Return Volatility Research Articles

LOG-NORMAL STOCHASTIC VOLATILITY MODEL WITH QUADRATIC DRIFT

In this paper, we introduce the log-normal stochastic volatility (SV) model with a quadratic drift to allow arbitrage-free valuation of options on assets under money-market account and inverse martingale measures. We show that the proposed volatility process has a unique strong solution, despite non-Lipschitz quadratic drift, and we establish the corresponding Feynman–Kac partial differential equation (PDE) for computation of conditional expectations under this SV model. We derive conditions for arbitrage-free valuations when return–volatility correlation is positive to preclude the “loss of martingality”, which occurs in many traditional SV models. Importantly, we develop an analytic approach to compute an affine expansion for the moment generating function of the log-price, its quadratic variance (QV) and the instantaneous volatility. Our solution allows for semi-analytic valuation of vanilla options under log-normal SV models closing a gap in existing studies. We apply our approach for solving the joint valuation problem of vanilla and inverse options, which are popular in the cryptocurrency option markets. We demonstrate the accuracy of our solution for valuation of vanilla and inverse options. a By calibrating the model to time series of options on Bitcoin over the past four years, we show that the log-normal SV model can work efficiently in different market regimes. Our model can be well applied for modeling of implied volatilities of assets with positive return–volatility correlation.

Research on the Synthesis of Hong Kong NFT Index Using Principal Component Analysis and Index Prediction Based on LSTM-Modified ARMA-GARCH Model

With the advent of the Web3.0 era, virtual assets have gained prominence in individuals asset portfolios, making Non-Fungible Tokens (NFTs) increasingly significant within the financial trading landscape. To address the issue of multicollinearity in regression analysis, this paper employs Principal Component Analysis (PCA) to perform dimensionality reduction on five correlated foundational sectors. Moreover, to enhance the accuracy and reliability of predictive outcomes, the study combines the Long Short-Term Memory (LSTM) model with the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model. Through the application of these methods and practical implementation, the study forecasts the NFT index of the Hong Kong stock market for the next 30 days. This forecasting of return volatility contributes vital insights for investment decision-making. The research complements and offers application recommendations in financial innovation, deepening, and regulation. By devising novel products and tools to meet investor demands, providing risk management and investment opportunities, the models predictive outcomes can be utilized in regulatory and risk management strategies within the national financial trading market. This study provides regulatory guidance, policy formulation insights, and envisions further refinements of the research methodology by integrating information shock effects.

Does Random Auction Ending Curb Stock Price Manipulation?

This paper examines the effect on stock market efficiency and potential market manipulation of introducing a random ending time for the call auctions that start and end continuous trading on three equity markets. We find that the probability of a price dislocation at the end of the auction declines, indicating a lower risk of market manipulation. In addition, the variance ratio and market-adjusted return volatility measures decrease, suggesting a more efficient and less volatile price discovery process. We confirm a behavioral change in order submissions by observing the timing of order entry, amendments, and deletions on one of the exchanges for which we have access to order data. Overall, our results indicate that adding a random auction ending time can reduce the risk of stock market manipulation and improve price efficiency.

Returns behavior of ESG ETFs in the COVID‐19 market crash: Are green funds more resilient?

AbstractThis study explores the importance of environmental, social, and governance (ESG) performance in explaining the returns and volatility of returns of exchange‐traded funds (ETFs) during the market crash induced by the COVID‐19 pandemic. The study uses an unbalanced panel of monthly data during the period 2005–2021 for a diversified sample of 160 iShares Morgan Stanley Capital International conventional and ESG/socially responsible investment (SRI) equity funds. To obtain robust results, a variety of analyses are conducted, including multiple panel regressions based on one‐way clustering, standard error estimates obtained from different covariance matrix estimators that are heteroskedasticity and autocorrelation consistent, simultaneous quantile and inter‐quantile regressions, weighted least square regressions with alternative weights for the regression coefficients, and tests of coefficient equality with bootstrapped standard error of difference in subsample regressions. All the sensitivity analyses give similar results, confirming their validity. The results provide strong evidence for the resilience of sustainable funds during the crash, with ETF returns showing persistence during the market downturn. This study suggests that ESG/SRI investments may help ETFs to outperform their counterparts, indicates that sustainability investments matter in times of crisis, and implies the adoption of strategies that promote sustainable investment development.

