Forecasting realized volatility of crude oil futures with equity market uncertainty

This paper examines whether the equity market uncertainty (EMU) index contains incremental information for forecasting the realized volatility of crude oil futures. We use 5-min high-frequency transaction data for WTI crude oil futures and develop six heterogeneous autoregressive (HAR) models based on classical HAR-type models. The empirical results suggest that EMU contains more incremental information than the economic policy uncertainty (EPU) for forecasting the realized volatility of crude oil futures. More importantly, we argue that EMU is a non negligible additional predictive variable that can significantly improve the 1-day ahead predictive accuracy of all six HAR-type models, and improve the 1-week ahead forecasting performance of the HAR-RV, HAR-RV-J, HAR-RSV, HAR-RV-SJ models. These findings highlight a strong short-term and a weak mid-term predictive ability of EMU in the crude oil futures market.     

The role of the political cycle in the relationship between economic policy uncertainty and the long-run volatility of industry-level stock returns in the United States

In this study, we investigate how US economic policy uncertainty (EPU) drives the long-run components of volatilities in industry-level stock markets. We use a modified specification of GARCH-MIDAS and find that EPU increases the long-run volatility of the industrials and materials industries and decreases it in 4 of the 10 industries considered here: consumer staples, healthcare, information technology and materials. In addition, we add a dummy variable for the political cycle (PLC) to study whether the relationship between EPU and the volatility of industry returns is significantly different under different political regimes. The results imply that a Republican presidency dampens the effects of EPU on the long-run volatility of the consumer staples, healthcare and information technology industries. We also decompose the aggregated EPU into 11 category-specific EPUs to explore the detailed relationship between category-specific EPU and long-run volatility driven by aggregate EPU. The results for the category-specific EPU are consistent with the findings for the aggregate EPU. In particular, the weakened effect of PLC on the relationship between EPU and the long-run volatility of industry-level returns is also confirmed by MIDAS regression with beta weight scheme.     

Predictability of sustainable investments and the role of uncertainty: evidence from a non-parametric causality-in-quantiles test

In this article, we examine sustainable investments returns predictability based on the U.S. Dow Jones Sustainability Index (DJSI) and a wide set of uncertainty and financial distress indicators for the period 2002:01–2014:12. To this end, we employ a novel non-parametric causality-in-quantile approach that captures non-linearities in returns distribution. Based on our findings we conclude that the aggregate economic policy uncertainty (EPU) indicator and some components have predictive ability for real returns of the U.S. sustainable investments index. Moreover, if we split our sample to before and after the global financial crisis our results suggest that predictors carry causal information for real returns only in the after-crisis period. Finally, some marginal evidence of predictability from sovereign debt is also observed at the lower and upper ends of the conditional distribution of the real returns of sustainable investments. Our results might entail policy implications for investors and market authorities.     

Does international oil volatility have directional predictability for stock returns? Evidence from BRICS countries based on cross-quantilogram analysis

While numerous studies have investigated the relationship between oil volatility and stock returns, it is surprising that little research has examined the quantile dependence and directional predictability from oil volatility to stock returns in BRICS (Brazil, Russia, India, China, and South Africa) countries. We address this issue by using the cross-quantilogram model proposed by Han et al. (2016). The empirical results show that, overall, oil volatility has a directional predictability for the stock returns in BRICS countries. When the oil volatility is in a low quantile (lower than its 0.1 quantiles), it is less likely to show either a large loss or a large gain in the stock market. In contrast, there is an increased likelihood of either large loss or a large gain in the stock market when the oil volatility is in a high quantile (higher than its 0.9 quantiles). The directional predictability from the oil volatility to stock returns depends on the net position of oil imports and exports of these BRICS countries in the oil market. The net oil exporters (Russia and Brazil) are less likely to have large gains and large losses in the stock market than are the net oil importers (India, China, and South Africa) when the oil volatility is in a low quantile. The net oil exporters are more likely to have large gains and large losses than are the net oil importers when the oil volatility is in a high quantile. The results are robust to change in the variable of oil volatility and the sample interval.     

The source of global stock market risk: A viewpoint of economic policy uncertainty

This study explores the effect of economic policy uncertainty (EPU) in four countries or regions (China, Japan, Europe, and the United States) on the contagion risk of investments in the global stock market. The stock returns of 22 stock markets worldwide are analyzed to determine which region’s EPU exhibits the greatest effect on regional systematic risk in the global stock market and on volatility risk in individual stock markets. First, all of the samples, the markets of different continents and the spillover indices of the developed and emerging markets, are calculated to observe the dynamic correlation among these markets with the aim of quantifying regional systematic risk and further examining the contagion risk effect of EPU. The results indicate the following: EPU in China is the most influential, and its contagion risk spreads to different regional markets, except for Europe; the effect of EPU in the United States is inferior to that in China; EPU in Japan merely influences contagion risk in emerging markets; contagion risk in European markets is not influenced by the four EPU indices; and EPU in Europe is not influenced by contagion risk in the global stock market. However, according to the volatility risk in each market, the EPU in Europe and China respectively influence Asian countries and European countries the most. These results may be attributable to the extremely high trade dependence among these countries because the performance of international enterprises is mainly determined by the economic policies of their trading partners.     

