Daily Stock Return Volatility Research Articles

Hierarchical modeling of irregularly spaced financial returns

Volatility modeling is crucial in finance, especially when dealing with intraday transaction‐level asset returns. The irregular and high‐frequency nature of the data presents unique challenges. While stochastic volatility (SV) models are widely used for understanding patterns in volatility of daily stock returns which constitute regularly spaced time series, new classes of models must be introduced for analyzing volatility in irregularly spaced intraday data. Specifically these models must accommodate the random gaps between successive transactional events. By modeling the gaps using autoregressive conditional duration (ACD) models, we describe a hierarchical irregular SV autoregressive conditional duration (IR‐SV‐ACD) model for estimating and forecasting intertransaction gaps and the volatility of log‐returns. We carry out the analysis in the Bayesian framework via the Hamiltonian Monte Carlo (HMC) algorithm with No‐U‐turn sampler (NUTS) in R using the cmdstanr package. The fits and forecasts are obtained using Monte Carlo averages based on the posterior samples. We illustrate this approach using simulation studies and real data analysis for intraday prices available at microseconds level of health stocks traded on the New York Stock Exchange (NYSE). The log‐returns and gaps are calculated for the stocks and are used for modeling.

The effect of Hungry Ghost Festival (HGF) on stock market returns and volatility: An empirical analysis of Asian stock markets

This study examines the effect of the Hungry Ghost Festival (HGF) on stock returns and volatility in five Asian stock indices. Shanghai demonstrated the highest average return and dispersion, while Singapore had the lowest. During the HGF, all countries’ kurtosis spiked, except for Philippines and Shanghai, signalling a high-risk investment with high returns. Finally, the current volatility of daily stock returns persists across countries and is influenced by previous shocks. Investment during the HGF may appear riskier and more volatile in some countries, but there is inconclusive evidence to conclude that the HGF had a significant effect on all markets.

Stock market integration and volatility spillovers: new evidence from Asia–Pacific and European markets

PurposeThis work investigates the volatility spillovers across stock markets and the nature of such spillovers through different periods of crises and tranquility.Design/methodology/approachUsing daily stock return volatility data from June 2003 to June 2021, the generalized forecast error variance decomposition method (based on Diebold and Yilmaz, 2012 approach) is employed to measure the degree of volatility spillovers/connectedness among stock markets of 24 Asia–Pacific and 12 European Union (EU) economies.FindingsThe empirical results from static analysis suggested that about 28.1% (63.7%) of forecast error variance in return volatility for Asia–Pacific (EU) markets is due to spillovers. The evidence from dynamic analysis suggested that during mid of the global financial crisis, European debt crisis (EDC) and Covid-19, the gross volatility spillovers for Asia–Pacific (EU) was around 67% (80%), 65% (80%) and 73% (67%), respectively. The degree of net volatility transmission from Singapore (Denmark) to other Asia–Pacific (EU) markets was found to be highest.Practical implicationsThe findings have crucial implications for the investors and portfolio managers in assessment of risk and optimum allocation of assets and investment decisions.Originality/valueThis study adds to the literature on risk management by systematically examining the impact of global financial crises, EDC and Covid-19 on the market interactions by capturing the magnitude, duration and pattern of the shock-specific market volatilities for a large sample of Asian and European markets using recent and large data set.

Top management team gender diversity and firm risk-taking in the hospitality industry

PurposeThe purpose of this study is to investigate the link between top management team (TMT) gender diversity and firm risk-taking in hospitality companies. The study also links female leadership to risk-taking. Finally, this study examines the moderating effects of TMT incentive pay and TMT age on the relationship between TMT gender diversity and firm risk-taking.Design/methodology/approachThis study uses an unbalanced data set of 81 hospitality firms and 888 firm-year observations over the period of 1992–2020. The study uses fixed-effects regression estimation for primary analyses and addresses potential endogeneity concerns via two-stage least square regression with firm fixed-effects instrumental variable regression. Risk-taking is measured by total firm risk (i.e. the annualized volatility of daily stock returns). Main results are supported with alternative measures of firm risk and estimation methods.FindingsThe study finds that increasing TMT gender diversity leads to a reduction in firm risk-taking in the hospitality industry. Moreover, the study finds that hospitality firms led by a female CEO experience lower firm risk compared to firms led by a male CEO. Finally, the study finds evidence that the relationship between TMT gender diversity and firm risk is contingent on the level of incentive pay awarded to TMT members and the age of TMT members. Increasing incentive pay and aging executive teams decrease the risk reduction effect of TMT gender diversity.Practical implicationsThe findings of this study recommend that firm risk-taking in the hospitality industry is related to gender diversity in TMTs. Hence, the board of directors should pay attention to gender composition for executive positions for risk management. Moreover, the results also suggest that care should be exercised when using incentive pay to align the interests of managers and shareholders. Finally, the board of directors needs to consider both gender diversity and age of the TMT members for TMT composition to manage executives’ risk-taking behavior.Originality/valueThis study fills a research gap in the hospitality literature by providing empirical evidence for the link between TMT gender diversity and firm risk-taking. Additionally, the study introduces incentive pay and age of TMT as contingency factors for the link between TMT gender diversity and firm risk-taking.

The long-term effects of section transfers on return volatility: Intraday and overnight periods

PurposeStudies on transfers to a more regulated section show an increase in information disclosure and stocks’ liquidity levels. Classical theories suggest that volatility should also be reduced. This study aims to analyse the long-term effects of a section transfer to a more regulated section (TSE 1/TSE 2) on stock return volatility.Design/methodology/approachThis study uses an empirical framework relying on two-sample t-tests and panel regressions. These use robust standard errors and control for fixed effects, day effects and macroeconomic factors. The return variance of comparable stocks’ benchmark sample, instead of market variance, is used as a control variable. Comparable stocks operate within the same industry and do not transfer during the sample period. The authors test our results’ robustness using generalized autoregressive conditional heteroskedasticity estimates.FindingsThe study’s main findings show that pre-transferred stocks are more volatile than the stocks’ benchmark sample. The transfer to a more regulated section leads to a gradual decrease in the total daily stock return volatility, intraday return volatility and overnight return volatility.Originality/valueTo the best of my knowledge, this study is the first to empirically address the volatility change caused by the stocks’ transfer to a more regulated section. This study highlights the benefits of choosing section transfers to reduce volatility.

Dynamics of Time Varying Volatility in Stock Returns: Evidence from Nepal Stock Exchange

This study examines the properties of time varying volatility of daily stock returns in Nepal over the period 2011-2020 using 2059 observations on daily returns of NEPSE index series. The study examines various symmetric and asymmetric GARCH family models using several specifications of error distribution. The results of symmetric GARCH (1,1) and GARCH-M (1, 1) models indicate that there is volatility persistence in daily returns on composite NEPSE index series over the sampled period. However, the estimated results for GARCH-M (1, 1) models show that the stock returns in Nepal offer no significant risk premium to hedge against risk associated with investment in stocks. The study also demonstrates that asymmetric TGARCH (1, 1) and EGARCH (1, 1) models fail to capture the leverage effects on the volatility. Finally, study results show that GARCH (1, 1) with student’s t error distribution model is the best fitted one to capture the volatility persistence of daily returns on NEPSE index series over the sampled period. The findings from this study offers an additional insight in understanding the volatility pattern of daily stock returns in Nepal for the most recent period that helps investors in forming a sound strategy to address the risk pattern of investing in stock market of Nepal.

On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting

This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models: sGARCH, girGARCH, eGARCH, iGARCH, aGARCH, TGARCH, NGARCH, NAGARCH, and AVGARCH along with value at risk estimation and backtesting. We use daily data for Total Nigeria Plc returns for the period January 2, 2001 to May 8, 2017, and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations. This investigation of the volatility, VaR, and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach. We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable. Additionally, for student t innovation, the sGARCH and girGARCH models failed to converge; the mean reverting number of days for returns differed from model to model. From the analysis of VaR and its backtesting, this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices. Furthermore, risk was reflected by significant up and down movement in the stock price at a 99% confidence level, suggesting that high risk brings a high return.

Modeling Stock Return Data Using Asymmetric Volatility Models: A Performance Comparison Based On the Akaike Information Criterion and Schwarz Criterion

The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model has been widely used in time series forecasting especially with asymmetric volatility data. As the generalization of autoregressive conditional heteroscedasticity model, GARCH is known to be more flexible to lag structures. Some enhancements of GARCH models were introduced in literatures, among them are Exponential GARCH (EGARCH), Threshold GARCH (TGARCH) and Asymmetric Power GARCH (APGARCH) models. This paper aims to compare the performance of the three enhancements of the asymmetric volatility models by means of applying the three models to estimate real daily stock return volatility data. The presence of leverage effects in empirical series is investigated. Based on the value of Akaike information and Schwarz criterions, the result showed that the best forecasting model for our daily stock return data is the APARCH model.

Day of the week effect and stock market volatility in Ghana and Nairobi stock exchanges

We examine Day-of-the-Week Effect anomaly and volatility in returns on Ghana Stock Exchange (GSE) and Nairobi Stock Exchange (NSE) using daily closing price indices from 2005 to 2014. Ordinary Least Square regression with autoregressive term, GARCH (1, 1), TGARCH (1, 1) and EGARCH (1, 1) were used. There is no evidence of day-of-the-week effect in GSE but there exists Friday effect in NSE. Daily returns could be predicted in NSE but cannot be predicted in the GSE using past prices and returns information. The GARCH model suggests a high degree of persistent in the conditional volatility of daily stock returns in NSE. The TGARCH and EARCH models show no evidence of asymmetry in daily returns in NSE. However, there was no evidence of conditional volatility for GSE-CI. The two markets are inefficient and whiles day of the week effects are irrelevant in making investment decisions in GSE it is relevant in NSE. Reduction in trade settlement periods to t + 1 and promoting internet usage will make market information freely available to all investors hence making the markets more efficient.