Mean-Value-at-Risk Portfolio Optimization Based on Risk Tolerance Preferences and Asymmetric Volatility

Investors generally aim to obtain a high return from their stock portfolio. However, investors must realize that a high value-at-risk (VaR) is essential to calculate for this aim. One of the objects in the VaR calculation is the asymmetric return volatility of stocks, which causes an unbalanced decrease and increase in returns. Therefore, this study proposes a mean-value-at-risk (mean-VaR) stock portfolio optimization model based on stocks’ asymmetric return volatility and investors’ risk aversion preferences. The first stage is the determination of the mean of all stocks in the portfolio conducted using the autoregressive moving average Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity (ARMA-GJR-GARCH) models. Then, the second stage is weighting the capital of each stock based on the mean-VaR model with the investors’ risk aversion preferences. This is conducted using the Lagrange multiplier method. Then, the model is applied to stock data in Indonesia’s capital market. This application also analyzed the sensitivity between the mean, VaR, both ratios, and risk aversion. This research can be used for investors in the design and weighting of capital in a stock portfolio to ensure its asymmetrical effect is as small as possible.

A comparative study of error distributions in the GARCH model through a Monte Carlo simulation approach

Financial time series data are known to exhibit volatility clustering, which implies that the volatility of financial returns tends to persist over time. This phenomenon has significant implications for risk management and financial decision-making. To capture this volatility clustering effect, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is widely used. Through a Monte Carlo simulation experiment, the performance GARCH-type model with five different error distribution specifications were compared. The study found that the Skewed Generalized Error Distribution (SGED) was the most efficient and consistent distribution for modelling financial time series. The SGED outperformed the Normal, Student's t, GED, and skewed Student's t distributions in terms of goodness-of-fit, MSE, MAE and different sample sizes. The study suggests that the SGED may be more suitable for modelling financial time series with a GARCH-type specification in general contexts.

ESG news, stock volatility and tactical disclosure

Against the backdrop of the increasing importance of ESG investing, our goal is to explore whether ESG-related disclosure timing is tactically managed by listed companies. An international comparison is undertaken on a sample of 199 blue-chips listed firms, located in Europe, Usa and Asia. Using a dataset of 29.724 ESG news over a period of 6 years (2015–2020), we evaluate the disclosure determinants using a Probit model. We find that the probability of negative ESG news disclosure is lower when the stock is experiencing higher returns volatility. This evidence suggests that managers refrain from fuelling market instability and eroding firm’s reputation capital with the disclosure of bad ESG news, adopting a type of hoarding behaviour. Similarly, managers tend to boost positive ESG-related information when the returns volatility is higher, even though the evidence is milder than for bad news. Our findings highlight that the degree of transparency on ESG matters remains incomplete and there are spaces for tactical disclosure. Concerned investors should be aware that their information on sustainability matters might be biased, especially in times of higher volatility.

Returns and Asymmetric Volatility Interdependencies between Sustainable and Conventional Stock Indices: Evidence from Developed and Emerging Countries

The study investigates the co-integration and asymmetric volatility interdependence between the sustainable and conventional stock indices of emerging and developed countries. Daily closing values are used from 1st January 2015 to 15th July 2022. The study employs ARDL and VAR-Granger causality model to examine long-run association and cause-effect relation, respectively. Moreover, the Bivariate EGARCH model is also used to analyse return and asymmetric volatility spillover between two indices. The analysis reveals no evidence of co-integration between sustainable and conventional indices. However, bidirectional causality exists in all countries except the United Kingdom (UK) and United Arab Emirates (UAE). The return spillover result exhibits that return of the conventional index of Japan and Brazil are negatively affected by changes in its sustainable index. The EGARCH results reveal the bidirectional volatility spillover in all the markets. The volatility shocks from the sustainable index of the United States of America (USA) and China have an asymmetric effect on the conventional indices of these countries. Further, positive asymmetric spillover was observed between the sustainable and conventional indices of Brazil and UK. The knowledge of linkage and asymmetric volatility spillover will assist the investors in portfolio management and formulating hedging strategies during financial and economic adversities.

