Empirical analysis of long memory,leverage, and distribution effects for stock market risk estimates

In this study, eight generalized autoregressive conditional heteroskedasticity (GARCH) types of variance specifications and two return distribution settings, the normal and skewed generalized Student's t (SGT) of Theodossiou (1998), totaling nine GARCH-based models, are utilized to forecast the volatility of six stock indices, and then both the out-of-sample-period value-at-risk (VaR) and the expected shortfall (ES) are estimated following the rolling window approach. Moreover, the in-sample VaR is estimated for both the global financial crisis (GFC) period and the non-GFC period. Subsequently, through several accuracy measures, nine models are evaluated in order to explore the influence of long memory, leverage, and distribution effects on the performance of VaR and ES forecasts. As shown by the empirical results of the nine models, the long memory, leverage, and distribution effects subsist in the stock markets. Moreover, regarding the out-of-sample VaR forecasts, long memory is the most important effect, followed by the leverage effect for the low level, whereas the distribution effect is crucial for the high level. As for the three VaR approaches, weighted historical simulation achieves the best VaR forecasting performance, followed by filtered historical simulation, whereas the parametric approach has the worst VaR forecasting performance for all the levels. Furthermore, VaR models underestimate the true risk, whereas ES models overestimate the true risk, indicating that the ES risk measure is more conservative than the VaR risk measure. Additionally, based on back-testing, the VaR provides a better risk forecast than the ES since the ES highly overestimates the true risk. Notably, long memory is important for the ES estimate, whereas both the long memory and the leverage effect are crucial for the VaR estimate. Finally, via in-sample VaR forecasts in regard to the low level, it is found that long memory is important for the non-GFC period, whereas the distribution effect is crucial for the GFC period. On the other hand, with regard to the high level, the distribution effect is crucial for both the non-GFC and the GFC period. These results seem to be consistent with those found in the out-of-sample VaR forecasts. In accordance with these results, several important policy implications are proposed in this study.     

Empirical modeling of high-income and emerging stock and Forex market return volatility using Markov-switching GARCH models

Using weekly data for stock and Forex market returns, a set of MS-GARCH models is estimated for a group of high-income (HI) countries and emerging market economies (EMEs) using algorithms proposed by Augustyniak (2014) and Ardia et al. (2018, 2019a,b), allowing for a variety of conditional variance and distribution specifications. The main results are: (i) the models selected using Ardia et al. (2018) have a better fit than those estimated by Augustyniak (2014), contain skewed distributions, and often require that the main coefficients be different in each regime; (ii) in Latam Forex markets, estimates of the heavy-tail parameter are smaller than in HI Forex and all stock markets; (iii) the persistence of the high-volatility regime is considerable and more evident in stock markets (especially in Latam EMEs); (iv) in (HI and Latam) stock markets, a single-regime GJR model (leverage effects) with skewed distributions is selected; but when using MS models, virtually no MS-GJR models are selected. However, this does not happen in Forex markets, where leverage effects are not found either in single-regime or MS-GARCH models.     

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.     

Mean reversion and long memory in African stock market prices

We examine the behavior of stock market prices in several African countries by means of fractionally integrated techniques. In doing so, we can test for mean reversion in these markets. Our results can be summarized as follows: we cannot find evidence of mean reversion in any single market, and evidence of long memory returns (i.e., orders of integration above 1 in the logged stock prices) is obtained in the cases of Egypt and Nigeria, and, in a lesser extent in Tunisia, Morocco and Kenya. Permitting the existence of a structural change, the break dates take place in the earlier 2000s in the majority of the cases, and evidence of mean reversion seems to have taken place in the periods before the breaks in most of the countries. If we focus on the absolute and squared returns, evidence of long memory is obtained in Nigeria and Egypt. Thus, for these two countries, a long memory model incorporating positive fractional degrees of integration in both the level and the volatility process should be considered.     

The VIX,the variance premium and stock market volatility

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while the conditional stock market variance predicts economic activity and has a relatively higher predictive power for financial instability than does the variance premium.     

Economic indicators and stock market volatility in an emerging economy

By analyzing the daily realized volatility series calculated from intraday stock price observations, this study examines the direct causality between one-day-ahead aggregate stock market volatility and several economic and financial indicators in the Korean market, a leading emerging market. Using the predictive regression and superior predictive ability tests, we find that the model-free implied volatility index (VKOSPI) and stock market indicators both lead the daily market volatility. However, daily economic indicators provide no predictive information beyond that contained in historical volatility. Though in-sample causality does not guarantee a better out-of-sample forecasting performance, the VKOSPI and combinations of predictors exhibit significant predictive ability regardless of the time period. Our study verifies the information role of the VKOSPI as an indicator of daily market risk.     