Industry-level determinants of the linkage between credit and stock markets

This paper examines the relationship between US credit default swaps (CDS) and stock returns on an industry-wide basis across a number of investment horizons, with particular focus on the major determinants of such a relationship. Wavelet analysis is first applied to extract the CDS–stock wavelet correlation for each US industry. Then, Bayesian Model Averaging is employed to identify the key driving factors of the industry CDS–stock wavelet correlations at short- and long-term horizons. The empirical results indicate that the wavelet correlations between the industry CDS and stock returns are primarily negative over time and across time scales. Moreover, the CDS–stock correlation at longer horizons exhibits a much more stable pattern than its counterpart at shorter time frames. The results also demonstrate that the volatility of US Treasury and stock markets, as measured by the MOVE and VIX indices, respectively, the volatility of volatility, as captured by the VVIX index, and US economic policy uncertainty, as measured by the EPU index, are the most robust determinants of the correlation between CDS and stock returns at shorter and longer horizons for most US industries. In contrast, the Fama–French systematic equity factors exhibit a practically negligible explanatory power on the CDS–stock link.     

Economic policy uncertainty and the Chinese stock market volatility: Novel evidence

In this study, we investigate the impact of global economic policy uncertainty (GEPU) on Chinese stock market volatility. More importantly, for the first time, we explore the effects of directional GEPU based on the changing directions of GEPU and Chinese economic policy uncertainty (EPU). We make several noteworthy findings. First, the in-sample estimated results show that up and down GEPU can lead to substantially high stock market volatility for China. Second, the out-of-sample estimated results support the contention that the GEPU index is helpful for predicting volatility. Moreover, compared to GEPU alone, directional GEPU can provide more useful information that can increase the forecast accuracy. Third, we empirically find that directional GEPU is more effective in predicting Chinese stock market volatility when GEPU and EPU rise in the same month.     

Does economic policy uncertainty matter for commodity market in China? Evidence from quantile regression

ABSTRACT

This paper investigates the effect of economic policy uncertainty (EPU) on China’s agricultural and metal commodity futures returns across quantiles. We address this issue using the panel quantile regression approach, which allows for a more complete analysis of various conditions in the commodity market (i.e. bearish, normal, and bullish markets). Our empirical results reveal that domestic EPU shocks have a significantly negative effect on agricultural futures returns in bearish markets and a significantly positive effect on metal futures returns in bullish markets. The impacts of both domestic and U.S. EPU shocks on commodity markets are heterogeneous across quantiles and are sector specific. Additionally, by isolating positive and negative EPU shocks, the regression and test results indicate an asymmetric response of commodity futures prices in bullish markets. Moreover, our findings indicate that the metal futures market has a higher financialisation level than the agricultural futures market. The findings can be utilized by policymakers and investors.     

Uncertainty and crude oil market volatility: new evidence

ABSTRACT

The main goal of this paper is to investigate the predictability of five economic uncertainty indices for oil price volatility in a changing world. We employ the standard predictive regression framework, several model combination approaches, as well as two prevailing model shrinkage methods to evaluate the performances of the uncertainty indices. The empirical results based on simple autoregression models including only one index suggest that global economic policy uncertainty (GEPU) and US equity market volatility (EMV) indices have significant predictive power for crude oil market volatility. In addition, the model combination approaches adopted in this paper can improve slightly the performances of individual autoregressive models. Lastly, the two model shrinkage methods, namely Elastin net and Lasso, outperform other individual AR-type model and combination models in most forecasting cases. Other empirical results based on alternative forecasting methods, estimation window sizes, high/low volatility and economic expansion/recession time periods further make sure the robustness of our major conclusions. The findings in this paper also have several important economic implications for oil investors.     

