augmenting it again to 20%. 38 Uncertainty in the gold price was further amplied in 1981, when Reagan decided to establish a Gold Commission to reject the return of the United

States to the gold standard system, leading the Fed to implement a contractionary policy that reduced ination but favored recession. Other events likely to inuence gold prices, such as the bombing of the Twin Towers in September 2001 or the launch of the Electronic Trading Funds (ETF's) in the gold market in 2003, do not lead to price uncertainty. The silver price displays a similar behavior as the gold price, but uncertainty appears to be amplied, particularly at the beginning of the 1980s. Among the several causes of silver price movements, various political eventsincluding the continuous U.S. hostage crisis in Iran and the Soviet invasion of Afghanistanmotivated an increasing demand for silver, keeping the price at relatively high levels in 1980 and leading to high uncertainty in the silver market. Another well-known episode aecting the silver price evolution around this period is the Hunt Brothers' story from the 1970s to the end of the 1980s. [START_REF] Kilian | Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U[END_REF] The worldwide 1981-82 recession strengthened this period of uncer- tainty. The propagation mechanism at play during this recessionary episode was intrinsically linked to precious metals markets through ination targeting and the Fed interest rate policy, contrary to the recent nancial crisis. Unlike gold, which is mainly used in jewelry and as a store of value, platinum is a crucial resource in industryautomotive industry, for instance, represented approximately 60% of the total demand for platinum in 2007 (Johnson Matthey Plc., 2008). Its price is thus strongly related to economic activity, and episodes of high macroeconomic uncertainty tend to aect platinum price uncertainty, as it was the case during the 2007-09 recession.

As the oil price, most of industrial markets are strongly connected to global economic activity.

This important link is evidenced by the transfer of uncertainty from the macroeconomic level to commodity prices, especially for copper, lead, nickel and tin prices (see Figures 3E in Appendix E). For instance, because copper is primarily employed for industrial purposes 40 (such as mas- 38 On January 21, 1980, the gold price reached a record level of $850.

39 At the beginning of the 1970s, the Hunt Brothers began accumulating large amounts of silver to hedge against oil investment, totaling almost the global market by 1979 (with an estimated holdings of the one third of the entire silver world supply). This accumulation rose the price of silver from $11 per ounce at the end of 1979 to $50 per ounce in January 1980. In response to this speculative accumulation, the COMEX adopted on January 7, 1980 the Silver Rule 7 placing heavy restrictions on the purchase of this commodity. Three months after (on March 27, 1980), the silver price collapsed to below $11 per ounce. This event is known as the Silver Thursday.

40 Over the past century, the demand for rened copper has increased from 500,000 metric tons to over 19 million metric tons.

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sive construction projects, infrastructure renewal, and telecommunication modications), it is usually strongly related to global economic activity, and its price is often used as an indicator of global development. Therefore, the 2007-09 period of macroeconomic uncertainty is closely related to copper price uncertainty, as shown in Figure 3E. Similar patterns are observed for the other industrial markets, which are also strongly related to global economic conditions.

The transfer of macroeconomic uncertainty to agricultural and food prices uncertainty is of primary importance given the strong economical, political and social implications for both developing and developed countries. The increase in agricultural commodity prices since the mid-2000s has attracted particular attention among policymakers with regard to a global food shortage and inationary pressures. 41 Though a popular perception is that higher agricultural and food prices have been associated in recent yearsespecially during the 2007-09 period with oil price surges, this general statement has been recently contradicted by Baumeister and Kilian (2014), who argue that agricultural and food price increases should not be interpreted as the causal eect of higher oil prices, but are rather the result of unexpected changes in demand associated with shifts in global real economic activity. Our ndings (Figure 4E in Appendix E) conrm this interpretation, especially for corn, soybeans and wheat, for which price uncertainty occurs during the most striking episodes of macroeconomic uncertainty. Unexpected shifts in global economic activity would therefore be the main cause of uncertainty in agricultural prices during the 2007-09 recession. Periods for which agricultural price uncertainty is not related to macroeconomic uncertainty dier depending on the market. For instance, the most salient episode of coee price uncertainty appears during 1994-97, when the price started a brief recovery after an intense Brazilian frost episode in 1994, which reduced Brazil's coee exports.

In 1989, the coee price experienced a volatility spike after the end of the coee agreement, but this episode did not lead to uncertainty because it was largely anticipated. Another example is the cotton market, which is characterized by a strong uncertainty episode that occurred in 2011, with a huge unanticipated price increase that could be explained by a constellation of events such as gradually tightening stocks, an unexpected freeze in China's cotton producing areas, historic oods in Pakistan and a ban on exports from India.

Overall, though the results are obviously somewhat heterogeneous across markets, the recent 2007-09 recession generated an unprecedented episode of uncertainty in the price of numerous commodities, particularly oil, platinum, various agricultural commodities and, especially, all industrial raw materials. The main exception concerns uncertainty related to gold and silver prices, a fact that can be explained by their roles as safe havens and hedging instruments. An additional result is that, as clearly shown by Figures 1E to 4E in Appendix E, uncertainty episodes are not necessarily accompanied by high volatility in commodity prices.