An analysis of spatial effects of terrorism on stock market returns in the Middle East countries

PurposeStock markets are highly sensitive to foreign and domestic events. Stock exchange markets react promptly to news and are known as an indicator of good and bad trading conditions. Terrorist attacks leave adverse effects on the economy and cause stock price volatility and, consequently, stock return volatility. Therefore, this paper aims to analyze the spatial effects of terrorism on stock market returns in the Middle East from 2008 to 2019.Design/methodology/approachThis paper uses analytical research design and estimates spatial model. Before estimating the spatial model, the spillover effects were confirmed for the spatial autoregressive model using Moran’s diagnostic test for spatial dependence, Geary’s C test and Akaike statistic.FindingsThe results of this study on spatial panel data and based on spatial autoregressive estimator indicated that terrorism and associated neighborhood effects had a negative impact on stock returns in Middle East countries. Also, the corruption index and oil price negatively affected stock market return in these countries, while the democracy index had a positive effect on stock market returns. According to the results, to achieve a high and stable stock market return, it is recommended that high-level consultation is pursued with leaders of involved countries to reduce the devastating effects of terrorist activities, increase political and economic stability, attract stockholders to stock markets and spend corresponding incomes developing the infrastructures in this sector in countries of this region.Originality/valueMost of the studies have investigated the impact of terrorism on the stock market returns at the national or provincial level. However, the effect of terrorism on the stock market index in the tense region of the Middle East, which is the center of terrorist attacks in the world, has not been dealt with by considering the spatial econometric effects. Thus, to the best of the authors’ knowledge, this research is a first attempt to study the impact of terrorism on the stock market returns in Middle East countries using the spatial econometric approach.

Are stress‐tested banks in the United States becoming similar? Evidence from convergence tests

AbstractUS bank stress tests were introduced to improve the risk posture and management practices of large and complex banking institutions. We investigate whether stress‐tested banks in the United States converge to each other in their levels and determinants of profitability, as well as their risk taking and systemic risk contributions. Our results are consistent with convergence in profitability, asset quality, management quality, securitization income, and income diversification of stress‐tested banks. Our difference‐in‐differences estimation results provide causal evidence of stress tests leading to convergence in income diversification. Furthermore, stress‐tested banks converge in their income diversification strategies, return volatility, default risk, leverage risk, and systemic risk contributions. This study sheds light on a situation where these banks may simultaneously have exposure to the same risks, leaving the financial system more vulnerable to a crisis as a result of the exposed risk.

Investors' opinion disagreement and abnormal trading around pre-earnings announcements

We use textual analysis techniques to construct a direct measure of investor disagreement to explain the abnormal stock volume and return around earnings expectation announcement in China. We crawl scrape the most a popular Chinese stock forum for individuals' posts about their opinion on a stocks, and classify the posts with machine learning methods. We find that our measure is most correlated with return volatility and historical turnover. We then conduct an event study for abnormal stock volume around pre-earnings announcements, examining the cumulative abnormal turnover for sample groups with prior agreement or disagreement, and with a convergence or divergence of opinion. We find that given prior disagreement of opinion, both convergence and divergence of opinion causing more trading volume in the Chinese market. We also observe the sample group with a divergence of opinion has more cumulative abnormal return than the convergence group. However, this effect is found to be more profound in stocks with higher institutional ownership, which is believed to be correlated with a low short-sale constraint.

Is the effect of a health crisis symmetric for physical and digital financial assets? An assessment of gold and bitcoin during the pandemic.

The emergence of the covid-19 health crisis, in this advanced technological era where connections between markets, nations, and economies have grown stronger than ever before, the shock of the COVID-19 pandemic quickly had an impact on both physical and digital financial assets. The Chinese financial market experienced the first consequences of the covid-19 pandemic, then spilled over to other financial markets, including those for cryptocurrencies and the precious metals. This study examines the impact of the covid-19 pandemic on the volatilities of the dynamics of bitcoin and gold. Both assets share some characteristics, such as online trading platforms, however, gold is a tangible financial asset unlike bitcoin, which is digitally generated without any physical form. This study argues that the similarities and differences between bitcoin and gold play major roles in how the covid-19 crisis affected their respective dynamics. Using daily data ranging from 9/22/2014 to 1/31/2023 and employing ARMA as the mean equation for GARCH model, the impact of the health crisis (covid-19) is examined on the volatilities of the prices and volumes of bitcoin and gold. Empirical evidence points out that, the pandemic has a symmetric impact on the volatilities of bitcoin and gold price returns, causing them to be more volatile. The impact of the covid-19 observed on the volume returns of the assets, however, is asymmetrical. The empirical results give evidence to the role that the vital differences existing between these assets played during the covid-19 pandemic.