PREDICTING STOCK RETURNS AND VOLATILITY WITH INVESTOR SENTIMENT INDICES: A RECONSIDERATION USING A NONPARAMETRIC CAUSALITY‐IN‐QUANTILES TEST

Evidence of monthly stock returns predictability based on popular investor sentiment indices, namely S^BW and S^PLS as introduced by Baker and Wurgler (2006, 2007) and Huang et al. (2015) respectively are mixed. While, linear predictive models show that only S^PLS can predict excess stock returns, nonparametric models (which accounts for misspecification of the linear frameworks due to nonlinearity and regime changes) finds no evidence of predictability based on either of these two indices for not only stock returns, but also its volatility. However, in this paper, we show that when we use a more general nonparametric causality‐in‐quantiles model of Balcilar et al., (forthcoming), in fact, both S^BW and S^PLS can predict stock returns and its volatility, with S^PLS being a relatively stronger predictor of excess returns during bear and bull regimes, and S^BW being a relatively powerful predictor of volatility of excess stock returns, barring the median of the conditional distribution.     

Economic policy uncertainty and the Chinese stock market volatility: new evidence

This study investigates the impacts of the economic policy uncertainty (EPU) indexes of China and the G7 countries on Chinese stock market volatility and further constructs a new diffusion index based on these indexes using principal component analysis (PCA) to achieve enhanced predictive ability. The in-sample results indicate that the EPU indexes of China and some of the G7 countries show a significantly negative impact on future volatility. Moreover, our constructed diffusion index also has a significantly negative impact. Furthermore, the out-of-sample results show that this diffusion index exhibits a significantly higher forecast accuracy than the EPU itself and combination forecasts. Finally, various robustness checks are consistent with our main conclusions. Overall, we construct a new and useful indicator that can substantially increase forecast accuracy with respect to the Chinese stock market.     

Quantile spillovers and dependence between Bitcoin,equities and strategic commodities

This paper has two aims. We first examine the dynamic spillovers between Bitcoin and 12 developed equities, gold, and crude oil for different market conditions using a Bayesian Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with daily spot prices. Our econometric approach enables us to capture the left and right tails as well as the shoulders of the return distribution corresponding to volatility spillovers under the bear, normal, and bull market states among these financial assets. We quantify and trace the dependence and directional predictability from Bitcoin to other assets using the sample cross-quantilogram. Our key findings offer convincing evidence of time variation in the level of volatility. Spillovers between Bitcoin and other financial assets intensify during extreme global market conditions. Secondly, results from the cross-quantilogram indicate strong dependence and positive directional predictability between Bitcoin and most equities and crude oil when market returns are bullish. However, during the bearish market period, there is negative dependence and predictability from Bitcoin to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market only. This implies that Bitcoin can act as a hedge to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market. However, insignificant dependence and directional predictability from Bitcoin to the remaining assets indicate that Bitcoin may act as a safe-haven to these assets during bearish markets. Our findings hold important implications for both international investors and portfolio managers who consider Bitcoin as part of their portfolio diversification and other investment strategies.     

Is economic policy uncertainty important to forecast the realized volatility of crude oil futures?

In this research, we first investigate whether economic policy uncertainty (EPU) index can increase the HAR-RV-type models’ forecast accuracy. In addition, we explore how EPU index can be effectively used to gain larger economic values in the oil futures market. To this end, this research provides a new perspective on setting thresholds for EPU and examines whether these thresholds can help improve both the forecast accuracy and economic values. Empirical results suggest that the HAR-RV-type models including EPU can generate more accurate forecasts and economic values. The HAR-RV-type models including above-threshold EPU can further improve the forecast accuracy and yield higher economic values by setting specific thresholds for a range of horizons. The findings highlight the importance of EPU and effective way of using EPU in risk management and portfolio strategies that is crucial for investors and policymakers.     

Has the 2008 financial crisis and its aftermath changed the impact of inflation on inflation uncertainty in member states of the european monetary union?

We study to what extent the financial crisis of 2008 and its aftermath have changed the impact of inflation on inflation uncertainty in the 12 original member states of the European Monetary Union (EMU). We adopt a time‐varying coefficient regression model with stochastic volatility effects, and extract two measures of inflation uncertainty from our data, namely, (1) The conditional volatility of inflation, (2) The conditional volatility of steady‐state inflation. (1)–(2) represent short‐run and steady‐state inflation uncertainty, respectively. The time‐varying impact of inflation on inflation uncertainty is analyzed using Markov‐switching regressions, where switching between the low and high inflation uncertainty regime is determined via an unobserved Markov process. Results suggest that the 2008 financial crisis and its aftermath have changed the impact of inflation on (1) and (2) across the selected EMU member states. However, a uniform pattern cannot be detected. For some member states, we document a strong link, whereas for others, the impact of inflation on inflation uncertainty is relatively weaker.     