Non-linear volatility dynamics and risk management of precious metals

In this paper, we investigate the value-at-risk predictions of four major precious metals (gold, silver, platinum, and palladium) with non-linear long memory volatility models, namely FIGARCH, FIAPARCH and HYGARCH, under normal and Student-t innovations’ distributions. For these analyses, we consider both long and short trading positions. Overall, our results reveal that long memory volatility models under Student-t distribution perform well in forecasting a one-day-ahead VaR for both long and short positions. In addition, we find that FIAPARCH model with Student-t distribution, which jointly captures long memory and asymmetry, as well as fat-tails, outperforms other models in VaR forecasting. Our results have potential implications for portfolio managers, producers, and policy makers.     

Country risk and stock market volatility,predictability, and diversification in the Middle East and Africa

With globalization, an understanding of country risk (political risk (PR), financial risk (FR), and economic risk (ER)) and its impact on stock market return volatility and predictability is important for evaluating direct investment and country selection decisions in globally and regionally diversified portfolios. This paper examines these issues in the context of the Middle East and Africa (MEAF) and analyzes 10 stock markets in the region over the period 1984–1999. After examining volatility and predictability, this paper explains how portfolios of stocks can be formed from these countries in order to achieve mean–variance efficient portfolios. This paper generally finds that country political, financial and economic risks significantly determine stock volatility and predictability. The diversification exercise shows that an international investor can still benefit by diversifying into the stock markets of Middle East and African countries.     

Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration

Given that the United States is an engine of global stock market while China is the largest emerging market with a cornucopia of anomalies in particular, it is vital to investigate the risk-return relationship in the two markets. This paper brings new insights not only into risk-return tradeoff, but also to the leverage effect, with the application of the fractionally co-integrated vector auto-regression (FCVAR) model capturing the fractional cointegrated relationship and long memory property. Results show that China stock markets own the property of double long memory but the US markets don’t. Most of all, in the US market, a positive risk-return tradeoff exists for the whole sample while after the crisis, even we find the negative relation, it’s not a volatility feedback effect but low risk and high returns. However, there is only a volatility feedback effect in China stock markets. Besides, there is a leverage effect in the US market, while Chinese market exhibits a reverse one, another anomaly, indicating significant difference in the two markets again.     

An analysis of firm and market volatility

In this paper, using time series data for the period 2 January 1998 to 31 December 2008 for 560 firms listed on the NYSE, we examine whether firm volatility is related to market volatility. The main contribution of this paper is that we develop an analytical framework motivating the firm-market volatility relationship. We present three new findings on volatility. First, we discover significant evidence of common volatility; for 12 out of 14 sectors, market volatility has a statistically significant effect on firm volatility for at least 50 percent of firms. Second, we discover significant evidence of size effects: for small-sized firms, there is weak evidence of commonality in volatility, while for large-sized firms there is high evidence (for as much as 75 percent of firms) of commonality in volatility. Third, we find that market volatility predicts firm volatility for firms belonging to five of the 14 sectors.     

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.     

Order aggressiveness, pre-trade transparency, and long memory in an order-driven market

Recent empirical research has documented that the state of the limit order book influences stock investors' strategies. Investors place more aggressive orders when the same side of the order book is thicker, and less aggressive orders when it is thinner. We conjecture and demonstrate that this behavior is related to long memories of trading volume, volatility, and order signs in stock markets. We investigate our conjecture in two types of artificial stock markets: a transparent market, in which agents observe all limit orders on both sides of the book and order volumes at those prices before they trade; and a less transparent market, in which agents observe only the best five bid and ask quotes with the depth available at these limit prices. The first market structure resembles certain actual stock exchanges in the level of pre-trade transparency, such as the Australian Stock Exchange, NYSE OpenBook, and the London Stock Exchange, whereas the second market structure is consistent with stock exchanges such as Euronext Paris, the Toronto Stock Exchange, the Tokyo Stock Exchange, and Hong Kong Exchanges and Clearing. We demonstrate that our long memory results are robust with different levels of pre-trade transparency, implying that the strategy constructed by the state of the order book is key for explaining long memories in many actual stock exchanges.     

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.     

Information disclosure ratings and managerial short-termism: An empirical investigation of the Chinese stock market

Information asymmetry between managers and outside investors creates agency problems and impedes efficient capital allocation. Information disclosure is critical in alleviating information asymmetry in capital markets. This study investigates the effect of information asymmetry on managerial short-termism by examining information disclosure ratings (IDRs). Using real earnings management as a proxy for managerial short-termism, our analysis of a sample of Chinese A-share companies during 2001–2018 indicates that high IDRs mitigate managerial short-termism. The results also indicate that the effect of IDRs in reducing managerial short-termism is driven mainly by stock liquidity. This conclusion holds after consideration of endogeneity and application of two-stage least-squares and generalized method of moments methods, adjustment of the definition of IDRs, consideration of alternative proxies for managerial short-termism, and control for firm characteristics that might affect the extent of managerial short-termism. This study also examines the effects within three subsamples: companies listed on the Shenzhen Stock Exchange main board, small and medium enterprise board, and growth enterprise market board. IDRs substantially reduce managerial short-termism among firms listed on all three boards. These findings indicate that enterprises have corrected previous internal governance problems, and IDRs have helped to improve internal governance through stock liquidity. Therefore, external supervision also helps to reduce the agency problem of managerial short-termism.