This major nding illustrates the interest of our retained measure of uncertainty, underlining that uncertainty is more related to predictability than to volatility. The relevance of the 41 As documented by [START_REF] Baumeister | Do Oil Price Increases Cause Higher Food Prices?[END_REF], in the Monday 23, 2007 edition, the Financial Times reported that retail food prices are heading for their biggest annual increase in as much as 30 years, raising fears that the world faces an unprecedented period of food price ination.

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predictability-based approach could be explained by some specic properties of the commodity markets. In particular, these markets are known to be characterized by a low elastic demand together with a strong inertial supply, making any unexpected adjustment dicult and costly.

Historical decomposition analysis

To assess the contribution of macroeconomic uncertainty to commodity prices uncertainty, we perform a historical decomposition analysis of each commodity market uncertainty with respect to macroeconomic uncertainty. Based on the estimation of VAR models, 42 Figure 1F in Appendix F reports the historical decomposition associated with oil and copper prices uncertainty. [START_REF] Kilian | Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003-2008[END_REF] Results in Appendix F conrm those of the previous section. Indeed, looking at the crude oil market, we nd a strong proportion of macroeconomic uncertainty in oil price uncertainty during the recent 2007-09 nancial crisis (around 35% of oil price uncertainty is explained by macroeconomic uncertainty during this period). Recalling that our proxy of macroeconomic uncertainty is demand-driven, these conclusions are in line with the literature.

During the 1986-87 episode, oil price uncertainty is not explained by macroeconomic uncertainty and the proportion of macroeconomic uncertainty appears to be negative. This suggests that other shocks not related to economic activity have been at play during this period. Turning to the copper price, we also nd a transfer from macroeconomic uncertainty to price uncertainty, the share of macroeconomic uncertainty being around 12% during the recent nancial turmoil.

Overall, these results show that a signicant component of price uncertainty can be explained by macroeconomic uncertainty. A key nding is that the recent oil price movements in 2005-08 associated with a rise in oil price uncertainty appear to be mainly the consequence of macroeconomic uncertainty, conrming the endogeneity of the oil price with respect to economic activity (i.e., the demand-driven characteristic).

Distinguishing between dierent types of shocks: the special case of oil

As stressed above, macroeconomic uncertainty contributes to a large extent to price uncertainty.

This result is of primary importance, particularly for the oil market since it shows that oil price uncertainty during the 2005-08 period can be partly explained by macroeconomic uncertainty.

Besides, the oil price uncertainty indicator (see Figure 1E in Appendix E) also reveals that the volatility of the crude oil price can be sometimes disconnected to price uncertainty, some shocks leading to uncertainty and volatility while some others not. One reason could be the origin of price shocks that can have dierent impacts on price uncertainty. Specically, four types of shocks can be distinguished (see, e.g., [START_REF] Kilian | Not all oil price shocks are alike: Disentangling Demand and Supply Shocks in the Crude Oil Market[END_REF]and Baumeister and Peersman, 42 The lag order of the VAR specication is 3, as selected by usual information criteria.

43 Due to space constraints, we only report the results for oil and copper, two series that are strongly related to the macroeconomic context. The results for the other series, available upon request to the authors, show that the contribution of macroeconomic uncertainty to commodity price uncertainty is somewhat heterogeneous between markets.

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2013): (i) shocks to the ow supply of oil, (ii) shocks to the ow demand for crude oil reecting the state of the global business cycle, (iii) shocks to the speculative demand for oil stocks above the ground, and (iv) other idiosyncratic oil demand shocks. These dierent shocks may also be reected in the degree of oil price uncertainty movements. Specically, we aim here at investigating which type of shock contributes the most to oil price uncertainty.

As it is common in the literature, we proxy the ow supply in two ways: by the data on Saudi Arabia crude oil production, and by the global crude oil productionboth series being from the Energy Information Agency (EIA). 46 and the Iraq War in 2002-03 47 as episodes of surges in speculative demand for oil responsible for sharp price increases, we nd that these events are not associated with important oil price uncertainty. More importantly, the contribution of the speculative shocks appears to be very limited or even negative compared to the contribution of ow supply (around 17%) and ow demand (4%) shocks in 1990.

Several events in the oil market history have appeared during the 1986-87 period. The two most important are the decision of Saudi Arabia to shut down the crude oil production to prop up the price of oil, and the OPEC collapse. While the latter is known to have limited impact on the crude oil price (Barsky and [START_REF] Barsky | Oil and the Macroeconomy since the 1970s[END_REF], the former created a major positive shock to the ow supply droving down the price of oil. As we have seen, this period has lead to an important movement in oil price uncertainty. Figure 1G in Appendix G shows that this event is mainly supply-driven: around 18% of oil price uncertainty is explained by the shut down of Saudi Arabia crude oil production against less than 4% by the ow demand.

