Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach

In this study, we examine the connection between geopolitical risk (GPR) and stock market volatility in emerging economies. Our motivation for this study is premised on the need to assess both the predictability and the associated economic gains in relation to the subject in order to offer more useful insights to investors and practitioners. To the best of our knowledge, this is the first study that jointly considers these objectives. Consequently, we employ the GARCH-MIDAS framework which accommodates mixed data frequencies thereby circumventing information loss or any associated bias. We find that emerging stock market volatility responds more positively to geopolitical risks although the act-related GPR index offers better out-of-sample forecasts than the threat-related GPR. We also find that accounting for global economic factors in the predictability analysis is crucial for robust outcomes. Finally, we provide some utility gains of including GPR in the predictive model of stock market volatility while also highlighting some useful implications of our findings for investment and policy decisions.     

Which implied volatility provides the best measure of future volatility?

The volatility smile/skew phenomenon makes it unclear which implied volatility provides the best measure of the market volatility expectation over the remaining life of the option. Due to the high liquidity of at-the-money option and the low sensitivity of its implied volatility to the price error, the at-the-money implied volatility is often considered a good measure of future volatility. In this paper, we raise the question: is at-the-money implied volatility the best we can do? We provide in this paper an analytical rationale that the implied volatility from option with highest vega outperforms the at-the-money implied volatility in terms of forecasting ability, especially for long forecasting horizons. Our empirical findings are consistent with our theoretical argument.     

Forecasting expected shortfall: Should we use a multivariate model for stock market factors?

Is univariate or multivariate modeling more effective when forecasting the market risk of stock portfolios? We examine this question in the context of forecasting the one-week-ahead expected shortfall of a stock portfolio based on its exposure to the Fama–French and momentum factors. Applying extensive tests and comparisons, we find that in most cases there are no statistically significant differences between the forecasting accuracy of the two approaches. This result suggests that univariate models, which are more parsimonious and simpler to implement than multivariate factor-based models, can be used to forecast the downside risk of equity portfolios without losses in precision.     

Forecasting exchange rate volatility using high-frequency data: Is the euro different?

We assess the performances of alternative procedures for forecasting the daily volatility of the euro’s bilateral exchange rates using 15 min data. We use realized volatility and traditional time series volatility models. Our results indicate that using high-frequency data and considering their long memory dimension enhances the performance of volatility forecasts significantly. We find that the intraday FIGARCH model and the ARFIMA model outperform other traditional models for all exchange rate series.     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

Herding behavior,market sentiment and volatility: Will the bubble resume?

This paper aims to investigate herding behavior and its impact on volatility under uncertainty. We apply a cross-sectional absolute deviation approach as well as Quantile Regression methods to capture the herding behavior in daily and monthly frequencies in US markets over several time-periods including the global financial crisis. In a novel attempt we modify the empirical CSAD herding modeling by introducing implied volatility as a measure of agent risk expectations. Our findings indicate that herding tends to be intense under extreme market conditions, as depicted in the upper high quantile range of the conditional distribution of returns. During crisis periods herding is observed at the beginning of the crisis and becomes insignificant towards the end. The US market herding behavior exhibits time-varying dynamic trading patterns that can be attributed e.g., to overconfidence or excessive “flight to quality” features, mostly observed in the aftermath of the global financial crisis. Moreover, implied volatility reveals asymmetric patterns and plays a key role in enforcing irrational behavior.     

What drives the liquidity premium in the Chinese stock market?

This paper examines the dynamics of the liquidity premium in the Chinese stock market by adopting a multivariate decomposition approach to measure the individual contributions of various driving forces of the premium (such as firm size, idiosyncratic volatility, and market liquidity betas). By employing a wide range of liquidity measures, we show that liquidity premium is generally significant in the Chinese stock market. Furthermore, this premium is increasing in recent years starting from 2011; this observation is different from the United States market, in which the premium has declined over the years. Moreover, the multivariate decomposition approach highlights several asset pricing factors as the main driving forces of the premium. Based on the Amihud liquidity measure, the decomposition approach indicates that the size factor contributes 45–65% to the liquidity premium. However, the measure based on turnover suggests that idiosyncratic volatility accounts for at least 60% of the liquidity premium. In contrast, the global market liquidity beta does not significantly contribute to the premium. However, there is some evidence that the local market liquidity beta has become more significant in its impact on the premium during the period from 2011 to 2015. Our results imply that the findings on the liquidity premium in the Chinese stock market could be sensitive to the liquidity measure used and period of analysis.     