     

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.     

Evolving United States stock market volatility: The role of conventional and unconventional monetary policies

Despite the econometric advances of the last 30 years, the effects of monetary policy stance during the boom and busts of the stock market are not clearly defined. In this paper, we use a structural heterogeneous vector autoregressive (SHVAR) model with identified structural breaks to analyse the impact of both conventional and unconventional monetary policies on U.S. stock market volatility. We find that contractionary monetary policy enhances stock market volatility, but the importance of monetary policy shocks in explaining volatility evolves across different regimes and is relative to supply shocks (and shocks to volatility itself). In comparison to business cycle fluctuations, monetary policy shocks explain a greater fraction of the variance of stock market volatility at shorter horizons, as in medium to longer horizons. Our basic findings of a positive impact of monetary policy on equity market volatility (being relatively stronger during calmer stock market periods) are also corroborated by analyses conducted at the daily frequency based on an augmented heterogeneous autoregressive model of realised volatility (HAR-RV) and a multivariate k-th order nonparametric causality-in-quantiles framework. Our results have important implications both for investors and policymakers.     

The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange,stock, and bond markets

We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables in our information set, including implied volatility backed out from option prices. Realized volatility is separated into its continuous and jump components, and the heterogeneous autoregressive (HAR) model is applied with implied volatility as an additional forecasting variable. A vector HAR (VecHAR) model for the resulting simultaneous system is introduced, controlling for possible endogeneity issues. We find that implied volatility contains incremental information about future volatility in all three markets, relative to past continuous and jump components, and it is an unbiased forecast in the foreign exchange and stock markets. Out-of-sample forecasting experiments confirm that implied volatility is important in forecasting future realized volatility components in all three markets. Perhaps surprisingly, the jump component is, to some extent, predictable, and options appear calibrated to incorporate information about future jumps in all three markets.     

Distribution theory for the studentized mean for long,short, and negative memory time series

We consider the problem of estimating the variance of the partial sums of a stationary time series that has either long memory, short memory, negative/intermediate memory, or is the first-difference of such a process. The rate of growth of this variance depends crucially on the type of memory, and we present results on the behavior of tapered sums of sample autocovariances in this context when the bandwidth vanishes asymptotically. We also present asymptotic results for the case that the bandwidth is a fixed proportion of sample size, extending known results to the case of flat-top tapers. We adopt the fixed proportion bandwidth perspective in our empirical section, presenting two methods for estimating the limiting critical values—both the subsampling method and a plug-in approach. Simulation studies compare the size and power of both approaches as applied to hypothesis testing for the mean. Both methods perform well–although the subsampling method appears to be better sized–and provide a viable framework for conducting inference for the mean. In summary, we supply a unified asymptotic theory that covers all different types of memory under a single umbrella.     

Analysis of the cross-region risk contagion effect in stock market based on volatility spillover networks: Evidence from China

This article uses the stock market regional indexes of 31 provinces (include Province-level municipalities and Minority Autonomous Regions) in mainland China as a sample, and constructs an inter-regional volatility spillover network of China’s stock market based on the GARCH-BEKK model. Through network centrality analysis, Diebold and Yilmaz's spillover index method and block model analysis, we comprehensively analyze the risk contagion effect among different regions in China’s stock market. The empirical results show that: (i) The risk contagion intensity (risk reception intensity) in various regions of China’s stock market has a typical “core-periphery” distribution characteristic due to regions’ different levels of economic development. (ii) There are obvious risk spillover effect in China’s stock market, among which the economically developed regions along the southeastern coast of China, such as Beijing, Shanghai, Zhejiang and Jiangsu, are the main risk transmitters, while the economically undeveloped regions in the Midwest of China, such as Xinjiang, Xizang, Gansu, Nei Menggu and Qinghai are the main risk receivers. (iii) Each region is divided into 4 blocks according to their respective roles in the risk spillover process in China’s stock market. Block 1 that is composed of the economically underdeveloped regions in the Midwest is the “main benefit block”, it acts as a “receiver”. Block 2 that is composed of regions with strong economic growth vitality in the Midwest is a “Bilateral spillover block”, it both plays the role of “receiver” and “transmitter”. Block 3 that is composed of developed regions along the southeast coast, it acts as a “transmitter”; Block 4 that is composed of the relatively fast-growing regions in the Southwest is the “brokers block”, it serves as a “bridge”. The results of this article can provide some reference for investors in financial institutions and decision makers in financial regulators.