The most interesting episode over the last decades is obviously the unprecedented price surge after 2003 and, in particular, in 2007-08, which led to the most heightened period of oil price 44 We only report results with Saudi Arabia crude oil production because they are more signicant. Results from the global crude oil production are available upon request to the authors.

45 As Kilian and Murphy (2014), we scaled the data on crude oil inventories by the ratio of OECD petroleum stocks over U.S. petroleum stocks for each time period.

46 [START_REF] Hamilton | Causes and Consequences of the Oil Shock of 2007-08[END_REF] rejected the hypothesis that shifts in speculative demand were behind the sharp increase in the crude oil price in 1990. Rather, he suggested that the price increase was the consequence of oil supply shocks. More recently, [START_REF] Kilian | The role of inventories and speculative trading in the global market for crude oil[END_REF] found that the oil price increase was represented by two shocks occurring simultaneously: an unexpected ow supply disruption and an unexpected increase in speculative demand.

47 Two eects on the oil price are at play during this period: a positive speculative demand shock due to the Iraq War in 2002-03, and a negative ow supply shock due to the Venezuelan crisis (see [START_REF] Kilian | The role of inventories and speculative trading in the global market for crude oil[END_REF], for a discussion).

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uncertainty. According to a popular view, this price increase was the consequence of speculative behaviors on the market (i.e., growing nancialization of oil futures markets) and could not be explained by changes in economic fundamentals (see [START_REF] Fattouh | The Role of Speculation in Oil Markets: What Have We Learned So Far?[END_REF], for a discussion).

The standard interpretation is that oil traders in spot markets buy crude oil now and store it in anticipation of higher future oil prices. On the contrary, the recent literature supports the conclusion that the surge in the oil price during this period was mainly caused by shifts in the ow demand driven by the global business cycle (see [START_REF] Kilian | Not all oil price shocks are alike: Disentangling Demand and Supply Shocks in the Crude Oil Market[END_REF][START_REF] Kilian | Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003-2008[END_REF].

Our ndings corroborate this view that oil price uncertainty has been primarily driven by global macroeconomic conditions. Indeed, as shown in Figure 1G, the contribution of speculative demand to price uncertainty is very small (around 5%) compared to the proportion of the ow demand from the global business cycle in 2008 (around 40%). An alternative view regarding speculation is that OPEC held back its production by using oil below ground in anticipation of higher oil prices. As discussed by [START_REF] Kilian | The role of inventories and speculative trading in the global market for crude oil[END_REF], this behavior would be classied as a negative oil supply shock. Our results provide no evidence that such negative oil supply shocks have signicantly contributed to oil price uncertainty, contrary to demand shocks.

Finally, looking at Figure 2G in Appendix G, which reports the simultaneous contribution of each shock (ow demand, ow supply, speculative demand, and macroeconomic uncertainty)

to oil price uncertainty, we nd that the 2007-08 period of heightened oil price uncertainty is mainly the consequence of shocks coming from the global business cycle and macroeconomic uncertainty.

6 Robustness checks Figure 1H in Appendix H reports the response of commodity markets (for oil, gold, copper and cotton prices) following a positive two-standard-deviation VXO index and macroeconomic uncertainty shocks in the high-uncertainty state. 51 , 52 In both cases, the level of oil and cotton prices is reduced following the shock, leading to similar conclusions whatever the retained proxy for uncertainty. The pattern is quite dierent for the other markets. Indeed, the response of gold and copper prices following a VXO index shock is very low compared to that obtained with 51 Due to space constraints, we only report the results for one series per group of commodities, and focus on positive shocks (i.e., increase in uncertainty) in the high-uncertainty regime. Complete results are available upon request to the authors.

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52 Following Bloom (2009), we include the log of the S&P500 stock market index as the rst variable in the VAR estimations to ensure the impact of stock market levels is already controlled for when looking at the eect of volatility shocks. Results are robust to alternative ordering.

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the macroeconomic uncertainty proxy. This particularity has also been noted by Jurado et al.

(2015) regarding the response of production and employment, nding that the magnitude is larger when the shock comes from macroeconomic uncertainty at 1 month rather than from the VXO index. Moreover, macroeconomic uncertainty shocks generate a less signicant volatility overshoot in commodity prices than the VXO index (with the exception of the cotton price).

While the eect of macroeconomic uncertainty on commodity prices (especially for oil and copper prices) is more protracted (around 7 months), the response of prices after the VXO index shock is more transient (around 4 months for oil prices). This result further conrms that the macroeconomic uncertainty proxy is more persistent compared to popular volatility-based uncertainty proxies.

To complete our comparison, we follow the same procedure as in Section 5, and investigate how volatility-based uncertainty linked to the VXO index can generate commodity uncertainty.

Figure 2H in Appendix H reports the evolution of commodity uncertainty for oil, gold, copper, and cotton prices for both the VXO and macroeconomic uncertainty proxies at 1 month (h = 1).

When uncertainty in commodity markets exceeds the horizontal bar, this refers to episodes of heightened uncertainty for the considered return series. When commodity uncertainty coincides with vertical bands, it indicates that uncertainty episodes occur at the same period than macroeconomic uncertainty (green bands for the VXO-based uncertainty measure and gray bands for our macroeconomic uncertainty proxy). As shown in Figure 2H, both proxies generate quite similar commodity uncertainty dynamics. An interesting result concerns the case of the oil market for which commodity uncertainty is found to be more pronounced during the recent crisis using our retained macroeconomic uncertainty measure than with the VXObased uncertainty measure. This is in line with our previous results showing that uncertainty episodes are not necessarily accompanied by high volatility in commodity prices. Moreover, macroeconomic uncertainty rightly aects the gold market during the 1981-82 recession, a characteristic that we identify using our predictability-based measure but not with Bloom (2009)'s proxy. On the whole, these ndings illustrate the robustness of our conclusions to the retained proxy of uncertainty, and highlight the relevance of our measure in linking uncertainty to predictability rather than to volatility.

Robustness of our proxy of oil price uncertainty

An important query regarding our results in Section 5 refers to the robustness of our proxy of commodity price uncertainty. This question is of primary importance since, as discussed in the literature, 53 one reason why previous studies do not nd plausible empirical evidence of oil price uncertainty impact on GDP lies in the choice of an appropriate measure of uncertainty.

Theoretically, our approach implicitly assumes that oil price uncertainty (and commodity price uncertainty in general) is endogenous with respect to macroeconomic activity. As discussed in 53 See Kilian and Vigfusson (2011) for a discussion. given the forward-looking nature of investors' expectations, our proxy appears to be the most suitable measure of uncertainty.

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7 Conclusion

The aim of this paper is to analyze the impact of macroeconomic uncertainty on various raw materials markets. To this end, we consider a large sample of 19 commodities and rely on a robust measure of macroeconomic uncertainty based on a wide range of monthly macroeconomic and nancial indicators. To assess whether the eect of macroeconomic uncertainty on commodity price returns depends on the degree of uncertainty, we estimate a nonlinear, structural threshold vector autoregressive model. Our ndings show that whereas the safe-haven role of precious metals is conrmed, agricultural and industrial markets are highly sensitive to the variability and the level of macroeconomic uncertainty, respectively. In addition, investigating the links between macroeconomic uncertainty and commodity price uncertainty, we show that the recent 2007-09 recession has generated an unprecedented episode of high uncertainty in the price of numerous raw materials, especially those that are linked to global economic activity.

Finally, we nd evidence that uncertainty episodes are not necessarily accompanied by high volatility in commodity prices, highlighting the relevance of our uncertainty measure in linking uncertainty to predictability rather than to volatility.

55 Another way to compare alternative measures of oil price uncertainty is to test which approach leads to the best predictor of volatility. [START_REF] Alquist | Forecasting the Price of Oil[END_REF] showed that the oil futures price is of limited use in forecasting the oil price, questioning the interest of using the implied volatility to measure uncertainty. 3. Given the initial condition (Ω r t-1 ), the estimated TVAR coecients for both regimes, and the shock process for h + 1 period, the future evolution of all variables is simulated;

Figure 1 : 12 Figure 2 :

 1122 Figure 1: Alternative measures of macroeconomic uncertainty

20

  In constructing the factors (see Equation (3)), the raw data have been trans- formed to achieve stationarity. Finally, it is worth mentioning that whereas both sets of data are used to estimate the forecasting factors, macroeconomic uncertainty is proxied using the 132 macroeconomic time series only. Indeed, as explained by[START_REF] Jurado | Measuring Uncertainty[END_REF], uncertainties in the 147 nancial indicators are not computed since the macroeconomic dataset already contains some nancial series, and the authors do not wish to over-represent the nancial sector in the aggregate uncertainty measure.As shown by[START_REF] Jurado | Measuring Uncertainty[END_REF], their measure of macroeconomic uncertainty exhibits a quantitatively important dynamic relationship with global economic activity, as well as with business cycles. Indeed, common macroeconomic uncertainty shocks are much larger associated with a decline in real activity (production, hours worked and employment) than are shocks to stock market volatility. In addition, macroeconomic uncertainty is evidenced to be (i) highly countercyclical, explaining a larger part of total uncertainty during recessions than in other periods, and (ii) strongly more persistent than usual uncertainty proxies. Overall, according to[START_REF] Jurado | Measuring Uncertainty[END_REF], important macroeconomic uncertainty shocks are strongly linked to changes in economic activity, and even more during recessions. 17 The results of unit root tests are available upon request to the authors and show that all price return series are stationary at conventional statistical levels.

  18 https://www.sydneyludvigson.com/. 19 Those data are described on Mc Cracken's homepage: https://research.stlouisfed.org/econ/mccracken/fred- databases/. 20 A detailed description can be found in the on-line supplementary le of Jurado et al. (2015)'s paper, available on Ludvigson's homepage.A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT Finally, an appealing characteristic is that our retained uncertainty measures have the advantage of providing far fewer important uncertainty episodes than do popular proxies. As an example, though Bloom (2009) identies 17 uncertainty periods based on stock market volatility, Jurado et al. (2015) nd evidence of only three episodes of uncertainty over the 1959-2011 period: the month surrounding the 1973-74 and 1981-82 recessions and the recent 2007-09 great recession. This suggests that much variability in the stock market does not come from changes in genuine uncertainty across the broader economy: in other words, much of the variation in traditional uncertainty proxies is not driven by uncertainty itself. Such variability may come from other factors as changes in risk aversion, sentiments, leverages, etc. As stressed above, this nding can be explained by the fact that (i) standard proxies tend to overestimate the weight of a single series in the evaluation of macroeconomic uncertainty, and (ii) popular uncertainty proxies based on volatility measures usually erroneously attribute to uncertainty uctuations that are actually forecastable.

  the form: 21 See Balke(2000),[START_REF] Tong | Threshold Models in Time Series Analysis-30 Years On[END_REF], and[START_REF] Hansen | Threshold Autoregression in Economics[END_REF] for more details on these models. It is important to note that nonlinearities may arise in many dierent forms. So far, empirical papers on the energy prices-economic activity nexus have mainly focused on asymmetric specications (see e.g.[START_REF] Hamilton | This is What Happened to the Oil-Price Macroeconomy Relationship[END_REF][START_REF] Kilian | Nonlinearities in the Oil Price-Output Relationship[END_REF][START_REF] Venditti | From Oil to Consumer Energy Prices: How much Asymmetry along the Way?[END_REF]. Our main purpose in this paper is to investigate whether the eect of macroeconomic uncertainty on commodity price returns depends on the regime of uncertainty, justifying the use of a threshold specication.

  [START_REF] Jurado | Measuring Uncertainty[END_REF]'s conclusions, highlighting that the level of uncertainty tends to augment along with the maturity. Third, turning to the percentage of high uncertainty, the results deserve some detailed comments. Whatever the maturity considered, agricultural and industrial markets display the highest percentages, whereas energy and precious metals markets are characterized by similar lower values. Moreover, high-uncertainty percentage values are quite stable, regardless of the considered maturity for the latter two markets. Regarding precious metals, this result can be explained by their safe-haven role across all horizons during times of economic turmoil (Bredin et al., 2014), making them an interesting tool to transfer short-run uncertainty to longer one. Concerning energy markets, the limited substitution eects at relatively short-term horizons may explain the stability of the high-uncertainty percentage values. Indeed, whereas consumers' demand for energy may potentially slightly decrease following its corresponding price rise, this dynamics is weakened by limited substitution opportunities. It may be more accentuated at longer horizons but two brakes will be at play. First, such movement would take some time to be truly eective given the required important investments needed in the energy domain. Second, the substitution 26 Results (available upon request to the authors) are robust to dierent lag specications.27Following Jurado et al. (2015), we retain three maturities corresponding to macroeconomic uncertainty at 1, 3, and 12 months, referring to short-, medium-, and long-run perceptions, respectively.

Figure

  Figure1Bin Appendix B presents these ndings in a more dynamic way by showing the evolution of our macroeconomic uncertainty measure at one month over the whole period together with the corresponding threshold value. For almost all markets, periods of high uncertainty coincide with episodes of heightened macroeconomic uncertainty, such as the 1981-82 and 2007-09 recessions. In addition and as expected given our preceding arguments (See Section 3), we identify a few high-uncertainty episodes compared with studies that rely on standard volatility-type proxies, such as[START_REF] Bloom | The impact of uncertainty shocks[END_REF] and Van Robays (2013).

31 5. 2

 312 Figures 1E to 4Ein Appendix E depict the evolution of uncertainty in commodity markets for 1 month (blue line), together with the evolution of corresponding prices (black line) and volatility (green line). The horizontal bar corresponds to 1.65 standard deviation above the mean of each commodity-related uncertainty series. When uncertainty in commodity markets exceeds the horizontal bar, this refers to episodes of heightened uncertainty for the considered price return series. When commodity price uncertainty coincides with the vertical gray bands, it indicates a potential transfer from macroeconomic to commodity market uncertainty (with both uncertainty episodes occurring in the same period).

  that just follows the invasion of Kuwait in 1990, the Afghan war in 2001, and the Iraq War in 2002-03 are episodes characterized by sharp spikes in oil prices. The Iran-Iraq war in 1980 and the 1999 OPEC meeting are, in contrast, associated with small price movements. As stressed by[START_REF] Barsky | Oil and the Macroeconomy since the 1970s[END_REF], a simplistic view should be that major war episodes cause price uncertainty to increase through a rise in precautionary demand for oil. However, among all episodes of important uctuations in oil prices, only two seem to be accompanied by uncertainty: 33 (i) the 2007-09 recession, and (ii) the 1984-86 period. During the 2007-09 recession, oil price uncertainty is indeed very sensitive to macroeconomic uncertainty, a result that is not surprising given the well-known relationship that exists between economic activity and the oil market. 34 This episode of high oil price uncertainty is accompanied by the biggest oil price spike in the postwar experience and results from various macroeconomic factors. A common explanation lies in the global economic growth starting in 2003, as illustrated by the increase in real gross world product combined with the stagnant oil production from Saudi Arabia from 2005 to 2007. 35 Whatever the origin of price surges, this period of macroeconomic uncertainty 31 See Chan and Jeliazkov (2009) and Chan and Hsiao (2013) for more details. The Matlab code used to estimate the moving average stochastic volatility model is freely available from the website of Joshua Chan. We obtain 20000 draws from the posterior distribution using the Gibbs sampler after a burn-in period of 1000. 32 Recall that the gray bands correspond to episodes of important macroeconomic uncertainty: the months surrounding the 1973-74 and 1981-82 recessions and the 2007-09 great recession. 33 In Section 5.2 we precisely investigate what types of shocks tend to increase oil price uncertainty.