An Analysis of the Fama-French Three-Factor Models Capacity to Account for Cross-sectional Volatility in Stock Returns

The studys main focus is on the Fama-French Three-Factor Models capacity to account for cross-sectional volatility in stock returns. The Fama-French Three-Factor Model was used to assess a sample of 720 equity funds and determine each funds excess returns and three-factor risk exposures. The exposure of each element was then tested using linear regression to see if it could predict the excess returns. The studys focus is on the shortcomings of the traditional Capital Asset Pricing Model (CAPM) and the possibility that the 3-factor model could offer a more precise and thorough explanation of stock returns. The empirical results indicate that it may not be robust all the time, and there could be other factors functioning meanwhile, which are not captured by the model. Furthermore, the Efficient Market Hypothesis (EMH) may not be effective and accurate in its strictest form. The studys empirical examination of the Three-Factor Model and its implications for the Efficient Market Hypothesis (EMH) and other related ideas are what give it its value.

The Effect of Rise of Federal Funds Rate on US Stock Market: From the Perspective of US Dollar Appreciation

The study examines the impact of the rise of the Federal Funds Rate (FFR) on the stock price returns and volatility of the United States of America. The study extracted the stock price from January 3, 2022, to June 16, 2023, from the Investing finance terminal, including the Standard and Poors (S&P 500), US Dollar Index, and the National Association of Securities Dealers Automated Quotations (NASDAQ) Index. This study uses the Vector Autoregression (VAR) model and the ARMA-GARCH model to evaluate the stock returns and daily return volatility, respectively. The two models inspect the effect of the US Dollar index on both the NASDAQ and S&P 500. The result of this study suggests that the rise in the Federal Funds Rate would cause the stock market price to decline for a relatively long period. However, the volatility of the daily return remains stable. By studying the effect of the rise in the Federal Funds Rate, policymakers can change the stock prices by adjusting the Federal Funds Rate based on the markets needs.

Corporate acquisitions and firm-level uncertainty: Domestic versus cross-border deals

This paper investigates how the announcement of acquisitions affect the uncertainty that financial markets perceive about acquiring firms. We use data for publicly-listed firms in the UK between 2004 and 2017 and employ a matching estimator combined with difference-in-differences to address the endogenous selection of firms into acquisitions. While acquisition announcements do not result in a significant change in the volatility of stock returns of acquiring firms across our whole sample, this result hides substantial heterogeneity. Our main finding is that the impact of acquisitions on uncertainty is crucially shaped by a deal's geographic scope—i.e. whether the takeover involves a target in the same country or abroad. Domestic deals reduce the volatility of acquirers' returns by 5% on average one quarter after the announcement, while acquiring a foreign firm increases volatility by a similar magnitude. The heightened volatility resulting from cross-border transactions is primarily driven by acquisitions in industries characterized by high investment irreversibility and foreign markets where barriers to investment are higher. Conversely, the volatility reduction following domestic acquisitions is more pronounced in industries with low irreversibility. Additionally, characteristics of the deal itself, such as the relative size of the acquisition, the payment method, and whether the deal achieves majority control of the target, also play an important role in mediating the effect of acquisitions on volatility.

Institutional ownership and stock return volatility during the COVID-19 crisis: An international evidence

Using a sample of 39 countries from 2010 through 2021, we find that while institutional holdings have the stabilizing effect during the normal (non-crisis) period, they appear to move stock prices away from fundamentals during the COVID-19 crisis. Furthermore, the impact of foreign and domestic institutions is not homogenous. We also find that institutional investors help reduce stock return volatility in countries more strongly affected by the COVID-19 crisis. Finally, our results indicate that investor protection moderates the impact of institutional holdings on stock return volatility in both pre- and crisis periods.