Oil Price Shocks and Volatility in Australian Stock Returns

This paper examines the effect of oil shocks on return and volatility in the sectors of Australian stock market and finds significant effects for most sectors. For the overall market index, an increase in oil price return significantly reduces return, and an increase in oil price return volatility significantly reduces volatility. An advantage of looking at sector returns rather than a general index of stock returns is that sectors may well differ markedly in how they respond to oil price shocks. The energy and material sectors (as expected) and the financial sector (surprisingly) are out of step (in different ways) with results for the other sectors and for the overall index. A rise in oil price increases returns in the energy and material sectors and an increase in oil price return volatility increases stock return volatility in the financial sector. Explanation for the negative (positive) association between oil return (oil return volatility) and returns (volatility of returns) in the financial sector must be based on the association via lending to and/or holdings of corporate bonds issued by firms with significant exposure to oil price fluctuations and their speculative positions in oil‐related instruments.     

Policy uncertainty and inventory behavior: Evidence from the US manufacturing sector

This study investigates the impact of economic policy uncertainty (EPU) on asymmetric inventory investment (i.e., inventory stickiness or sticky inventory management). Using a sample of 74,912 US firm-year observations over the 1984–2021 period, we observe a significantly negative relationship between EPU and asymmetric inventory investment. Our cross-sectional analyses reveal that managers' pessimistic expectations regarding future demand and higher cost of funding and maintaining capacity are the channels through which EPU affects asymmetric inventory behavior. Moreover, this negative impact is more pronounced for firms that face longer-duration uncertainty, rely heavily on government purchases, and have higher firm-specific political risk. Lastly, we find that reducing inventory stickiness leads to improved firm performance during periods of increasing policy uncertainty.     

The effect of economic policy uncertainty on bank valuations

This article examines how economic policy uncertainty (EPU) affects bank valuations. Using a large sample of banks over a long period, we find that EPU has a negative effect on bank valuations. One explanation for this result is that EPU reduces bank loan growth, and lower loan growth then leads to lower bank valuations. Consistent with this explanation, we find that the negative effect of EPU is more pronounced for banks with a higher ratio of loans to total assets.     

The interdependence of global oil price,China's stock price and economic policy uncertainty

This investigation examines the interaction among global oil price (OP), China's stock price (SP) and China's economic policy uncertainty (EPU) during the period of 2005:01 and 2017:12. A rolling window Toda‐Yamamoto causality method shows a complex time‐varying relationship. Bilateral causalities between these variables mostly accompany by sharp fluctuations in global or China's economy. Taking into account the inherent consistency of this time‐varying relation, the causal steps approach shows EPU follows a partial but time‐varying mediator process during crisis periods, which suggests EPU is one of mediator variables in this transmission mechanism. The mediator role of EPU in the transmission mechanism of OP and SP has not been paid enough attention before. Our findings provide a new direction for investors from the perspective of policy changes to deal with risks caused by OP and SP fluctuations especially when the financial market experiencing huge fluctuations.     

Modeling Price Volatility Linkages between Corn and Wheat: A Multivariate GARCH Estimation

The present paper attempts an empirical investigation on price volatility linkages between two important agricultural commodities: corn and wheat, by using a multivariate GARCH-BEKK model. Evidence of bidirectional linkages were found between corn and wheat in terms of returns and volatility. Multivariate conditional Student’s-t distribution results show a unidirectional volatility transmission from corn to wheat. Diagnostic tests reveal that the Student’s-t distribution will better model the volatility of returns when compared to the Gaussian distribution. Overall, the results show that the conditional variances and covariances between agricultural commodity market returns exhibit significant changes over time.     

Modeling the volatility of futures return in rubber and oil—A Copula-based GARCH model approach

This paper attempts to make use of a Copula-based GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) Model to find out the relationships between the volatility of rubber futures returns in the Agricultural Futures Exchange of Thailand (AFET) and other four main markets, namely, the volatility of rubber futures returns in the Singapore Commodity Exchange (SICOM), the volatility of rubber futures returns, crude oil returns, and gas oil returns in the Tokyo Commodity Exchange (TOCOM). The results illustrate that the Student-t dependence only shows better explanatory power than the Gaussian dependence structure and the persistence pertaining to the dependence structure between rubber futures returns in AFET and oil futures returns, namely, crude oil futures returns and gas oil futures returns in TOCOM. Whereas, the Gaussian dependence shows better explanatory ability between rubber futures returns in AFET and other rubber futures returns, namely, the volatility of rubber futures in SICOM and TOCOM. For the multivariate Copula model, all the parameters between AFET and other variables are significant. Based on these results, with the liberalization of agricultural trade and the withdrawal of government support to agricultural producers, there is in many countries a new need for price discovery and even physical trading mechanisms, a need that can often be met by commodity futures exchanges. Hence, this paper recommends that the government supports the hedge mutual funds that can be invested in every commodities futures exchange in the world. It can also put the funds together that will contribute farmers to invest in each commodities futures market.