Stock Return Volatility Research Articles

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.

Trading Volume Activity, Earning Quality and Stock Return Volatility of Listed Consumer Non-Cyclicals Companies in Indonesia: Does Corporate Reputation Play a Role?

This study aims to investigate the impact of trading volume activity and earning quality on stock return volatility, with corporate reputation as a moderating factor, within 175 consumer non-cyclicals companies listed on the Indonesia Stock Exchange (IDX) from 2017 to 2021. Findings indicate that trading volume activity positively influences stock return volatility, while earning quality negatively affects it. Moreover, corporate reputation amplifies the positive relationship between trading volume activity and stock return volatility, and it also enhances the negative relationship between earning quality and stock return volatility. The negative effect of earning quality on stock return volatility is reinforced by corporate reputation. The originality of this study is to add the corporate reputation variable as a moderating variable in moderating the effect of trading volume activity and earning quality on return volatility. This research is expected to be useful for investors when investing in the stock market. Besides that, it is also expected to be useful for companies to be more careful in carrying out the company's operational activities because it will be related to the provision of capital by investors.

Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers.

Corporate Governance and Stock Return Volatility

This research highlights the importance of corporate governance and helps investors and financial economists understand stock price behaviour after COVID-19. The study aims to find out the influence of the independent board of commissioners, and audit quality on the volatility of share returns in companies operating in the body care product sector and listed in the EIB in 2021-2022. In addition, this study also employs four control variables to avoid the model specification error. The population in this study is a company of body care products listed on the Indonesian Stock Exchange for the year 2021-202. The number of samples used is as many as eight companies in 2021-2022 based on purposive sampling methods that meet the sample selection criteria. The data analytical techniques used are descriptive statistical testing, classical assumption testing, and hypothetical testing using SPSS software version 25 and GRETL. The research results show that the independent board of commissioners and the audit quality influence share return volatility. As a sector directly related to daily consumer needs, the primary consumer goods sector tends to be highly responsive to changing trends and consumer preferences. It makes it interesting research subject to understand how the volatility of share returns in this sector reacts to external and internal factors.

Risk Premium and Volatility Analysis on Indonesia Stock Exchange

Market risk premium and market volatility are important in investment decisions. Volatility is an important variable in derivative securities which is a measure of changes in stock returns. The research focuses on stock return volatility, research that points to a risk premium in emerging markets. The purpose of this study is to explain the relationship between market equity premium and volatility using GARCH (1.1) on the Indonesia Stock Exchange. This research uses daily closing price data of the Indonesia Stock Exchange Composite Index (JCI). The result of this study, that there is a relationship between risk premium and volatility in the Indonesian stock market. The conclusion of this study is to try to test whether there is a relationship from the volatility of return to risk premium in the Indonesian stock market. Using the daily trend of the Indonesian stock market (IDX) from January 2010 to September 2023.

Empirical Analysis of Factors Affecting Stock Liquidity --Taking Ping An Bank as an Example

With the continuous development of the Chinese economy, the stock market has become one of the important choices for investors, and the liquidity of the stock market has always been the focus of investors. Ping An Bank is one of China's well-known commercial banks and occupies an important position in the A-share market. The goal of this paper is to study the relationship between stock liquidity and return volatility, risk factors, circulation market value and cross-period of risk factors. Through ordinary least squares (OLS) regression analysis, it can be found that earnings volatility and circulating market value have an impact on stock liquidity significantly, that is, the higher the earnings volatility and circulating market value, the higher the stock liquidity. Risk factors and cross-terms of risk factors have no significant influence on stock liquidity. These findings have important implications for investors and market regulators to help them more fully assess stock value and risk and make more informed investment decisions.

Corporate information preference and stock return volatility

ABSTRACT This paper models the effect of corporate information preference on stock return volatility based on optimization problems of information decisions for firms and investors. Our model hypothesizes a positive correlation between corporate information preference and volatility. Utilizing the ideal institutional background of the Chinese stock market, we empirically confirm that corporate information preference has a positive impact on volatility, particularly for firms facing more severe financial distress, limited investor attention, and less analyst coverage. Our study provides a new perspective for analyzing the interaction between information supply and asset price dynamics.

CEO past distress experience and risk‐taking: Evidence from US property–liability insurance firms

AbstractWe investigate the relationship between CEOs' past distress experience and risk‐taking in US property–liability insurance companies. Our evidence shows that CEOs' past distress experience is negatively associated with insurers' risk‐taking behavior, suggesting that CEOs with distress experiences tend to take lower levels of risk in making financial decisions for their firms. The results are robust to using alternative measures of risk‐taking, including value at risk, expected shortfall, volatility of stock return, idiosyncratic volatility, systematic volatility, underwriting risk, and investment risk. Additionally, our results pass a placebo test, and we mitigate endogeneity concerns with the propensity score matching method.

