Economic uncertainty,oil prices,hedging and U.S. stock returns of the airline industry

This paper examines the impacts of economic policy uncertainty and oil price shocks on stock returns of U.S. airlines using both industry and firm-level data. Our empirical approach considers a structural vector-autoregressive model with variables recognized to be important for airline returns including jet fuel price volatility. Empirical results confirm that oil price increase, economic uncertainty and jet fuel price volatility have significantly adverse effect on real stock returns of airlines both at industry and at firm level. In addition, we also find that hedging future fuel purchase has statistically positive impact on the smaller airlines. Our results suggest policy implications for practitioners, managers of airline industry and commodity investors.     

OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach

This paper provides a novel perspective to the predictive ability of OPEC meeting dates and production announcements for (Brent Crude and West Texas Intermediate) oil futures market returns and GARCH-based volatility using a nonparametric quantile-based methodology. We show a nonlinear relationship between oil futures returns and OPEC-based predictors; hence, linear Granger causality tests are misspecified and the linear model results of non-predictability are unreliable. When the quantile-causality test is implemented, we observe that the impact of OPEC variables is restricted to Brent Crude futures only (with no effect observed for the WTI market). Specifically, OPEC production announcements, and meeting dates predict only lower quantiles of the conditional distribution of Brent futures market returns. While, predictability of volatility covers the majority of the quantile distribution, barring extreme ends.     

Testing for linear and nonlinear granger causality in the stock price-volume relation: Korean evidence

In this paper, linear and nonlinear Granger causality tests are used to examine the dynamic relationship between daily Korean stock returns and trading volume. We find evidence of significant bidirectional linear and nonlinear causality between these two series. ARCH-ype models are used to examine whether the nonlinear causal relations can be explained by stock returns and volume serving as proxies for information flow in the stochastic process generating volume and stock returns respectively. After controlling for volatility persistent in both series and filtering for linear dependence, we find evidence of nonlinear bidirectional causality between stock returns and volume series. The finding of strong bidirectional stock price-volume causal relationships implies that knowledge of current trading volume improves the ability to forecast stock prices. This evidence is not supportive of the efficient market hypothesis. Another finding is that the nonlinear relationship is sensitive to institutional, organizational, and structural factors. The results of this study should be useful to regulators, practitioners and derivative market participants whose success precariously depends on the ability to forecast stock price movements.     

Efficient predictability of stock return volatility: The role of stock market implied volatility

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our in-sample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.     

基于波动率的货币与产出关系检验——来自分位数回归模型的新证据

本文使用1991年1月到2012年3月的样本数据对货币波动率和实际产出波动率之间的关系进行了检验。首先,应用GARCH模型度量货币波动率和产出波动率,进而对二者进行了Granger因果关系检验,发现我国货币供给增长率及其波动率对实际产出增长率及其波动率具有解释和预测能力。其次,使用分位数回归模型研究产出波动率在较小(低分位数)和较大(高分位数)时对货币供给量M0和M1波动率的不同反应程度。最后,提出了稳定产出增长,防止产出剧烈波动的货币政策建议。     

Economic policy uncertainty,oil price volatility and stock market returns: Evidence from a nonlinear model

This paper investigates the nonlinear relationship between economic policy uncertainty, oil price volatility and stock market returns for 25 countries by applying the panel smooth transition regression model. We find that oil price volatility has a negative effect on stock returns, and this effect increases with economic policy uncertainty. Furthermore, there is pronounced heterogeneity in responses. First, oil-exporting countries whose economies depend more on oil prices respond more strongly to oil price volatility than oil-importing countries. Second, stock returns of developing countries are more susceptible to oil price volatility than that of developed countries. Third, crisis plays a crucial role in the relation between oil price volatility and stock returns.     

Economic policy uncertainty and stock market returns: New evidence

This paper investigates the predictive performance of the Chinese economic policy uncertainty (EPU) index constructed by Davis, Liu, and Sheng (2019) in forecasting the returns of China’s stock market. Using the univariate and bivariate predictive regression model, we confirm that the monthly EPU index can significantly and negatively impact the next month’s stock returns, and has better out-of-sample predictability than the existing EPU index and several macroeconomic variables. By comparing the forecasting effect of the EPU index before and during special events with sharply increased uncertainty, we find that the EPU’s forecasting power decline rapidly when an event of sharply increased uncertainty occurs. Finally, our conclusions are consistent through a batch of robustness tests.     

