Stock Market Volatility Research Articles

Industry return volatility and spillover effects in stock market under climate risk perception

ABSTRACT Utilizing alternative dimension reduction methods, this study constructs three Climate Risk Perception Indices (CRPIs) to quantify the impact of investor climate change sentiment on industry return volatility in the Chinese stock market. There are several findings in this study including (1) the CRPIs effectively capture the trend of climate change in China over the past decade. (2) The investor’s attention to climate change positively influences short-term stock market returns, exhibiting seasonal characteristics and industry heterogeneity. (3) The attention serves as an information sender for stocks in Telecommunication, Oil & Petrochemicals, and Environmental Protection. (4) The total spillover effect among climate-sensitive industries is significantly higher during extreme climate events. These conclusions provide empirical evidence for the stock market to respond effectively to climate risks.

SOCIAL MEDIA SENTIMENT AND STOCK MARKET VOLATILITY: EVIDENCE FROM THE US HI-TECH COMPANIES

Purpose: This study aims to investigate the effects of social media sentiment, measured by Twitter-based uncertainty index, on the stock market volatility of the US technology companies. Design/methodology/approach: Methodologically, we employ the quantile regression model. Our findings reveal that the volatility of Amazon, Apple, Google and IBM stocks are sensitive to the variations in twitter-based economic and market uncertainties (i.e., TEU and TMU indexes). Findings: We observe significant influences at both lower and upper quantiles. Thus, for both high and low volatility regimes, the information on twitter-based uncertainty indexes can be used to predict the market volatility of these leading hi-tech companies. Moreover, TEU and TMU indexes exert positive effects on the stock price implied volatility implying that the variance of these technology firms experiences an upward trend as the social media uncertainty rises. Originality/value: While numerous studies have focused on the influence of social media (e.g., Facebook, twitter etc.) on investment strategies, the impact of twitter sentiments on the risk linked to hi-tech firms remains understudied. Hence, investors participating in the technology sectors could use our findings for managing portfolio risk.

Exploiting News Analytics for Volatility Forecasting

ABSTRACTThis study investigates the potential of news sentiment in predicting stock market volatility. We augment traditional time series models of realized volatility with the sentiment of macroeconomic and firm‐specific news. Our results demonstrate that incorporating the sentiment of domestic macroeconomic news significantly improves volatility predictions for individual stocks and the S&P 500 Index. Notably, we find substantial enhancements in long‐horizon volatility predictions when including the sentiment of macroeconomic news in the regression models. In contrast, firm‐specific news sentiment shows only modest predictive power in the general framework. However, expanding the set of predictors to include the news count of firm‐specific news occurring overnight between two consecutive trading periods significantly improves one‐period‐ahead volatility forecasts.JEL Classification: C53, C55, C58, G14, G17

The spillover and contagion effects of sovereign risk on stock markets

This study examines the spillover effects of sovereign risk on international stock markets using a dynamic spatial Durbin model (SDM) on a panel of forty countries from 2009Q1 to 2024Q2. The findings show that an increase in sovereign risk, as measured by CDS spreads, leads to a significant decrease in both local and foreign stock prices, with 0.027 percent reductions across foreign markets on average. These effects are transmitted through geographical, and economic and financial channels, with exchange rates, gross fixed capital formation, and industrial production playing crucial roles in influencing stock market performance. Additionally, the negative (positive) spillover effects from rising gross fixed capital formation and sovereign risk (industrial production) demonstrate how foreign markets are more impacted than domestic ones. The study highlights the interconnected nature of global financial markets, reinforcing the need for coordinated international policy responses to mitigate the transmission of sovereign risk across borders. These findings provide new insights into how global economic factors influence stock market volatility, particularly through specific transmission channels, underscoring the importance of multilateral collaboration in managing sovereign risk.

Stock market volatility and commodity prices

Stock market volatility and commodity prices

Does index options trading destabilize Indian stock market volatility: an application of ARCH and GARCH models

Does index options trading destabilize Indian stock market volatility: an application of ARCH and GARCH models

Estimating probability of default via delinquencies? Evidence from European P2P lending market

The unprecedented growth of the financial sector's digital transformation opens wide areas to the scaling up of finance in innovative and knowledge-based projects. Improving risk management takes centre stage in the acceleration of this process. This study uses loan-book data from the peer-to-peer (P2P) lending market to empirically investigate the determinants of default risk. Using the loan-book database covering the period from 2014 to 2020, we examine multiple factors related to the default risk of loans issued by P2P lending platforms. The results indicate that a higher interest rate and higher stock market returns increase the probability of default in the P2P lending market. Results are robust to additional tests based on endogeneity correction, the LASSO method and sampling bias. The severity of the impact of market returns and interest rates is found to be significantly different based on the levels of financial technology (FinTech) adoption and banking sector distress. Increases in the market interest rate are found to boost the sensitivity of P2P loan defaults to stock market volatility. This study contributes to existing literature on risk management models with its consideration of country-specific factors, paving the way to future best practices in the market.