Climate Change Effects and Stock Market Returns

To capture climate change risk at the business level, use records of performance briefings from different enterprises. This examination discovers that chances of worst climate change, the negative impact of business environment risk on market stock returns. Study also conducts a broad assessment of the empirical and theoretical literature on the influence of climate change related risks on financial market. The main aim of this analysis is to enhance our knowledge of the estimation significances of climate change risk in financial markets. It is initiated by discussing the theoretical connections between market asset pricing and climate change related risks, and then propose a hypothesis of how climate change risk drivers convey costs to enterprises and cause stock returns variations. It studies the historical climate change related events, which indicate that both climate physical effects and transition dynamics can cause a stock return volatility. Finally, the results imply that disclosures of climate change related information can support the stock market in more efficiently as pricing climate risk.

R&D investments and idiosyncratic volatility: evidence from a quasi-natural experiment

ABSTRACT An intriguing phenomenon in the U.S. stock market is that the level of average stock return volatility has increased considerably since the 1960s. We argue that corporate R&D investments provide an explanation for this phenomenon due to the uncertainties of R&D investment payouts and the information asymmetry between firms and investors. We employ a quasi-natural experiment, namely, the changes in state-level R&D tax credits in the U.S. to investigate the effect of R&D investments on idiosyncratic volatility. The results of baseline regressions show that a one-standard-deviation increase in R&D tax credits (user cost of R&D capital) is associated with a 0.013 (0.049) standard-deviation increase (reduction) in idiosyncratic volatility. This finding provides an explanation for the increasing trend in the average stock return volatility over the recent decades.

Do Financial Performance and Sustainability Activities Matter for the Stock Performance? Investigating Non-Primary Consumer Goods in Indonesia

Purpose: This study seeks to investigate the impact of sustainability reporting index, particularly focusing on non-primary consumer goods, on stock returns during the period of 2018 to 2022, with a specific emphasis on the COVID-19 pandemic period. By analyzing the relationship between sustainability reporting and stock returns, the research aims to provide insights into the resilience and stability of companies in the face of market volatility induced by the COVID-19 crisis, particularly in the Indonesian stock market. Methods: The research methodology involves the application of multiple linear regression, incorporating dummy variables to empirically test the influence of sustainability reporting index on company stock returns during the COVID-19 event. Data collection entails gathering sustainability report data from each company within the non-primary consumer goods sector. Statistical analysis techniques are used to assess the impact of sustainability performance on stock return volatility and stability during the specified time period. Results and Discussion: The findings of the study reveal that firms within the non-primary consumer goods sector experienced substantial impacts on their leverage and profitability during the COVID-19 pandemic, significantly influencing stock returns. Notably, companies with strong performance in ESG (Environment, Social, and Governance) aspects demonstrate relatively stable stock prices amidst the epidemic. However, the analysis indicates that social reporting index performance exhibits higher volatility in stock prices compared to other ESG components. Moreover, the study observes a minimal increase in stock return volatility for companies with good ESG performance. Implications of the Research: The research outcomes have important implications for investors, policymakers, and corporate stakeholders, particularly in understanding the role of sustainability reporting in mitigating stock return volatility during times of crisis. The study underscores the significance of integrating ESG considerations into investment decisions and corporate strategies to enhance resilience and stability in the face of market disruptions such as the COVID-19 pandemic. Additionally, the research provides policy recommendations for government intervention through fiscal incentives to promote ESG investment and sustainable corporate practices. Originality/Value: This study contributes to the literature by examining the impact of sustainability reporting index on stock returns within the context of the COVID-19 pandemic, particularly focusing on the non-primary consumer goods sector in Indonesia. By providing empirical evidence of the relationship between ESG performance and stock return volatility during a period of crisis, the research offers valuable insights into the role of sustainability reporting in driving investor confidence and market stability. The findings highlight the importance of sustainable investment practices and provide actionable recommendations for policymakers and corporate decision-makers alike.

Risk and Return Analysis on Selected 10 Companies: a Five-year Study (2018-2022)

The "Risk and Return Analysis on Selected 10 Companies: A Five-Year Study (2018-2022)" delves into the intricate dynamics of risk and return within the equity market landscape over a significant period. Through meticulous examination spanning five years, this study aims to unravel the performance metrics of ten carefully chosen companies, providing investors and financial analysts with invaluable insights into optimizing portfolio strategies and maximizing returns. Employing contemporary methodologies and empirical data, the study contributes to the ongoing discourse surrounding risk-return dynamics, addressing the escalating need for evidence-based decision-making in financial markets amidst increasing complexity and volatility. Methodologically, the study employs a multifaceted approach, including the meticulous selection of ten companies based on predefined criteria, comprehensive data collection, and the application of various quantitative techniques to evaluate risk and return metrics. The analysis focuses on equity stocks over the specified five-year period, utilizing variance and standard deviation as fundamental statistical measures to quantify dispersion and volatility of stock returns. The study aims to furnish stakeholders with actionable insights to optimize investment decisions and mitigate potential risks effectively, ultimately aiding stakeholders in making well-informed investment decisions aligned with their financial objectives and risk tolerance levels.

