•We analyze the role of financial stress in forecasting oil-price volatility.•We use various variants of the Heterogenous Autoregressive model of realized volatility.•We find that indexes of ...financial stress help to improve forecasting performance.•The shape of the forecaster loss function that used to evaluate performance is important.•Alternative types of investors benefit from monitoring different regional sources of financial stress.
We analyze the role of global and regional measures of financial stress in forecasting realized volatility of the oil market based on 5-min intraday data covering the period of 4th January, 2000 until 26th May, 2017. In this regard, we use various variants of the Heterogeneous Autoregressive (HAR) model of realized volatility (HAR-RV). Our main finding is that indexes of financial stress help to improve forecasting performance, with it being important to differentiate between regional sources of financial stress (United States, other advanced economies, emerging markets). Another key finding is that the shape of the forecaster loss function that one uses to evaluate forecasting performance plays an important role. More specifically, forecasters who attach a higher cost to an overprediction of realized volatility as compared to an underprediction of the same absolute size should pay particular attention to financial stress originating in the U.S. But, in case an underprediction is more costly than a comparable overprediction, then forecasters should closely monitor financial stress caused by developments in emerging-market economies. In sum, financial stress does have predictive value for realized oil-price volatility, with alternative types of investors benefiting from monitoring different regional sources of financial stress.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Much significant research has been done to study the links between gold returns and the returns of other asset classes in times of economic crisis and high uncertainty. We contribute to this research ...by using a novel nonparametric causality-in-quantiles test to study how measures of policy and equity-market uncertainty affect gold-price returns and volatility. For daily and monthly data, we find evidence of causality running from various uncertainty measures to both gold returns and volatility. For quarterly data, evidence of causality weakens and is significant only for some uncertainty measures and only for gold volatility.
•Predictability of uncertainty for Gold returns and volatility is tested.•We use nonparametric causality in quantiles test.•Daily, monthly and quarterly frequencies of data used.•Stronger evidence of predictability found for daily and monthly data.•Evidence is weaker for quarterly data and limited to returns only.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
We examine the predictive power of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) for oil-market volatility. Using the heterogeneous autoregressive realized ...volatility (HAR-RV) model, we document a positive effect of the EMVID index on the realized volatility of crude oil prices at the highest level of statistical significance, within-sample. Importantly, we show that incorporating EMVID into a forecasting setting significantly improves the forecast accuracy of oil realized volatility at short-, medium-, and long-run horizons. Our findings comprise important implications for investors and risk managers during the unprecedented episode of high uncertainty resulting from the COVID-19 pandemic.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
We extend the widely-studied Heterogeneous Autoregressive Realized Volatility (HAR-RV) model to examine the out-of-sample forecasting value of climate-risk factors for the realized volatility of ...movements of the prices of crude oil, heating oil, and natural gas. The climate-risk factors have been constructed in recent literature using techniques of computational linguistics, and consist of daily proxies of physical (natural disasters and global warming) and transition (U.S. climate policy and international summits) risks involving the climate. We find that climate-risk factors contribute to out-of-sample forecasting performance mainly at a monthly and, in some cases, also at a weekly forecast horizon. We demonstrate that our main finding is robust to various modifications of our forecasting experiment, and to using three different popular shrinkage estimators to estimate the extended HAR-RV model. We also study longer forecast horizons of up to three months, and we account for the possibility that policymakers and forecasters may have an asymmetric loss function.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas ...Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Because the U.S. is a major player in the international oil market, it is interesting to study whether aggregate and state-level economic conditions can predict the subsequent realized volatility of ...oil price returns. To address this research question, we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility (HAR-RV) model. To estimate the models, we use quantile-regression and quantile machine learning (Lasso) estimators. Our estimation results highlights the differential effects of economic conditions on the quantiles of the conditional distribution of realized volatility. Using weekly data for the period April 1987 to December 2021, we document evidence of predictability at a biweekly and monthly horizon.
We investigate whether oil-price uncertainty helps forecast the international stock returns of ten advanced and emerging countries. We consider an out-of-sample period of August 1925 to September ...2021, with an in-sample period between August 1920 and July 1925, and employ a quantile-predictive-regression approach, which is more informative relative to a linear model, as it investigates the ability of oil-price uncertainty to forecast the entire conditional distribution of stock returns Based on a recursive estimation scheme, we draw the following main conclusions: the quantile-predictive-regression approach using oil-price uncertainty as a predictor statistically outperforms the corresponding quantile-based constant-mean model for all ten countries at certain quantiles (capturing normal, bear, and bull markets), and over specific forecast horizons, compared to forecastability being detected for eight countries under the linear predictive model. Importantly, we detect forecasting gains in many more horizons (at particular quantiles) compared to the linear case. In addition, an oil-price uncertainty-based state-contingent spillover analysis reveals that the ten equity markets are connected more tightly at the upper regime, suggesting that heightened oil-market volatility erodes the benefits from diversification across equity markets.
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Using data for the group of G7 countries and China for the sample period 1996Q1 to 2020Q4, we study the role of uncertainty and spillovers for the out-of-sample forecasting of the realized variance ...of gold returns and its upside (good) and downside (bad) counterparts. We go beyond earlier research in that we do not focus exclusively on U.S.-based measures of uncertainty, and in that we account for international spillovers of uncertainty. Our results, based on the Lasso estimator, show that, across the various model configurations that we study, uncertainty has a more systematic effect on out-of-sample forecast accuracy than spillovers. Our results have important implications for investors in terms of, for example, pricing of related derivative securities and the development of portfolio-allocation strategies.
We examine the predictive value of gold-to-silver and gold-to-platinum price ratios, as proxies for global risks affecting the realized variance (RV) of oil-price movements, using monthly data over ...the longest available periods of 1915:01–2021:03 and 1968:01–2021:03, respectively. Using the two ratios, we find statistically significant evidence of in-sample predictability for increases in RV for both ratios. This finding also translates into statistically significant out-of-sample forecasting gains derived from these two ratios for RV. Given the importance of real-time forecasts of the volatility of oil-price movements, our results have important implications for investors and policymakers.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
I propose a simple to implement bootstrap-based efficiency (BBE) test to reexamine the efficiency of growth and inflation forecasts for Germany. The BBE test is useful as a test of forecast ...efficiency when a researcher, as is usually the case, can use a large number of macroeconomic and financial variables to proxy the information set of a forecast producer at the time when a forecast was published. A large number of proxy variables translates into a large number of candidate efficiency-regression models and the decision problem is that it is a priori unclear which model a researcher should choose to test for forecast efficiency. The BBE test solves this decision problem in that it requires a researcher to sample from the set of candidate models and, thereby, makes the decision problem tractable.
•Develops a simple-to-implement bootstrap-based efficiency test.•Test works efficiently when the number of predictors is large.•Applies test to study efficiency of growth and inflation forecasts for Germany.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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