Cross-Market Volatility Dynamics Among Crude Oil and South Asian Emerging Markets During the COVID-19 Pandemic.

The COVID-19 pandemic had profound impacts on the global economy, initially triggering a severe economic downturn due to widespread lockdowns, this outbreak generated various economic shocks, a major one being the oil price crash. A brief understanding of how this pandemic is affecting the financial markets, empirical analysis is done on three financial markets. Three stock exchange markets are selected from emerging markets in South Asia (Pakistan, China, and India). The world crude oil market is selected as a reference, to how it transmits volatility to South Asian markets during the COVID-19 period. We employ a bivariate BEKK-GARCH model for analyzing the volatility spillover from November 19, 2019, to March 31, 2021. All markets show negative coefficients with the oil market, indicating that the previous day’s shocks in oil prices reduce today's stock market volatility, with significant values for India and Pakistan. China has a negative coefficient while the rest of the two markets show a positive and significant coefficient means past volatilities in the stock market decrease today’s oil market volatility, likely indicating a reactive or adaptive market behavior to stock volatility. Generally, oil price changes have a significant impact on these markets, with both immediate effects (via ARCH terms) and persistent effects (via GARCH terms). The study's findings offer valuable insights for policymakers, as they help identify the direction of spillovers among interconnected economies. Additionally, portfolio investors can use this information to effectively allocate weights to different asset classes when making investment decisions.

Stock market volatility and multi-scale positive and negative bubbles

We study whether booms and busts in the stock market of the United States (US) drives its volatility. Given this, first, we employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. We successfully detect major crashes and rallies during the weekly period from January 1973 to December 2020. Second, we utilize a nonparametric causality-in-quantiles approach to analyze the predictive impact of our bubble indicators on daily data-based weekly realized volatility (RV). This econometric framework allows us to circumvent potential misspecification due to nonlinearity and instability, rendering the results of weak causal influence derived from a linear framework invalid. The MS-LPPLS-CIs reveal strong evidence of predictability for RV over its entire conditional distribution. We observe relatively stronger impacts for the positive bubbles indicators, with our findings being robust to an alternative metric of volatility, namely squared returns, and weekly realized volatilities derived from 5 (RV5)- and 10 (RV10)-minutes interval intraday data. Furthermore, we detect evidence of predictability for RV5 and RV10 of nine other developed and emerging stock markets. In addition, we also find strong evidence of causal feedbacks from RV5 and RV10 on to the MS-LPPLS-CIs of the 10 countries considered. Finally, time-varying connectedness of the RVs of the G7 stock markets is also shown to be strongly (positively) predicted by the connectedness of the six bubbles indicators. Our findings have significant implications for investors and policymakers.

Technological Shocks and Stock Market Volatility Over a Century

This paper provides a novel perspective on the innovation-stock market nexus by examining the predictive relationship between technological shocks and stock market volatility using data over a period of more than 140 years. Utilizing annual patent data for the U.S. and a large set of economies to create proxies for local and global technological shocks and a mixed-sampling data (MIDAS) framework, we present robust evidence that technological shocks capture significant predictive information regarding future realizations of stock market volatility, both in- and out-of-sample and at both the short and long forecast horizons. Further economic analysis shows that investment portfolios created by the volatility forecasts obtained from the forecasting models that incorporate technological shocks as predictors in volatility models experience significantly lower return volatility in the out-of-sample horizons, which in turn helps to improve the risk-return profile of those portfolios. Our findings present a novel take on the nexus between technological innovations and stock market dynamics and pave the way for several interesting avenues for future research regarding the role of technological innovations on asset pricing tests and portfolio models.

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.

Impact of crude oil price innovations on global stock market volatility: Evidence across time and space

This study investigates the impact of crude oil price innovations on global stock market volatility through a ripple-spreading network model, incorporating four dimensions of crude oil price changes: realized volatility, implied volatility, variance risk premium, and realized skewness volatility. Additionally, we assess the effects of three types of crude oil shocks—oil-specific, aggregate demand, and oil supply shocks. The results indicate that while all four dimensions exhibit similar temporal diffusion patterns, their spatial impacts differ. Global stock markets demonstrate heightened sensitivity to implied volatility and variance risk premium, followed by realized volatility and, lastly, realized skewness volatility. Moreover, we find that realized volatility spreads through multiple transmission pathways, albeit at a slower pace compared to implied volatility and the variance risk premium. Among the crude oil shocks, oil-specific shock induces the most rapid volatility transmission across global markets, with aggregate demand shock following and oil supply shock exerting the smallest influence.