The effect of board of commissioners size on risk management trends

This study aims to obtain empirical evidence of the effect of board size on risk management tendencies. The data collection technique used was a purposive sampling method in order to obtain a sample of 531 manufacturing sector companies listed on the Indonesia Stock Exchange in 2014-2017. The data analysis used is Multiple Linear Regression Analysis. The results of this study are that a large number of boards has no impact on the volatility of company stock returns. However, it is positively related to cash flow volatility proxies.

Zastosowanie jednowskaźnikowego semiparametrycznego modelu ekonometrycznego w analizie ryzyka inwestycyjnego

One of the tasks of applied econometrics and statistics is the estimation of the conditional mean function of the assumed model. The available methods for estimating such a function and the estimation results depend mostly on the a priori assumptions about the population or process that generates the data. The aim of the research presented in this paper is to apply single-index semiparametric econometric model to measure investment risk on the Warsaw Stock Exchange (WSE). Such a model is characterised by some assumptions less restrictive than in the case of other parametric models for the conditional mean function, such as a linear model or a binary probit model. At the same time, a single-index model retains many of the desirable features of a linear model and a least squares method. The presented model was used to measure investment risk for 10 IT companies quoted on the WSE in the period from 2018 to 2021. The data came from the Bloomberg financial service. Rates of return of two stock market indices, WIG20 (Poland) and S&P 500 (USA), were adopted as risk factors. The results indicate that the Polish market determines the volatility of returns of the analysed companies to a much larger extent than is the case with the US market. Furthermore, semiparametric models proved more flexible than the parametric ones regarding theoretical assumptions, which in the event of a large inflow of information might facilitate making correct investment decisions.

Does CEO Power Affect the Relations between CEO Compensation and Risk-Taking?

Purpose - This study investigates the effect of incentive compensation and CEO power on a firm’s risk-taking behavior, leveraging stock return volatility and earnings volatility as the key indicators of risk-taking.
 Design/Methodology/Approach - A comprehensive analysis is conducted using data from 1,958 U.S. firms over a 28-year period (1992- 2019). The study employ both ordinary least square (OLS) regression and two-stage least square (2SLS) regression for analysis. Pay-volatility sensitivity (PVS) and CEO-pay slice (CPS) are used as proxies for incentive compensation and CEO power, respectively.
 Findings - The findings offer solid empirical proof that PVS and CPS have a negative influence on both earnings volatility and stock return volatility. Furthermore, the detrimental effect of PVS on managerial risk-taking is significantly more pronounced for CEOs with lower CPS compared with those with higher CPS. While incentive compensation appears to deter CEOs from embracing higher risks, CEOs with more power are less risk-averse when offered incentive compensation compared to their less powerful counterparts.
 Research Implications - This study significantly contributes to existing literature by revealing the interactive effects between CEO power and incentive compensation on a firm’s risk-taking behavior. Understanding these dynamics is pivotal as it offers a nuanced view of how CEO compensation structures and power hierarchies within a firm influence its risk-taking propensity, which is critical for both firm performance and broader financial stability considerations. The insights derived from this analysis can inform more balanced executive compensation structures and governance models that ensure an optimal level of risk-taking.

Pandemics and Stock Price Volatility: A Sectoral Analysis

In this paper, we assess the impacts of the five most recent pandemics on the volatility of stock prices across forty-nine sectors of the economy in the United States. These five most recent pandemics are the 1957–1958 Asian flu, the 1977 Russian flu, SARS-CoV-1, swine flu and COVID-19. Applying the GJR-GARCH model, we find that pandemics other than COVID-19 have heterogeneous impacts on the volatility of stock returns. The results of our analysis indicate that COVID-19 has increased the volatility of stock returns in all sectors. Similarly, stocks in more than seventy percent of sectors in our study declined during the ongoing pandemic, perhaps reflecting the severity of the pandemic. In addition, our results on sectors such as healthcare and natural gas diverge from other literature. The mixed results on SARS-CoV-1 are partially explained by the fact it emerged at a time when stock valuations were particularly pessimistic. In the case of Russian flu, it was relatively short-lived and limited in spread relative to other pandemics in our study.