  (i) the production shutdown in Saudi Arabia between 1981 and 1985, which caused a strong price decrease; 36 and to a lesser extent (ii) the OPEC collapse in 1986. On the whole, our results are in line with the literature that has recently stressed the limited impact of exogenous events on oil price uctuations. Another interesting period is the one from June 2014 up to now where the WTI crude oil price has fallen from US$ 105.8 to around US$ 40 per barrel. Most of this decline appeared between June 2014 and January 2015 where the price fell by nearly 44% leading to severe economic stress on oil producers. Because sustained declines in the price of oil are rare events, many observers and authors have conjectured that unexpected factors specic to the oil market have played an important role. Arezki and Blanchard (2014), for example, attribute a large part of the steep decline in the price of oil to a shift in expectations about the future path of global oil production (especially from the Libyan and Iraqi oil production, and the OPEC November 2014 announcement). Competing with this view, Baumeister and Kilian

  struments during nancial and macroeconomic turmoils, their prices are often countercyclical as well (see Christie-David et al., 2000; Roache and Rossi, 2009; Creti et al., 2013) and should increase in periods of macroeconomic uncertainty, at least in the short run. Regarding the 2007-09 recession, it seems (Figures 2E in Appendix E) that increases in gold and silver prices were in 2007.

Figure 2 :

 2 Figure 2: Bloom (2009)'s index vs. Jurado et al. (2015)'s measure for uncertainty

Figure 1 :

 1 Figure 1: Bloom (2009)'s index vs. Jurado et al. (2015)'s proxy

  uncertainty episodes, suggesting that variability in stock markets is much more related to volatility than to uncertainty aecting the whole economy. Therefore, comparing these two approaches allows us to compare both measures of uncertainty, namely the volatility-based and predictability-based proxies. Theoretically, each measure relies on dierent econometric concepts: the former approach approximates uncertainty by conditional volatility, while the latter requires to remove the forecastable component of the series before computing its conditional volatility. In other words, the predictability-based proxy distinguishes uncertainty in a series and its conditional volatility, while the volatility-based measure does not. As an illustrative purpose, Figure2reports the VXO index of Bloom (2009) and the macroeconomic uncertainty proxy (h = 1) introduced by Jurado et al. (2015) in standardized units. As shown, the predictability-based proxy allows us to identify only three episodes of uncertainty, which is far fewer than the 17 uncertainty dates detected using Bloom (2009)'s measure.

Figure 3 :

 3 Figure 3: Alternative measures of oil price uncertainty

Section 2 ,

 2 this debate regarding the endogenous/exogenous nature of oil prices is of primary importance since it conditions the framework used to investigate the economy-oil price nexus.Considering that commodity price uncertainty is endogenous with respect to macroeconomic uncertainty does not mean that exogenous eects are null or without interest, but rather that they are secondary or indirect. By constructing our proxy of uncertainty based on the global measure of[START_REF] Jurado | Measuring Uncertainty[END_REF], we allow both the supply and demand components of the price to exist, but condition the latter to be the primary channel. With regard to this property, our measure of oil price uncertainty is thus theoretically robust, compared to those based on conditional volatility or implied volatility.Empirically, existing proxies of oil price uncertainty have been so far either based on conditional volatility, implied or realized volatility. However, due to their backward-or forward-looking nature, it is unclear how these proxies can properly capture oil price uncertainty. To illustrate this issue, Figure3reports the one-month implied crude oil volatility from the daily CBOE VXO volatility index (Panel A), the realized volatility estimate constructed from daily percent changes in the WTI crude oil spot price 54 (Panel B), the conditional variance of the percent change of WTI crude oil spot price based on the estimation of a GARCH(1,1) model (Panel C), and our oil price uncertainty measure based on the variance of the forecast errors conditional on macroeconomic uncertainty (Panel D). For almost all series, the most important peak corresponds to the recent, 2007-09 nancial turmoil and occurs near the end of 2008. However, 54 See Bachmeier, Li and Liu (2008).

A

  C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTmajor dierences between the four proxies can be highlighted regarding their backward-and forward-looking degree. The GARCH-based proxy which peaks on January 2009 is the most backward-looking measure compared to the realized and implied volatility approaches which both peak on December 2008. Our oil price uncertainty proxy conditional on macroeconomic uncertainty is the most forward-looking measure since it peaks on October 2008. Therefore,

2 .

 2 Pick a sequence of shocks from the variance-covariance matrix by bootstrapping the residuals of the TVAR depending on the regime drawn at each horizon (given the assumption of the joint distribution of shocks);

4 .

 4 Repeat the previous exercise by adding a new shock at time 0; 5. Steps 2 to 4 are repeated B times (here B = 500) in order for the shocks to average out; 6. Repeat B times (here B = 500) steps 1 to 4 to obtain an average out over the respective regime history; A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT

7 .Y

 7 Compute the average dierence between the shocked simulated forecast and the nonshocked one, and then compute the average GIRF associated with a particular regime with R observations as:GIRF h, Ω t-1 , u * j t+h Ω r t-1 | u * j , u t+h -Y t+h Ω r t-1 | u t+h

  in October 1978 for energy markets, February 1976 for precious metals markets, and February 1980 for agricultural and industrial markets. The period ends in April 2015 for all groups of commodities. All series are transformed into rst-logarithmic dierences (i.e., price returns), 17 as described in Table1in Appendix A, which also provides data sources.Turning to macroeconomic uncertainty measures for distinct maturities, we rely on data freely available on Ludvigson's homepage.[START_REF] Don | Does gold glitter in the long-run? Gold as hedge and safe haven across time and investment horizon[END_REF] As stressed above, these uncertainty indexes are based on the information contained in several macroeconomic and nancial series. Specically, two monthly datasets are considered. The rst one gathers together 132 macroeconomic time series used in[START_REF] Ludvigson | A Factor Analysis of Bond Risk Premia[END_REF] and extracted from the FRED-MD monthly database, 19 including real output and income, employment and hours, real retail, manufacturing and trade sales, consumer spending, housing starts, inventories and inventory sales ratios, orders and un-

lled orders, compensation and labor costs, capacity utilization measures, price indexes, bond and stock market indexes, and foreign exchange measures. The second dataset consists of 147 time series of nancial indicators that measure the behavior of a broad cross-section of asset returns and some aggregate nancial indicators not contained in the rst, macroeconomic dataset. This nancial dataset includes dividend-price and earning-price ratios, growth rates of aggregate dividends and prices, default and term spreads, yields on corporate bonds of dierent ratings grades, yields on Treasuries and yield spreads, and a broad cross-section of industry equity returns.

  44 Our measure of uctuations in global real activity is the dry cargo shipping rate index developed by[START_REF] Kilian | Not all oil price shocks are alike: Disentangling Demand and Supply Shocks in the Crude Oil Market[END_REF]. Finally, turning to the speculative component of the oil demand, we rely on data for the U.S. crude oil inventories provided by the EIA.[START_REF] Kilian | Nonlinearities in the Oil Price-Output Relationship[END_REF] Figure1Gin Appendix G allows us to assess the quantitative importance of each type of shock (supply, demand, and speculation) on oil price uncertainty. Results show that the contribution of each shock to price uncertainty varies depending on the period, and that the nature of the shock matters in explaining oil price uncertainty. For instance, while Kilian (2009) identied the invasion of Kuwait in 1990

  6.1 Volatility-based vs. predictability-based uncertainty measureTo conrm the relevance of our predictability-based measure in the case of commodity markets, we assess the robustness of our ndings to the choice of the proxy retained for uncertainty. As stressed above, uncertainty is an abstract concept that may have dierent meanings. Indeed, according to behavioral theories, it reects a psychological state about possible futures, 48 but it is also a broad concept linked to macro or micro phenomena (like GDP growth or the growth rate of rms among others). 49 Whatever the considered denition, there is no perfect measure of uncertainty but instead a broad range of proxies. As described in Section 2, several studies have used stock market volatility as a proxy for uncertainty.[START_REF] Pindyck | Uncertainty and exhaustible resource markets[END_REF] In an inuential paper, Bloom

	(2009) proposes a popular measure of uncertainty based on the VXO index, constructed by the
	Chicago Board of Options Exchange from the prices of options contracts written on the S&P
	100 index. He identies around 17 uncertainty dates, whereas Jurado et al. (2015) report far
	48 In many behavioral theories, psychological uncertainty is assumed to be an important mediator of human
	in situations with unknown outcomes (see Windschitl and Wells, 1996).
	49 See Bloom (2014) for more details.

50 See e.g.

[START_REF] Romer | The Great Crash and the Onset of the Great Depression[END_REF]

, Hassler (2001), Greasley and Madsen (2006), Bloom et al. (2007), and Gilchrist et al. (2014).

Table 2 :

 2 Tests for the threshold eect in energy markets at 1, 3 and 12 months ) 48.50 (0.000 * ) 40.51 (0.000 * ) 16.25% Notes: sup-Wald: maximum Wald statistic over all possible threshold values, avg-Wald: average Wald statistic over all possible values, exp-Wald: function of the sum of exponential Wald statistics. Corresponding p-values are given in parentheses. * , * * denote the rejection of the null hypothesis at 1% and 5% signicance levels, respectively.

