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.     

Shifts in volatility driven by large stock market shocks

This paper presents an extension of the stochastic volatility model which allows for level shifts in volatility of stock market returns, known as structural breaks. These shifts are endogenously driven by large return shocks (innovations), reflecting large pieces of market news. These shocks are identified from the data as being bigger in absolute terms than the values of two threshold parameters of the model: one for the negative shocks and one for the positive shocks. The model can be employed to investigate different sources of stock market volatility shifts driven by market news, without relying on exogenous information. In addition to this, it has a number of interesting features which enable us to study the effects of large return shocks on future levels of market volatility. The above properties of the model are shown based on a study for the US stock market volatility.     

The role of oil futures intraday information on predicting US stock market volatility

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.     

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.     

Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.     

Dynamic relationships between industry returns and stock market returns

Different from prior studies which concentrate on the unidirectional impact of industry leading, this study examines the bi-directional dynamical causal relation between industry returns and stock market returns by considering multiple structural breaks for ten major eastern and southern Asia countries. Our results show that finance and consumer service industry returns have significant power in explaining the movements of market returns. Further, we apply logit regressions to explore the determinants of the leading hypotheses and find exchange rate and interest rate are important in explaining the industry–market nexus. In a developed market the industry and the market have feedback relations, but in a highly controlled economy the influence from the stock market dominates.     

Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching

The purpose of this paper is to investigate the role of regime switching in the prediction of the Chinese stock market volatility with international market volatilities. Our work is based on the heterogeneous autoregressive (HAR) model and we further extend this simple benchmark model by incorporating an individual volatility measure from 27 international stock markets. The in-sample estimation results show that the transition probabilities are significant and the high volatility regime exhibits substantially higher volatility level than the low volatility regime. The out-of-sample forecasting results based on the Diebold-Mariano (DM) test suggest that the regime switching models consistently outperform their original counterparts with respect to not only the HAR and its extended models but also the five used combination approaches. In addition to point accuracy, the regime switching models also exhibit substantially higher directional accuracy. Furthermore, compared to time-varying parameter, Markov regime switching is found to be a more efficient way to process the volatility information in the changing world. Our results are also robust to alternative evaluation methods, various loss functions, alternative volatility estimators, various sample periods, and various settings of Markov regime switching. Finally, we provide an extension of forecasting aggregate market volatility on monthly frequency and observe mixed results.     

Fiscal and monetary policies in complex evolving economies

What is the most appropriate combination of fiscal and monetary policies in economies subject to banking crises and deep recessions? We study this issue using an agent-based model that is able to reproduce a wide array of macro- and micro-empirical regularities. Simulation results suggest that policy mixes associating unconstrained, counter-cyclical fiscal policy and monetary policy targeting employment is required to stabilise the economy. We also show that “discipline-guided” fiscal rules can be self-defeating, as they depress the economy without improving public finances. Finally, we find that the effects of monetary and fiscal policies become sharper as the level of income inequality increases.     

The effects of foreign exchange and monetary policies in Russia

This paper examines the effects of Russian foreign exchange and monetary policies under conditions of abundant natural resources during the period 1999–2011 using structural VAR models. The results suggest that monetary policy shocks, which are identified as money supply disturbances, have a persistent effect on real output, and more than half of the volatility in real output can be explained by changes in the money supply. Furthermore, the analysis reveals that stock prices are a more significant transmission channel of monetary policy than bank loans.     

The impact of sentiment and attention measures on stock market volatility

We analyze the impact of sentiment and attention variables on the stock market volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. We apply a state-of-the-art sentiment classification technique in order to investigate the question of whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify the most relevant variables to be investors’ attention, as measured by the number of Google searches on financial keywords (e.g. “financial market” and “stock market”), and the daily volume of company-specific short messages posted on StockTwits. In addition, our study shows that attention and sentiment variables are able to improve volatility forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view.     

SFAS 133 and income smoothing via discretionary accruals: The role of hedge effectiveness and market volatility

This study investigates whether the Statement of Financial Accounting Standard No. 133 (SFAS 133) influences firms’ income smoothing via discretionary accruals decisions. Moreover, we investigate whether the level of hedge effectiveness and market volatility affects the impact of SFAS 133 on firms’ income smoothing via discretionary accruals decisions. Consistent with our predictions, we find a significant increase in income smoothing via discretionary accruals activity after the adoption of SFAS 133. We also find that income smoothing via discretionary accruals after the adoption of SFAS 133 increases with the level of hedge ineffectiveness. By contrast, we find that perfect hedgers do not engage in more income smoothing via discretionary accruals after the adoption of SFAS 133. Finally, we find that the higher the market volatility is the larger the income smoothing is via discretionary accruals after the adoption of SFAS 133. This implies that higher market volatility makes it more difficult for firms to meet hedge accounting requirements, thereby increasing unmanaged earnings volatility and income smoothing. Prior studies suggest that regulators are expressing concern about the effect of earnings management on the quality of reported earnings and the functioning of capital markets (e.g., Barton, 2001 ). In this regard, our findings imply that accounting standard setters should take into account the trade‐off between transparency and income smoothing.     

Time-varying impact of monetary policy shocks on US stock returns: The role of investor sentiment

This paper investigates how monetary policy shock affects the stock market of the United States (US) conditional on states of investor sentiment. In this regard, we use a recently developed estimator that uses high-frequency surprises as a proxy for the structural monetary policy shocks, which in turn is achieved by integrating the current short-term rate surprises, which are least affected by an information effect, into a vector autoregressive (VAR) model as an exogenous variable. When allowing for time-varying model parameters, we find that, compared to the low investor sentiment regime, the negative reaction of stock returns to contractionary monetary policy shocks is stronger in the state associated with relatively higher investor sentiment. Our results are robust to alternative sample period (which excludes the zero lower bound) and model specification and also have important implications for academicians, investors, and policymakers.     

Predicting stock market crises using daily stock market valuation and investor sentiment indicators

The purpose of this paper is to develop a daily early warning system for stock market crises using daily stock market valuation and investor sentiment indicators. To achieve this goal, we use principal components analysis to propose a comprehensive index of daily market indicators that reflects stock market valuation and investor sentiment. Based on the comprehensive index, we employ a logit model with Ensemble Empirical Mode Decomposition to develop a daily early warning system for stock market crises. Finally, we apply the proposed system to the early warning for stock market crises in China. The in-sample forecasting results show that investor sentiment and the forecast horizon by Ensemble Empirical Mode Decomposition improve the forecasting performance of conventional early warning systems. The out-of-sample forecasting results indicate that the proposed warning system still has a good performance.     

Lottery mindset,mispricing and idiosyncratic volatility puzzle: Evidence from the Chinese stock market

This study investigates the MAX effect regarding lottery mindset in the Chinese stock market. The MAX effect significantly affects stock returns through quintile portfolio and cross-sectional regression analyses. The most-overpriced stock groups, as categorized by mispricing index, show more support for the MAX effect. However, the idiosyncratic volatility (IVOL) effect continues regardless of consideration for the MAX effect, indicating that the MAX effect is not a source of the IVOL effect. Our results suggest that the MAX effect, which is highly relevant for overpriced stocks, might have information for determining stock price, and appears to be independent from information of the IVOL effect in the Chinese stock market.     

The effects of employee stock ownership on stock liquidity: Evidence from the Korean market

This study investigates the relationship between the level of employee stock ownership (ESO) and stock liquidity. Using Korean ESO data, we find that ESO is positively associated with various liquidity measures. Stock-owning employees tend to mitigate information asymmetry to increase their benefits from the transparent market. We also find stronger effects when the firm is not an affiliate of a chaebol family group, and is less monitored by financial analysts, foreign investors, and outside directors. Furthermore, we employ various robustness tests to mitigate potential endogeneity concerns.     

Price gap anomaly in the US stock market: The whole story

This paper analyses the price gap anomaly in the US stock market (comprised of the DJI, S&P 500 and NASDAQ) covering the period 1928 to 2018. This paper aims to investigate whether or not price gaps create market inefficiencies. Price gaps occur when the current day’s opening price is different from the previous day’s closing price due orders placed before the opening of the market. Several hypotheses are tested using various statistical tests (Student’s t-test, ANOVA, Mann-Whitney test), regression analysis, and special methods, that is, the modified cumulative returns and the trading simulation approaches. We find strong evidence in favour of abnormal price movements after price gaps. We observe that during a gap day prices tend to change in the direction of the gap. A trading strategy based on this anomaly was efficient in that its results were not random, indicating that this market was not efficient. The momentum effect was found to be temporary and no evidence of seasonality in price gaps was found. Lastly, our results were also contrary to the myth that price gaps tend to get filled.     

Misvaluation comovement,market efficiency and the cross-section of stock returns: Evidence from China

In this study, we examine the relation between stock misvaluation and expected returns in China's A-share market. We measure individual stocks’ misvaluation based on their pricing deviation from fundamental values, following Rhodes-Kropf et al. (2005. J. Finan. Econ. 77 (3), 561) and Chang et al. (2013. J. Bank. Finance, forthcoming), and find that the measure has strong and robust return predictive power in the Chinese market. We further form a misvaluation factor and find that misvaluation comovement and systematic misvaluation exist in the Chinese market. A comparison of our results with those of Chang et al. (2013. J. Bank. Finance, forthcoming) reveals that the misvaluation effect is much stronger in the Chinese market than in the U.S market. This evidence is consistent with the notion that the Chinese market is much less efficient than the U.S. market. Finally, we show that the return predictive power of misvaluation has weakened since China launched its split-share structure reform in 2005, which could result from the fact that the reform helps to promote market efficiency.     

Foreign exchange market volatility in EU accession countries in the run-up to Euro adoption: weathering uncharted waters

The paper analyzes foreign exchange market volatility in four Central European EU accession countries in 2001–2003. By using a Markov regime-switching model, it identifies two regimes representing high- and low-volatility periods. The estimation results show not only that volatilities are different between the two regimes, but also that some of the cross-correlations differ. Notably, cross-correlations increase substantially for two pairs of currencies (the Hungarian forint–Polish zloty and the Czech koruna–Slovak koruna) in the high-volatility period. The paper concludes by discussing the policy implications of these findings.     

The outbreak of COVID-19 and stock market liquidity: Evidence from emerging and developed equity markets

The outbreak of the novel corona virus has heightened concerns surrounding the adverse financial effects of the outbreak on stock market liquidity and economic policies. This paper contributes to the emerging strand of studies examining the adverse effects of the virus on varied aspect of global markets. The paper examines the causality and co-movements between COVID-19 and the aggregate stock market liquidity of China, Australia and the G7 countries (Canada, France, Italy, Japan, Germany, the UK and the US), using daily three liquidity proxies (Amihud, Spread and Traded Value) over the period December 2019 to July 2020. Our empirical analysis encompasses wavelet coherence and phase-differences as well as a linear Granger causality test. Linear causality test results suggest that a causal relationship exists between the number of cases of COVID 19 infections and stock market liquidity. To quantitatively examine the degree of causality between COVID-19 outbreak and stock market liquidity, we employ the continuous wavelet coherence approach with results revealing the unprecedented impact of COVID-19 on stock market liquidity during the low frequency bands for countries that were hard hit with the COVID-19 outbreak, i.e., Italy, Germany, France, the UK and the US. Further, evidence shows that there is a heterogeneous lead-lag nexus across scales for the entire period of the study.