Interdependence between Eastern and Western European stock markets: Evidence from intraday data

We analyze comovements among three stock markets in Central and Eastern Europe and, in addition, interdependence which may exist between Western European (DAX, CAC, UKX) and Central and Eastern European (BUX, PX-50, WIG-20) stock markets. The novelty of our paper rests mainly on the use of 5-min tick intraday price data from mid-2003 to early 2005 for stock indices and on the wide range of econometric techniques employed. We find no robust cointegration relationship for any of the stock index pairs or for any of the extended specifications. There are signs of short-term spillover effects both in terms of stock returns and stock price volatility. Granger causality tests show the presence of bidirectional causality for returns as well as volatility series. The results based on a VAR framework indicate a more limited number of short-term relationships among the stock markets.     

Time-frequency causality and dependence structure between crude oil,EPU and Chinese industry stock: Evidence from multiscale quantile perspectives

This article investigates the time-frequency causality and dependence structure of Chinese industry stock returns on crude oil shocks and China's economic policy uncertainty (EPU) across quantiles over the period from January 2001 to June 2021. We use wavelet-based decomposition series to establish a multiscale causality-in-quantiles test and a quantile-on-quantile regression approach to reveal the complicated relationships involving crude oil, EPU and stock returns. Our empirical results are as follows: First, the predictability of crude oil and EPU on industry stock returns is significantly strong under extreme market conditions. Second, the explanatory ability of EPU on industry stock returns in the long term is stronger than EPU’s ability to explain short term returns. Third, the impacts of crude oil and EPU on industry stock returns remain remarkably asymmetric across quantile levels. Finally, nonenergy-intensive industries are also affected by crude oil shocks, but less than energy-intensive industries. Overall, these empirical findings can provide implications for policymakers to stabilize stock markets and investors to hedge the potential risks from crude oil and EPU.     

Do terror attacks affect the dollar-pound exchange rate? A nonparametric causality-in-quantiles analysis

While much significant research has been done to study the effects of terror attacks on stock markets, less is known about the response of exchange rates to terror attacks. We suggest a non-parametric causality-in-quantiles test to study whether (relative) terror attacks affect exchange-rate returns and volatility. Using data on the dollar-pound exchange rate to illustrate the test, we show that terror attacks mainly affect the lower and upper quantiles of the conditional distribution of exchange-rate returns, while misspecified (due to nonlinearity and structural breaks) linear Granger causality test show no evidence of predictability. Terror attacks also affect almost all quantiles of the conditional distribution of exchange-rate volatility (except the extreme upper-end), with the significance of the effect being particularly strong for the lower quantiles. The importance of terror attacks is shown to hold also under an alternative measure of volatility and for an important emerging-market exchange rate as well.     

Causality effects in return volatility measures with random times

We provide a structural approach to identify instantaneous causality effects between durations and stock price volatility. So far, in the literature, instantaneous causality effects have either been excluded or cannot be identified separately from Granger type causality effects. By giving explicit moment conditions for observed returns over (random) duration intervals, we are able to identify an instantaneous causality effect. The documented causality effect has significant impact on inference for tick-by-tick data. We find that instantaneous volatility forecasts for, e.g., IBM stock returns must be decreased by as much as 40% when not having seen the next quote change before its (conditionally) median time. Also, instantaneous volatilities are found to be much higher than indicated by standard volatility assessment procedures using tick-by-tick data. For IBM, a naive assessment of spot volatility based on observed returns between quote changes would only account for 60% of the actual volatility. For less liquidly traded stocks at NYSE this effect is even stronger.     

Short and long run causality measures: Theory and inference

The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. Beckenback, ed., The Theory of Prediction, McGraw-Hill, New York (Chapter 8)] and Granger [Granger, C. W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37, 424–459] is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at any given horizon h$h$ as well as tests for the corresponding non-causality [Dufour, J.-M., Renault, E., 1998. Short-run and long-run causality in time series: Theory. Econometrica 66, 1099–1125; Dufour, J.-M., Pelletier, D., Renault, É., 2006. Short run and long run causality in time series: Inference, Journal of Econometrics 132 (2), 337–362]. Instead of tests for non-causality at a given horizon, we study the problem of measuring causality between two vector processes. Existing causality measures have been defined only for the horizon 1, and they fail to capture indirect causality. We propose generalizations to any horizon h$h$ of the measures introduced by Geweke [Geweke, J., 1982. Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 304–313]. Nonparametric and parametric measures of unidirectional causality and instantaneous effects are considered. On noting that the causality measures typically involve complex functions of model parameters in VAR and VARMA models, we propose a simple simulation-based method to evaluate these measures for any VARMA model. We also describe asymptotically valid nonparametric confidence intervals, based on a bootstrap technique. Finally, the proposed measures are applied to study causality relations at different horizons between macroeconomic, monetary and financial variables in the US.     

Oil price uncertainty and movements in the US government bond risk premia

In this paper, we analyze the predictability of the movements of bond premia of US Treasury due to oil price uncertainty over the monthly period 1953:06 to 2016:12. For our purpose, we use a higher order nonparametric causality-in-quantiles framework, which in turn, allows us to test for predictability over the entire conditional distribution of not only bond returns, but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and structural breaks, which we show to exist in our data. We find that oil uncertainty not only predicts (increases) US bond returns, but also its volatility, with the effect on the latter being stronger. In addition, oil uncertainty tends to have a stronger impact on the shortest and longest maturities (2- and 5-year), and relatively weaker impact on bonds with medium-term (3- and 4-year) maturities. Our results are robust to alternative measures of oil market uncertainty and bond market volatility.     

Multi-scale interactions between economic policy uncertainty and oil prices in time-frequency domains

Dynamic interactions between policy uncertainty and economic activity, including oil prices, have attracted increasing amounts of scholarly interest, but few studies have considered the inherent feature that the entire market is composed of different stakeholders operating in different time horizons. To fill this gap and address this issue, this paper proposes a multi-scale correlation framework. Specifically, we use the wavelet coherence method and scale-by-scale linear Granger causality tests to explore the co-movement and causality of pairs of economic policy uncertainty indices of G7 countries, China, Brazil, and Russia and West Texas Intermediate (WTI) oil prices. Our results show that the interaction between economic policy uncertainty and oil prices in the short-term is weak but gradually strengthens towards the long-term, especially when significant historical political or financial events occurred. Moreover, a consistent conclusion is that the interaction is negative in the medium-term, while it is positive in the long-term. Further, Granger causality tests at different time-scales show that no Granger causality from economic policy uncertainty to oil prices exists in the short-term for all sample countries, except the US, while there is a strong unidirectional or bidirectional Granger causality for all researched countries in the medium- and the long-term.     

Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines dimensionality reduction, regime-switching models, and forecast combination to predict excess returns on the S&P 500. First, we aggregate the weekly information of 146 popular macroeconomic and financial variables using different principal component analysis techniques. Second, we estimate Markov-switching models with time-varying transition probabilities using the principal components as predictors. Third, we pool the models in forecast clusters to hedge against model risk and to evaluate the usefulness of different specifications. Our weekly forecasts respond to regime changes in a timely manner to participate in recoveries or to prevent losses. This is also reflected in an improvement of risk-adjusted performance measures as compared to several benchmarks. However, when considering stock market returns, our forecasts do not outperform common benchmarks. Nevertheless, they do add statistical and, in particular, economic value during recessions or in declining markets.     

Cross-sectional implications of dynamic asset pricing with stochastic volatility and ambiguity aversion

Building on recent research that highlights the importance of macroeconomic volatility and ambiguity aversion in explaining the dynamics of stock returns, in this paper we propose a dynamic asset pricing model that simultaneously accounts for stochastic macroeconomic volatility and ambiguity, assuming that investors deal with uncertainty about the mechanics of macroeconomic fluctuations using first-release consumption and revisions to aggregate consumption on vintage data. Our results show that the proposed model captures a large fraction of the cross-sectional variation of excess returns for a wide range of market anomaly portfolios. Furthermore, while the price of risk for ambiguity is positive and significant for the vast majority of assets under study, macroeconomic volatility yields ambiguous outcomes, although it significantly increases the explanatory power of the model for specific assets. Our results suggest that macroeconomic volatility and ambiguity complement each other in explaining the cross-sectional behavior of stock returns.     