Forecasting of the Korean Stock Market Volatility Index Using a Modified Multi-Input LSTM Model

Forecasting of the Korean Stock Market Volatility Index Using a Modified Multi-Input LSTM Model

Partisan conflict and stock volatility predictability

ABSTRACT This study investigates the impact of the Partisan Conflict Index (PCI) on stock market volatility within the United States. Results indicate that the PCI is a robust predictor of both in-sample and out-of-sample stock volatility across the aggregate market and most sector-specific markets. Interestingly, the predictive strength of the PCI is pronounced for the aggregate market index and the sectors of utilities, materials, industrials, information technology, energy, telecom services, and consumer discretionary during periods of Republican governance. In contrast, under Democratic governance, the PCI demonstrates significant predictive power in the health care, consumer staples, information technology, and telecom services sectors. These findings contribute novel insights into the stock volatility predictability.

A Study of Stock Market Volatility Prediction Based on Traditional Regression and Intelligent Algorithms

China's stock market has been turbulent since the reform and opening up, often with the risk of plummeting, and in recent years it has been even more volatile, while the national level's concern for stock market stability is increasing day by day, and research on the stability of China's stock market is imminent. In this paper, on the basis of previous research and traditional macro fundamental analysis, firstly, we use LASSO regression analysis to initially determine the influence indicators that are causally related to stock market stability, and then test the significance of the model coefficients, and amend the original model by using multivariate linear regression, to further screen out the significant influencing factors; secondly, we use support vector machine regression (SVM) and random forest regression to fit the stock market volatility in the machine learning method. regression to fit the stock market volatility, determine the importance ratio of different characteristic variables in the model, and analyse the factors affecting the prediction of stock market volatility; finally, the LASSO regression is combined with machine learning to establish an improved model, and the screened indicator factors are fitted with machine learning models to further deepen the prediction of stock market volatility. Stock market oscillations and volatility are further deepened to measure the stability of the Chinese stock market.

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.

The economic consequences of misinformation an analysis of the impact of fake news on stock market volatility during the covid19 pandemic| International Journal of Innovative Science and Research Technology

This paper examines the economic consequences of misinformation on stock market volatility during the COVID-19 pandemic, highlighting how false information significantly disrupted financial markets. The analysis explores specific high-profile cases where misinformation about vaccines, lockdowns, and treatments led to increased market volatility, panic selling, and shifts in investor behaviour. The study delves into the effects on major indices such as the S&P 500 and Dow Jones, revealing the substantial financial losses experienced by retail and institutional investors. It also discusses the regulatory and institutional responses from financial authorities and social media platforms, as well as the challenges they face in curbing misinformation’s rapid spread. The paper concludes with recommendations for enhancing market resilience, emphasising the importance of media literacy, robust fact-checking, and proactive regulatory frameworks to mitigate the impact of misinformation in future crises. This study underscores the ongoing need for vigilant market practices and improved information governance to maintain economic stability.

Financialization and Stock Volatility of Low-/High-Tech Manufacturing Firms in China: The Moderating Role of M&A

ABSTRACT In recent years, the degree of financialization of manufacturing firms may be one of the key factors in the “real to virtual” transition of the Chinese economy. Using data on Chinese listed firms in the low-tech manufacturing sector over the period 2013–2018, this paper examines the relationship between financialization and stock market volatility, and how mergers and acquisitions (M&A) play a moderating role in this relationship. The results show that there is a positive relationship between financialization and stock market volatility, while M&A activity has a negative moderating effect on the relationship. The positive effect of financialization on stock volatility is more pronounced for high-tech firms than for low-tech firms, while the negative moderating effect of M&A on stock volatility is more pronounced for low-tech firms than for high-tech firms. The link between the financialization of high-tech firms and their volatility has been strengthened with the implementation of the Made in China 2025 program. The main conclusions are stabilized after fully accounting for multicollinearity and R&D capital investment. For high-tech firms, financialization has a more pronounced impact on the volatility of non-state-owned firms compared to state-owned firms (SOEs).

Only strong short-term contrarian effect exists in Chinese stock market: The role of the T+1 trading mechanism

We use an alternative sorting method which groups stocks into return intervals rather than percentiles to test the relationship between the T+1 trading mechanism and the short-term contrarian effect in the China's A-share market. Our sample period is from January 2010 to December 2020. China's stock market exhibits a significant contrarian effect in daily, weekly, and monthly frequencies, while the momentum effect is not significant. This paper argues that the special T+1 trading mechanism of China's stock market is the reason for this typical anomaly. The essence of the T+1 trading mechanism is a marketability put option, and this paper takes the volatility as the core of the analysis. This study shows that stock market volatility has a significant impact on the overnight return, with higher volatility leading to a higher discount. Through different daily, weekly, and monthly frequency groupings, the study finds that the winner's portfolio would reverse significantly in the next period. Under the T+1 mechanism, the high return stock portfolio is also characterized by high volatility and high turnover. The contrarian effect of China's stock market is unique in that both the winner's portfolio and the loser's portfolio will fall, but the winner's portfolio will fall more. The reversal of the winner's portfolio dominates in the entire market. The paper provides a new explanation for the contrarian effect and adds a unique explanation to the study of worldwide contrarian effect.

COVID-19 and Uncertainty Effects on Tunisian Stock Market Volatility: Insights from GJR-GARCH, Wavelet Coherence, and ARDL

This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended.