Stock Return Volatility Research Articles

The Memory in Return Volatility: An Analysis of Mutual Fund Returns

ABSTRACTThis paper examines long memory in the return volatility in the cross‐section of U.S. mutual funds. Our results provide evidence of this phenomenon. Through univariate analysis, we find that the long memory in mutual fund return volatility is more pronounced than in stock return volatility. Additionally, the long memory estimate is negatively related to expected fund returns. Holding a long position in shorter‐term memory funds and a short position in longer‐term memory funds generates significant excess returns of 0.26% per month for value‐weighted portfolios.

Valuation and Returns on Stock Return Volatility

ABSTRACT This paper provides an accounting-based valuation model that predicts that cross-sectional variation in firm-level returns to investments in both stock and stock return volatility are related to cross-sectional variation in firm-level fundamentals. The model predicts that expected stock returns have a positive quadratic relation with stock return variance and a negative quadratic relation with gains to trading in stock return variance. Consistent with these predictions, firms with high model-implied expected stock returns have high future stock return variance, and the relation is roughly quadratic. In contrast, firms with high expected stock returns have low future returns to trading in stock return variance through option contracts because these firms have high option-implied variance relative to future realized variance, i.e., low variance risk premia (VRP). The study provides a framework for using fundamentals for trading in individual stocks and options. JEL Classifications: G12; G14; G17.

Nexus Between Stock Return and Market Volatility in Indian Perspective

This research seeks to explore the volatility patterns for the SENSEX index to figure out the characteristics of volatility in Indian stock trading and to investigate the relationship between returns and volatility of the Indian market for the last ten years. The research adds to the existing knowledge about stock market fluctuations, their implications for investors, and their impact on developing the country's economy. The study involves using daily returns data from BSE SENSEX from 01 Jan 2014 to 31 Dec 2023. Daily closing prices are obtained from the official website of BSE, and returns are calculated based on these prices. The Dickey-Fuller and Phillips-perron are taken to make the time series static. ARCH, GARCH, and GARCH-M tests are utilized to capture volatility clustering, return and volatility relationship. The results reveal that Fluctuations in the Indian stock market, particularly shown in the SENSEX index of the BSE, were highest in the year 2020. Findings suggest that GARCH-M does not show any relation between expected returns and market volatility. The risk-premium parameter is positive but statistically insignificant. If you want to hedge against risk, the risk premium is not very high. It means that taking risks does not give you more returns. Durbin Watson's value of 1.981401 further supports the model's suitability.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Mapping Risk–Return Linkages and Volatility Spillover in BRICS Stock Markets through the Lens of Linear and Non-Linear GARCH Models

This paper explores the influence of the risk–return relationship and volatility spillover on stock market returns of emerging economies, with a particular focus on the BRICS countries. This research is undertaken in a context where discussions on de-dollarization and the expansion of BRICS membership are gaining momentum, making it a novel and distinct exercise compared to prior studies. Utilizing econometric techniques to investigate daily market returns from 1 April 2008 to 31 March 2023, a period that witnessed major events like the global financial crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict, linear and non-linear models like ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, are employed to assess stock return volatility behaviour, assuming a Gaussian distribution of error terms. The diagnostic test confirms that the distribution is non-normal, stationary, and heteroscedastic. The key findings indicate a lack of the risk–return relationship across all BRICS stock markets, except for South Africa; a more pronounced effect of unpleasant news over pleasant news; a slow mean-reverting process in volatility; the EGARCH model is the best fit model as evidenced by a higher log likelihood and lower Akaike information criterion and Schwardz information criterion parameters; and finally, the presence of significant bidirectional and unidirectional spillover effects in the majority of instances. These findings are valuable for investors, regulators, and policymakers in enhancing returns and mitigating risk through portfolio diversification and informed decision making.

Realized Stock Market Volatility of the United States: The Role of Employee Sentiment

Using data for the United States (US) stock market covering the sample period from 2008:06 to 2020:12, we study the incremental predictive value of employee sentiment for the realized volatility of stock returns. In doing so, we control four different measures of investor sentiment and various macroeconomic and financial factors and uncertainties. We report results for several combinations of forecast horizons and estimation windows and find that employee sentiment contributes to forecast accuracy for several combinations.

Measuring firm-level manager risk perception

Using textual analysis of Chinese listed firms’ filings, we construct a new firm-level measure of manager risk perception. The measure identifies the extent of risk perceived by managers. We validate our measure by showing that it varies over time and across industries, correlates with aggregate economic policy uncertainty and firm-level stock return volatility, and relates cross-sectionally with firm characteristics. Managers with higher risk perception leads to a lower level of firm investment.

The Dissimilar Market Volatility in Neighboring Financial Markets: An Empirical Study Using A Multivariate Garch Model

ABSTRACT: Conventional knowledge of market volatility represents that two proximate financial markets operated in the same country would display a similar pattern in their time-varying volatility. In this paper, we propose that the heterogeneity in financial policy orientation of stock markets creates significant inter-market differences in terms of the volatility of stock returns, even if they are culturally and geographically linked to each other. For the empirical investigation of our proposition, the volatility data from two major stock exchanges in the Middle East, the Abu Dhabi Securities Exchange (ADX) and the Dubai Financial Market (DFM) are used. Both the ADX and DFM stock exchanges operate in the same country, the United Arab Emirates (UAE). The ADX is based in Abu Dhabi, the administrative capital, while the DFM is based in Dubai, the commercial and business hub of the UAE. They were both established in the year 2000, and their operational headquarters are located about 150 km from each other. This paper uses a multivariate GARCH model, in particular a diagonal VECH GARCH (1, 1) model to estimate volatility measures for stock returns between ADX-listed and DFM-listed stocks. The paper finds that the stock returns are significantly less volatile in the ADX compared to those in the DFM suggesting that the volatility transmission is incomplete between these two neighboring financial markets. This difference in volatility can potentially be attributed to the relatively conservative financial policies adopted by the oil-rich emirate of Abu Dhabi compared to the more market-oriented economic policies of Dubai, the country’s financial and commercial hub. Our findings have considerable implications for portfolio managers in ascertaining risk premiums when allocating investments across the two stock exchanges. For instance, this disparity in volatility should be taken into account by investors and policymakers when designing risk management strategies because it suggests that investors in the DFM may be exposed to higher levels of risk and uncertainty compared to their counterparts in the ADX. Investors operating in the DFM may require more robust risk management strategies, such as asset diversification and hedging, compared to their ASX counterparts.

Trading Volume, Intraday and Overnight Volatility of Stock Returns in the Nigerian Stock Exchange

Trading Volume, Intraday and Overnight Volatility of Stock Returns in the Nigerian Stock Exchange

Firms’ cost structure and stock return volatility

PurposeThis paper proposes a framework to identify a pattern in the relationship between firms’ cost structure (i.e. fixed versus variable) and their volatility in stock returns.Design/methodology/approachOur empirical analysis is based on a panel data regression where we use an extended sample period and a time-series regression-based elasticity measure of operating leverage.FindingsWe document significantly higher systematic risk among firms with large fixed costs, a conclusion which confirms theoretical predictions of earlier studies. In new findings, we document high firm-specific risk and high stock return volatility among firms with a fixed cost structure.Originality/valueThe paper fills a gap in the literature by examining the effect of cost structure using various operating leverage measures and other control measures for firm characteristics on idiosyncratic risk. Studies that seek to explain firms’ systematic risks are numerous; conversely, there are relatively fewer studies on the determinants of firms’ specific risks.

Inflation and stock return volatility in selected African countries: A GARCH-MIDAS approach

Amidst persistent inflationary pressures in Africa and central banks raising rates to counter inflation, leading to a shift from stocks to alternative assets and impacting stock return volatility, this paper investigates the influence of inflation on stock return volatility in five African countries using the GARCH-MIDAS approach, using mixed data of daily and monthly frequencies. We observe that inflation is important for stock return volatility, as higher inflation increases volatility and reduces returns. Our results provide a better understanding of the dynamics of stock market volatility while accounting for the role of inflation, using the natural frequencies of the variables. Our findings encourage monetary authorities to communicate their inflation expectations to the market participants/investors to moderate negative stock return volatility.

Impact of foreign ownership on stock return volatility of financial firms in Vietnam

The paper investigates the impact of foreign ownership on the stock return volatility of financial firms in Vietnam. Applying panel regression models on a year-end research sample of 23 banks and financial firms listed on stock exchanges in Vietnam from 2017 to 2022, the study assures the negative relationship between foreign ownership and the stock return volatility, implying that increasing foreign ownership reduces the volatility of stock returns of financial firms. Our study’s novelty is reflected in following aspects. First, our study, examining about the foreign ownership's impact on stock returns’ volatility in emerging stock market conditions, will enrich this research field by contributing additional empirical evidence to the field through a concrete and well-designed research. Additonally, our research findings not only provide useful information about the volatility of those influential shares in mentioned industries coupled with its determinants but also support investors and policymakers in making investment decisions informedly as well as reviewing suitable regulations for investors and capitals from foreign countries, which is a critical aspect in helping Vietnam's stock market to expand more steadily and sustainably.

Disaster learning and aggregate investment

We extend a production-based asset pricing model by introducing learning about disaster risk. The information is not perfect, and Bayesian learning is adopted to update beliefs about the likelihood of rare disasters. We show that disaster learning reconciles key stylized facts about macroeconomic quantities and financial markets. For macroeconomic quantities, during the crisis, the decline in aggregate investments is much worse than that of output, whereas the decline in aggregate consumption is moderate relative to that of output. Additionally, the model endogenously features lower consumption volatility and higher investment volatility than that of output. For financial markets, belief updating over a rare disaster produces a higher equity premium, lower risk-free rate, and more volatile stock returns. Finally, we show that jump intensity uncertainty accounts for a substantial fraction of the total welfare cost of rare disasters.

ESG and stock return volatility: Evidence from industry analysis in Europe

This study investigates the impact of environmental, social and governance fac- tors on stock return volatility in different European industries. Employing a sample of European companies belonging to different industries (Basic Materials, Consumer Cyclicals, Consumer Non-Cyclicals, Energy, Financials, Healthcare, Industrials, Real Estate, Technol- ogy and Utilities), I tested if ESG performance and ESG controversies influence volatility. Additionally, the study analyses the impact of the individual Environmental, Social and Governance pillars of ESG to determine through which component ESG affects volatility. Research findings showed that ESG factors impact stock return volatility across industries. The findings of the study contribute to and enrich the academic literature in this field through an industry analysis

Faktor Yang Mempengaruhi Volatilitas Return Saham di Indonesia: Perusahaan LQ45

This article is an empirical article with purpose of providing evidence regarding to factors influence volatility stock return such as dividend policy, leverage and investor sentiment. This study consists of 145 observation year-firm of LQ45 corporate on the Indonesia Stock Exchange for the 2017-2021 period as a research samples based on the purposive sampling method. This study also uses secondary data from company financial report and multiple linear regression analysis as an analytical tool. The result of analysis show that dividend policy doesn’t play a role in influencing stock returns volatility. While, leverage and investor sentiment can positively influence volatility stock returns. This research can be a guide for capital market investors in estimating stock return volatility by considering macroeconomic factors such as dividend policy and leverage as well as through investor sentiment.

Does climate policy uncertainty predict renewable energy stocks? A quantile-based (a)symmetric causality analysis

The identification of factors influencing the renewable energy stock market has recently received much attention from scholars. While the implications of economic policy uncertainty on renewable energy stock markets have been extensively studied in recent literature, less attention has been paid to climate policy uncertainty. This study adds to the existing body of knowledge by investigating whether the U.S. climate policy uncertainty predicts the return and volatility of five NASDAQ OMX Green Economy indices (fuel cell, wind, solar, developer/operator, and geothermal). The analysis is based on the nonparametric causality-in-quantile test and employs data from November 2010 to August 2022. The findings provide strong evidence of heterogeneous distributional predictability of climate policy uncertainty on the return and volatility of renewable energy stocks, which depends on the renewable energy sector and the market condition. More specifically, climate policy uncertainty does not cause stock returns during extreme downward and/or upward renewable energy market conditions. Additionally, uncertainty cannot predict volatility during the turmoil episodes that occurred in the renewable energy stock market. Further in-depth analysis considering the asymmetry reveals that a decrease (increase) in climate policy uncertainty improves the prediction of renewable energy stock returns (volatility) instead of a decrease (increase). These findings still hold when using a parametric causality-in-quantile test. The findings underscore the critical role of climate policy transparency and sector-specific strategies in reducing uncertainty and encouraging long-term investments in the renewable energy sector.

Does reform policy of municipal bonds raise firm risk? A quasi-natural experiment from China

Does reform policy of municipal bonds increase firm risk? We address this question by examining the effects of the ‘Local Issuance and Local Repayment’ (LILR) reform policy launched by the Chinese central government on firm stock return volatility between 2009 and 2019. We find that the LILR reform policy increases the volatility of firms' stock returns. Possible explanations for the effect of reform policy are that it exacerbates firms' financial constraints and reduces the effectiveness of fiscal policy. Consistently, we find that the aggravating effect of reform policy is more pronounced in regions characterized by worse financial environment, worse fiscal condition, firms in growth and decline stages and in downstream and service and support industries. We provide empirical evidence from a Chinese quasi-natural experiment to address the long-term economic development issues of local government debts.

Why do firms with no leverage still have leverage and volatility feedback effects?

The leverage effect hypothesis of Black (1976) and Christie (1982) posits that time-series variation in debt causes an inverse relation between stock return volatility and stock returns. Hasanhodzic and Lo (2019) test this hypothesis in a novel sample of firms with no debt and yet they still find an inverse relation, motivating them to espouse volatility feedback as an alternative. Under standard assumptions governing the risk-return relation from the asset pricing literature, I explain why the stock returns of all-equity-financed firms will still have leverage effects on par with those of debt-financed firms and why the absence of debt at the firm level has no bearing on the leverage and volatility feedback hypotheses.

Heteroscedasticity effects as component to future stock market predictions using RNN-based models.

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

Modeling and forecasting stock return volatility using the HARGARCH model with VIX information

AbstractThis study develops a novel approach for improving stock return volatility forecasts using volatility index information with the entropic tilting technique. Unlike traditional linear heteroskedasticity autoregressive methods with option‐implied information, we first derive predictive densities from traditional models, and then tilt using both the first and second moments of the risk‐neutral distribution, which enables us to capture the nonlinear effect in our specification. The empirical findings demonstrate a substantial enhancement in the forecasting accuracy of all models once the first‐ and second‐moment information is considered, where the improvement is both statistically and economically significant. These results have important implications for risk management in well‐established derivatives markets.

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.