Jump risk and cross section of stock returns: evidence from China’s stock market

Various studies have confirmed the existence of jumps in different financial markets. However, there is sparse theoretical or empirical effort to examine the dynamic relation between jump risk and cross-sectional expected stock returns. We follow a stylized SDF-based diffusion-jump model to examine its testable implications about the relation between cross-section expected excess returns and variations in jump intensities across stocks. The zero-cost portfolio, exploiting the return spreads between the top and bottom decile portfolios formed on jump intensity, could earn an annualized return as high as 24% with an annualized Sharpe ratio of 1.67. A Fama-MacBeth test shows that stock excess returns monotonically decrease in jump intensity even after controlling for other common risk factors.     

Can crude oil drive the co-movement in the international stock market? Evidence from partial wavelet coherence analysis

In this paper, we employ partial- and multiple-wavelet coherence analyses to examine co-movement between international stock markets by considering the influence of crude oil in a time domain perspective. Overall, we find that crude oil is a major factor driving co-movement between international stock markets in the median and long term. However, when considering the oil-importing and oil-exporting countries differently, we still find that crude oil is a driver for interdependence between oil-importing and oil-exporting countries. In contrast, the crude oil has relative lower impact on the co-movement in oil-importing or in oil-exporting countries, which indicates its co-movement is caused by other factors. In addition, Gulf Cooperation Council stock market may lead the stock markets of oil-importing countries in the long term. Our empirical results provide meaningful information for investors and policymakers.     

Dynamic interaction of risk–return trade-offs between oil market and China’s stock market: An analysis from the risk preferences perspective

The risk–return trade-off refers to the compensation required by investors for bearing risks, which can be viewed as the risk preference of investors in a market. The current study investigates the dynamic interdependence of risk–return trade-offs between China’s stock market and the crude oil market from the perspective of risk preference of investors, which is designed to explore the transmission process of investors’ risk preference in both markets. Specifically, this study applies the time-varying parameter GARCH-M model, namely TVP-GARCH-M model, to characterize the time-dependent risk–return trade-offs (investors’ risk preferences) in the crude oil and China’s stock markets, then examines their relationship through Granger causality tests. Results show that a variation in risk preferences of the oil market investors can dramatically cause a variation in risk preferences of the Chinese stock market investors, while the risk preference of investors in the Chinese stock market does not lead to that in the crude oil market, which is in accordance with expectations. The dynamic effect of investors’ risk appetite in the crude oil market is further examined by the TVP-VAR model. The findings of this work suggest that there generally exists a positive impact of investors’ risk preference in the oil market and that the effect is time-varying to a greater degree during the short and medium term. Moreover, responses of the Chinese stock market investors’ risk preference were more significant during the 2008 financial crisis. Additionally, the empirical results remain robust when applying alternative crude oil prices and China’s stock prices.     

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.     

Risk reporting and stock return in the UK: Does market competition Matter?

This study examines the extent to which market competition influences risk reporting practice. It also explores how market competition affects the usefulness of risk reporting. The automated textual analysis measures the level of risk reporting [how much to report] and its tone [how it is reported] of UK FTSE 350 firms. The abnormal stock return is used as a proxy for the usefulness of risk reporting. In contrast to the proprietary cost hypothesis, our results indicate that the level of risk reporting is a positive function of market competition. Besides, UK firms are likely to disseminate more (less) negative (positive) news about their risks when market competition increases. However, after examining the informativeness of this reporting, we provide evidence that the level of reported risk information does not significantly enhance the abnormal stock returns of UK firms. Nevertheless, the tone of the reported risks carries incremental information indicative of a firm’s abnormal stock return, especially when market competition decreases. The findings suggest that firms are likely to alleviate their proprietary costs by framing their reporting of risk information in a way that deters potential competitors from entering their market and that market competition diminishes the perceived informativeness of such reporting. The results provide implications for investors as they should not acknowledge the disclosure of higher risk information when asking for more corporate transparency, as it lacks informativeness. Besides, policymakers may impose extra compulsory requirements on the UK firms to avoid reporting overly optimistic risk news to protect investors and avoid the adverse effects of this reporting.     