	Horizon h Threshold		Wald Statistics	% high uncertainty
		value	Sup-Wald	Avg-Wald	Exp-Wald
	h = 1	0.7596	101.84 (0.000 * ) 52.15 (0.030 * * ) 45.51 (0.000 * )	18.25%
	h = 3	0.9138	111.38 (0.000 * ) 53.90 (0.010 * * ) 51.22 (0.000 * )	18.00%
	h = 12	0.9890	90.42 (0.000

*

Table 3 :

 3 Tests for the threshold eect in precious metals markets at 1, 3 and 12 months Wald: maximum Wald statistic over all possible threshold values, avg-Wald: average Wald statistic over all possible values, exp-Wald: function of the sum of exponential Wald statistics. Corresponding p-values are given in parentheses. * denotes the rejection of the null hypothesis at the 1% signicance level.

	Horizon h Threshold		Wald Statistics	% high uncertainty
		value	Sup-Wald	Avg-Wald	Exp-Wald
	h = 1 184.15 (0.000 h = 3 0.7509 0.9091 194.31 (0.000 * ) 99.28 (0.000 * ) 91.98 (0.000 * )	16.90%
	h = 12	0.9798	194.45 (0.000 * ) 103.58 (0.000 * ) 91.94 (0.000 * )	17.83%
	Notes: sup-

* ) 92.34 (0.002 * ) 87.51 (0.000 * ) 17.59%

Table 4 :

 4 Tests for the threshold eect in industrial markets at 1, 3 and 12 months ) 325.11 (0.000 * ) 210.79 (0.000 * ) 35.16% Notes: sup-Wald: maximum Wald statistic over all possible threshold values, avg-Wald: average Wald statistic over all possible values, exp-Wald: function of the sum of exponential Wald statistics. Corresponding p-values are given in parentheses. * denotes the rejection of the null hypothesis at the 1% signicance level.

	Horizon h Threshold		Wald Statistics	% high uncertainty
		value	Sup-Wald	Avg-Wald	Exp-Wald
	h = 1	0.7155	431.73 (0.000 * ) 326.01 (0.001 * ) 211.61 (0.000 * )	23.70%
	h = 3	0.8542	442.03 (0.000 * ) 318.74 (0.000 * ) 216.16 (0.000 * )	32.29%
	h = 12	0.9481	432.07 (0.000

*

Table 5 :

 5 Tests for the threshold eect in agricultural markets at 1, 3 and 12 months Wald: maximum Wald statistic over all possible threshold values, avg-Wald: average Wald statistic over all possible values, exp-Wald: function of the sum of exponential Wald statistics. Corresponding p-values are given in parentheses. * denotes the rejection of the null hypothesis at the 1% signicance level.

	Horizon h Threshold		Wald Statistics	% high uncertainty
		value	Sup-Wald	Avg-Wald	Exp-Wald
	h = 1 467.27 (0.000 h = 3 0.6827 0.8780 507.56 (0.000 * ) 453.68 (0.000 * ) 248.53 (0.000 * )	22.92%
	h = 12	0.9668	539.70 (0.000 * ) 462.15 (0.000 * ) 264.91 (0.000 * )	23.44%
	Notes: sup-

* ) 432.00 (0.000 * ) 229.12 (0.000 * ) 42.45%

See[START_REF] Bloom | Fluctuations in Uncertainty[END_REF] for a review of the related literature. The growth option argument is based on the idea that uncertainty can encourage investment and R&D if it increases the size of the potential prize. The Oi-Hartman-Abel eect refers to the fact that rms can expand to exploit good outcomes and contract to insure against bad outcomes, diminishing the potential impact of uncertainty.

6 Note that we do not explicitly investigate the channels through which uncertainty impacts commodity marketsa question that we leave for future researchbut examine whether this impact may dier across levels of maturity.

See Bloom (2009), Bloom et al. (2010), Fernandez-Villaverde et al. (2011), Bloom et al. (2012), Gilchrist et al. (2014), and Baker et al. (2015) among others.

A C C E P T E D M A N U S C R I P T

Appendix A. Data sources, threshold test results, and algorithm for GIRFs computation Dataset and threshold test results

Algorithm for GIRFs computation

Following [START_REF] Balke | Credit and economic activity: Credit regimes and nonlinear propagation of shocks[END_REF], GIRFs are derived from the following algorithm:

1. Pick past history for all variables in the model Ω r t-1 ;

A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT

Highlights

 We analyze the impact of macroeconomic uncertainty on a large sample of 19 commodity markets.  The safe-haven role of precious metals is confirmed, while agricultural and industrial markets are markets are highly sensitive to the variability and the level of macroeconomic uncertainty, respectively.  The recent 2007-09 recession has generated an unprecedented episode of high uncertainty in numerous commodity markets that is not necessarily accompanied by a subsequent volatility in the corresponding prices.