The Effect Of Accruals Quality On The Association Between Voluntary Disclosure And Information Asymmetry In Korea

Using data on the firms' voluntary disclosures from the Korea Stock Exchange from 2011 to 2014, we first empirically examine the association between voluntary disclosure and information asymmetry and then investigate the extent to which this association is affected by accruals quality since Korea adopted International Financial Reporting Standards (IFRS) in 2011. We use Comprix et al. (2011) and Shin and Park (2014)'s measures of information asymmetry. They are daily stock return volatility (VOLA) and trading volume turnover (VOL). We use the Dechow et al.'s (1995) revised Jones model and the Kothari et al.'s (2005) performance matched discretionary accrual model to measure the discretionary accruals. The absolute values of discretionay accruals are used as proxies for accruals quality. Final research samples with voluntary disclosure for this study are 1,226 (firms-years) companies. The research findings generally support our hypotheses. First, the relation between voluntary disclosure and information asymmetry is statistically and significantly positive as we have expected. The Korean companies with high voluntary disclosure would experience higher daily stock return volatility and less trading volume, which implies that companies tend to disclose biased information to the outside, which is consistent with prior studies in Korea. Second, the accruals quality (moderating variable) on the relation between voluntary disclosure and information asymmetry is statistically and significantly negative. Thus, we can conclude that when accruals quality is high, more voluntary disclosure decreases information asymmetry. These findings imply that accruals quality works as a mechanism in reducing the negative effect of voluntary disclosure on information asymmetry after the adoption of IFRS in Korea. The limitation of this study is such that we might not have considered other omitted variables and other proxies for the accruals quality, voluntary disclosure, and information asymmetry.

Cross-correlation asymmetries and causal relationships between stock and market risk.

We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock prices from the Standard & Poor's 500 index for 1994–2013. We focus on the behavior of the cross-correlations at the times of financial crises with significant jumps of market volatility. The observed historical dynamics showed that the dependence between the risks was almost linear during the US stock market downturn of 2002 and after the US housing bubble in 2007, remaining at that level until 2013. Moreover, the averaged cross-correlation function often had an asymmetric shape with respect to zero lag in the periods of high correlation. We develop the analysis by the application of the linear response formalism to study underlying causal relations. The calculated response functions suggest the presence of characteristic regimes near financial crashes, when the volatility of an individual stock follows the market volatility and vice versa.

Bayesian analysis of the distribution of volatility of daily stock returns

Bayesian analysis of the distribution of volatility of daily stock returns

Estimating Dynamics of S&P 500 Index and Volatility Using Option Market Data

This paper studies the joint dynamics of the daily return on the S&P 500 index and its volatility using the option market data. We construct a measure of the stock return volatility based on the Black-Scholes implied volatilities of exchange traded options. The implied volatility measure is shown to be an efficient proxy of the daily stock return volatility as it dominates the conditional volatility estimate obtained from the GARCH models. Using the time series of the implied volatility, we examine the goodness-of-fit of several existing stochastic volatility models and find that a CEV specification with non-linear drift fits the data well. We further estimate a jump-diffusion model of stock return and volatility. We present evidence that both stock return and volatility processes contain jumps and the jump sizes in return and volatility are negatively correlated. We also investigate the information content of two implied volatility related variables: the slope of volatility smile and the term structure of implied volatility. We find evidence that the slope can predict future stock returns while both slope and term structure can predict future volatilities.

Predicting stock index volatility: can market volume help?

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance. © 1998 John Wiley & Sons, Ltd.

An Empirical Examination of the Return Volatility‐Volume Relation in Related Markets: The Case of Stock and Options

AbstractThis paper empirically examines the impact of option trading on the relation between daily stock return volatility and stock trading volume. For a sample of firms for which options were newly listed on the CBOE from 1982 to 1985, the empirical evidence indicates that there is a structural shift in the relation after option trading is introduced. Also, the findings show that daily stock return volatility is significantly and positively correlated with contemporaneous option volume, but not one‐day lagged option volume. These results suggest that contemporaneous option volume may be an important variable in modelling daily stock return volatility and heteroskedasticity.

Information, trading, and volatility

We examine the effects of trading and information flows on the short-run behavior of stock prices by comparing the behavior of stock return volatility during trading and nontrading periods. We define nontrading periods as periods when exchanges and businesses are open but traders endogenously choose not to trade. After correcting for the bid/ask bounce and stickiness in quotes, we find that a large proportion of daily stock return volatility occurs without trades, especially for large firms. Furthermore, we provide new evidence that public (versus private) information is the major source of short-term return volatility.