Does weather impact the stock market? Empirical evidence in Taiwan

Psychological evidence suggests close relationships between weather and mood. Individuals feel in a more positive frame of mind on sunny than cloudy days. This study applies GJR–GARCH to examine the relationship among weather, stock returns and risk in Taiwan from 2001 to 2007. The empirical results indicate that precipitation does not significantly influence stock return and risk; likewise, sunshine hours and temperature insignificantly influence stock return, but do significant impact stock risk. These findings demonstrate that weather effect really exist in stock market, and can help investors in making innovative investment and management decisions.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Does the stock market really cause unemployment? A cross-country analysis

This study examines the relationship between the stock market and unemployment in 30 advanced and 11 developing and emerging countries. The results show that the unemployment rate and stock prices are cointegrated in all country groups; further, the causality between stock prices and unemployment appears in all country groups. Specifically, I found a particularly strong and one-way causal direction from stock prices to the unemployment rate in G7 countries. There is a strong bilateral causal relationship between stock prices and unemployment for other advanced countries. However, in the 11 developing and emerging countries, the causality test results indicate a strong Granger causality from unemployment to stock prices. The results for developing and emerging countries suggest that the unemployment rate can help forecast stock prices, but not vice versa. These findings complement existing studies and deliver useful implications for investors and policymakers, and suggest some new lines for future research.     

The role of the seller’s stock performance in the market reaction to divestiture announcements

Divestitures have the potential to create shareholder value. However, the extent of the market reaction should depend on the likelihood of finding more valuable uses for the divested assets or the ability on the part of the seller to eliminate negative synergies. We hypothesize that strong performers have less scope to achieve substantial improvements compared to poorly performing firms. Using the seller’s stock return in excess of the market return in the 1-year and 2-year periods preceding the divestiture announcement to expose the divesting firm’s inefficient use of its assets, we show that the market reaction to divestiture announcements is significantly higher for underperforming firms. The difference in abnormal returns can be as high as 4 %. In contrast, none of the accounting-based variables that have been used in previous studies are found to be significantly related to the announcement returns. These results suggest that the firm’s stock performance is a more useful indicator of the wealth effect associated with divestitures.     

Calibrating the wealth effects of decoupled payments: Does decreasing absolute risk aversion matter?

Arrow’s hypotheses regarding the relationship between wealth and risk aversion measures have formed the basis for a large body of empirical research and theory. For example, many have suggested that decoupled farm subsidy payments may increase production as they decrease farmers’ risk aversion. This paper develops a new calibration technique designed to measure the minimum change in concavity of a utility of wealth function necessary to describe a particular change in production behavior for some discrete change in wealth. I conclude that measurable changes in production levels should not be produced by changing levels of risk aversion except when wealth changes are a substantial portion of wealth. This tool draws into question the usefulness of Arrow’s hypotheses in many current applications.     

Connectedness and risk spillovers in China’s stock market: A sectoral analysis

This paper shows how sectors in the Chinese stock market are connected and investigates risk spillovers across these sectors. Using graph theory and a recently developed time series technique, we are able to identify the systemically important sector in the market and the patterns of risk spillovers across sectors over time. Unlike standard econometric modeling, graph theory enables us to approach this question in a more reader-friendly way. The empirical results show that Industrial sector plays a central role and should thus be considered the systemically most important sector in the Chinese stock market. The spillover structure is found to be time-varying. While Industrial sector dominates the system for most of the time, other sectors such as Consumer Discretionary sector also occasionally appear as the central sector. Our empirical results also indicate that the simple correlation-based approach can produce equally useful information as more advanced econometric models.     

Forecasting commodity prices out-of-sample: Can technical indicators help?

Economic variables are often used for forecasting commodity prices, but technical indicators have received much less attention in the literature. This paper demonstrates the predictability of commodity price changes using many technical indicators. Technical indicators are stronger predictors than economic indicators, and their forecasting performances are not affected by the problems of data mining or time changes. An investor with mean–variance preference receives utility gains of between 104.4 and 185.5 basis points from using technical indicators. Further analysis shows that technical indicators also perform better than economic variables for forecasting the density of commodity price changes.     

News and noise: do investors react to stock split announcements differently during periods of high and low market volatility?

In this paper, we find evidence that stock split announcements have a greater wealth effect when market volatility, as measured by the VIX index, is low. This effect is driven primarily by small firms. These results support the hypothesis that when market volatility is high, signals sent by small firms are more likely to be obscured by noise than when market volatility is low.