A new statistic and practical guidelines for nonparametric Granger causality testing

In this paper we introduce a new nonparametric test for Granger non-causality which avoids the over-rejection observed in the frequently used test proposed by Hiemstra and Jones [1994. Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance 49, 1639–1664]. After illustrating the problem by showing that rejection probabilities under the null hypothesis may tend to one as the sample size increases, we study the reason behind this phenomenon analytically. It turns out that the Hiemstra–Jones test for the null of Granger non-causality, which can be rephrased in terms of conditional independence of two vectors X and Z given a third vector Y, is sensitive to variations in the conditional distributions of X and Z that may be present under the null. To overcome this problem we replace the global test statistic by an average of local conditional dependence measures. By letting the bandwidth tend to zero at appropriate rates, the variations in the conditional distributions are accounted for automatically. Based on asymptotic theory we formulate practical guidelines for choosing the bandwidth depending on the sample size. We conclude with an application to historical returns and trading volumes of the Standard and Poor's index which indicates that the evidence for volume Granger-causing returns is weaker than suggested by the Hiemstra–Jones test.     

Impact of economic growth volatility on income inequality: ASEAN experience

The study attempts to examine the symmetric and the asymmetric impact of volatility of economic growth on the inequality of income in the major ASEAN economies over the period 1980–2015. Financial development, trade openness as a proxy of globalization, inflation, human capital formation, and fiscal policy are utilized as major control variables. The paper tries to explore the causal association between inequality of income distribution and economic growth volatility, exploring simultaneously the long-run association and the short-run dynamics in the time series structure. The study applied Clemente–Montanes–Reyes unit root test to identify the structural break in the time series. Further, the cointegrating relationship of the time series observations was explored by applying the ARDL (linear) bounds test approach along with the nonlinear ARDL for making fruitful comparisons in the long-run relationship among the variables. The countries chosen are Malaysia, Indonesia, Thailand, Singapore and The Philippines. The empirical findings strongly suggest a long-run cointegrating relationship between income inequality and growth volatility with a positive and statistically significant impact. Also, the causality analysis was explored using the Toda and Yamamoto (1995) method of Granger causality. The causality test shows that there exists bidirectional causality from inequality transmission to economic growth volatility. The implications that are developed from this study helps us to understand the various policy reforms in the ASEAN region, that are more transparent and can make these economies less susceptible to risks.     

Sensitivity of US equity returns to economic policy uncertainty and investor sentiments

This paper examines the sensitivity of major US sectoral returns to economic policy uncertainty and investor sentiments. Our analysis is based on weekly frequency and ranges from January 1995 to December 2015 covering a span of 20 years. Considering existing, however limited evidence of non-linear structure exhibited by investor sentiments and economic policy uncertainty and on the basis of our non-linear diagnostics, we use novel technique of non-parametric causality in quantiles approach proposed by Balcilar, Gupta, and Pierdzioch (2016). Our results highlight that economic policy uncertainty and investor sentiments act as driving factors for US sectoral returns. The nature of relationship is reported as asymmetrical for stock returns and symmetrical for variance of returns with an exception of Healthcare sector for economic policy uncertainty and bullish market sentiments. Our study carries implications for portfolio diversification and policy makers for forecasting market efficiency and economic trends.     

Stock returns and economic activity in mature and emerging markets

This paper empirically investigates the dynamic interdependencies between stock returns and economic activity in mature and emerging markets. The existence, kind and strength of potential uni-directional and/or bi-directional relations are examined, running from stock returns to future economic activity and/or from economic activity to future stock returns. A bivariate VAR(12) model is applied and Granger causality tests are performed. Monthly data covering the January 1991–December 2006 period are used. The existence of an empirical relationship, with forecasting ability, between stock returns and future economic activity is confirmed. The results are strongly differentiated between mature and emerging markets.