Board Gender Diversity and its Impact on Firm Risk-Taking

This study aims to analyze the gender diversity of boards of directors and its impact on firm risk-taking. It is interesting to study because the presence of women on the board of directors of a firm has garnered considerable attention in the last few years. The sample is drawn from the industrial sector listed on the Indonesian Stock Exchange from 2017 to 2021. Firm risk-taking is measured by the volatility of stock returns. Using panel data, the results of the research indicate that gender diversity has a significantly positive impact on firm risk-taking. This finding contributes that a female board of directors would lead to greater risk-taking than their male counterparts. This research provides a literature on gender diversity and firm risk-taking in a male-dominated sector in Indonesia, providing insights for investors and companies to consider the potential benefits of gender diversity on board of directors in managing firm risk-taking. Further research could apply other measurements of firm risk-taking and observe other male-dominated sectors for comparison to enhance the study’s limitation.

Vulnerability of a developing stock market to openness: One-way return and volatility transmissions

Drawing from two independent literature sources on vulnerability and stock price volatility, we show that the high magnitude and persistence of stock return volatility evident in the Nigerian Exchange Group (NGX) are, at least in part, indications of vulnerability to openness, which paves the way for the transmission of external shocks. Using a uniquely modified set of bivariate asymmetric GARCH models, we investigate one-way return and volatility transmissions from the international Brent oil market, natural gas markets, carbon markets, and naira-dollar exchange market to the NGX. Firstly, we document direct and asymmetric return and volatility transmissions from the Brent oil market to the NGX, denoting the fragile sensitivity of the NGX to international oil price turbulence. Secondly, we find a direct volatility transmission from natural gas markets to the NGX, implying an increasing reliance on natural gas exports by the Nigerian government. Thirdly, we document an inverse volatility transmission from carbon markets to the NGX, indicating Nigeria's negative reaction, as a major oil and gas producer, to increasing global sustainable production activities. Fourthly, the insignificant effect of the naira-dollar official exchange rate on volatility in the NGX demonstrates the spurious impression that Nigeria's exertion of control over the exchange rate is shielding the economy. In sum, our results confirm that the openness of the NGX makes it vulnerable to external shock transmissions from related external markets subsisting outside Nigeria. Our results are robust to an alternative estimation procedure that employs a different measure of price volatility from the three external markets.

Forecasting volatility of stock indices: Improved GARCH-type models through combined weighted volatility measure and weighted volatility indicators

This paper proposes an unbiased combined weighted (CW) volatility measure and weighted volatility indicators (WVI) that integrates the return- and range-based volatility measures to model the dynamics volatility of stock returns. The main feature of the CW measure is that it is formulated based on the weighted inter- and intra-price information to quantify the volatility directly, while the WVI effectively identifies signals on the shift of volatility. Empirical analysis using five stock indices demonstrates that the CW measure, utilising squared returns in combination with range-based Garman-Klass volatility measure, exhibits the lowest losses based on root mean squared error and quasi-likelihood when compared to 5-minute realised volatility as a proxy for true volatility. Furthermore, we investigate the feasibility of incorporating the CW measure and WVI as the exogenous variable(s) in the generalised autoregressive conditional heteroscedasticity (GARCH)-type models to enhance the forecasting performance. The findings indicate that the GARCH-CW-WVI and EGARCH-CW-WVI models exhibit superior in-sample model fit based on the Akaike information criterion than the existing GARCH and EGARCH models. Moreover, our proposed models also offer the best out-of-sample forecasts evaluated using various loss functions and further tested using Hansen’s model confidence set based on the mean squared error loss. Different risk levels of value-at-risk (VaR) and expected shortfall (ES) forecasts based on GARCH-CW-WVI and EGARCH-CW-WVI models are computed and examined with various backtests to confirm the accuracies of VaR and ES forecasts.

Kandungan Informasi Pengendalian Internal :Bukti Dari Pengungkapan Kelemahan Material

ABSTRACT This journal examines the information content of internal control. I use two conventional measures of information content – stock return volatility and trading volume – and find that both measures show a statistically greater event-period reaction to disclosure of a company's initial material weakness compared to non-period events. -adjacent events and against a sample of matched control companies. In a multivariate setting, I find significant and positive relationships between the material weakness variable and both measures of information content after controlling for several other event period news sources. Collectively, the evidence suggests that investors respond to the information contained in material weakness disclosures, consistent with these disclosures providing additional value-relevant news for investors to consider in making resource allocation decisions.

Research on Volatility Prediction of Stock Returns Based on ARIMA Model

This article illustrates the prediction of stock return volatilities for enhancing confidence and minimizing the risk of investors in making investments. The most suitable ARIMA model for making a forecast is selected with the help of KPSS and portmanteau test. To avoid the unpredictable influences from macroeconomic factors and to prove the validity of the volatility prediction in the real world, a training dataset and a test dataset are selected from a specific time period with minimum variations in these factors. The forecast data tells that in a period of few variations in macroeconomic factors, the selected ARIMA model could make precise predictions for the volatilities. In the period which the volatilities are strongly affected by factors such as inflation and money supply, the model could still be helpful in reducing the risk and enhancing the accuracy of predictions for the investors if they understand the way volatilities change with these factors.