Stock Market Volatility Research Articles

Shadow Banking, Stock Market Volatility, and Stock-Money Market Correlation: Evidence from China

ABSTRACT The study on the relationship between shadow banking (SB) and stock market volatility is scarce. Based on sample data from January 2006 to May 2024, this paper dives deep to clarify whether and how China’s SB brings uncertainties to China’s stock market and the impact of SB on the stock-money market correlation. The novelty of this paper is that we connect low-frequency SB with high-frequency financial market information under the framework of mixed-frequency data analysis. Our findings indicate that the expansion of SB could directly enhance China’s stock market volatility. There exists a persistent correlation between China’s stock and money markets in the long run. Furthermore, the rapid development of SB also strengthens the stock-money market correlation in the long run. Since SB could reduce the controllability of China’s monetary policy and increase China’s money market uncertainties, we believe that China’s money market may serve as an important channel for SB to spread risks to the stock market volatility by pushing the two markets to move more closely. We shed new light on the literature regarding the relationship between SB and stock market. This paper makes the first attempt to identify the role of SB in driving the dynamics of stock-money market correlation.

Can gold- and USD-backed stablecoins hedge against US stock market volatility? Evidence from a quantile coherency approach

ABSTRACT Emerging markets are increasingly appealing to market participants seeking alternative investments. Using the Quantile Coherency method, this study investigates the interrelatedness between stablecoins backed by two types of traditional assets (gold and US dollars) and the volatility indices of the US stock market across different frequency domains and market conditions. The potential of gold- and USD-backed stablecoins to hedge stock market volatility varies. There is a stronger connection under long-term and extreme conditions than in the short-/medium- and normal markets. The empirical results suggest that the USD-backed stablecoins should be used as hedging assets against US stock market volatility (typically showing a non-negative correlation) rather than the gold-backed stablecoins (which often exhibit a negative correlation). These findings provide valuable insights for market participants and academic research.

A Comprehensive Study of Stock Market Volatility: Types, Determinants, and Measurement Methods

The Volatility, a critical aspect of financial markets, quantifies the uncertainty associated with asset price movements. This paper provides a comprehensive review of volatility, encompassing its definition, types, determinants, and measurement methods. Various factors, including macroeconomic indicators, market sentiment, liquidity, global events, and corporate actions, influence volatility. A range of methods, such as standard deviation, historical volatility, implied volatility, GARCH models, EWMA, and range-based volatility, are employed to measure volatility. Understanding volatility is essential for investors, traders, and risk managers to make informed decisions and manage risk effectively. Future research can delve into advanced volatility models, volatility spillovers, investor behavior, and the relationship between volatility and asset pricing.

Forecasting Chinese Stock Market Volatility With Volatilities in Bond Markets

ABSTRACTIn this paper, we investigate whether the bond markets contain important information that can improve the accuracy of stock market volatility forecasts in China. We use realized volatility (RV) implemented by different maturity treasury bond futures contracts to predict the Chinese stock market volatility. Our work is based on the heterogeneous autoregressive (HAR) framework. Empirical results show that the volatility of treasury bond contracts with longer maturities (especially 10 years) has the best effect on predicting the Chinese stock market volatility, both in sample and out of sample. Two machine learning methods, the scaled principal component analysis (SPCA) and the least absolute shrinkage and selection operator (lasso), are also more effective than the HAR benchmark model's prediction. Finally, mean–variance investors can achieve substantial economic gains by allocating their investment portfolios based on volatility forecasts after introducing treasury bond futures volatility.

Geopolitical Risks and Stock Market Volatility in the SAARC Region

Abstract This study examined the stock market volatility of the member states of the South Asian Association for Regional Cooperation (SAARC) and geopolitical risk (GPR). The analysis period covered January 2014 to March 2024, and the time–frequency wavelet method was used to process the data. The time-varying parameter vector autoregression and spatial autoregressive models helped determine the dynamic connectedness of volatility in the analyzed states. The findings revealed similar stock market connections in Bangladesh and India. In addition, a comparative analysis of stocks in India and Pakistan led to the identification of common elements. The connection between geopolitical concerns and Sri Lankan stocks was the strongest and increased in intensity after 2019. GPR and Nepal’s stock market maintained a continuous but low-intensity relationship. The dynamic connectedness between member states’ stock markets was limited during the review period. The study results could encourage SAARC governments to bridge their political differences to ensure that South Asia becomes a strong partner in the global economy. Equally, our results can benefit investors, financial institutions, regulatory authorities, and governments.

Assessing the Effects of Cyber Attacks on Financial Markets

Cyber-attacks on financial markets have emerged as a significant threat to global economic stability, disrupting market operations, compromising sensitive data, and undermining investor confidence. This study explores the multifaceted impact of cyber-attacks on financial markets, examining both the immediate and long-term effects on market performance, regulatory response, and overall market resilience. By analyzing recent incidents, this research highlights how cyber-attacks influence stock prices, trading volumes, and market volatility, shedding light on the vulnerabilities within financial systems. Findings suggest that cyber threats not only impact individual institutions but also ripple through interconnected markets, amplifying financial risk on a broader scale. The study concludes with recommendations for strengthening cybersecurity measures in financial markets to enhance resilience and mitigate potential losses from future cyber incidents.

Does high volatility increase connectedness? A study of Asian equity markets

Financial market integration metrics based upon variance decompositions from vector autoregression (VAR) models have become quite popular, and prior literature shows how these metrics change over time. However, only a few studies explain what is driving connectedness to change. Using stock market volatilities from eight Asian equity markets, we postulate that volatility may be a driving force behind increased connectedness. We use a two-step VAR estimation procedure to analyze the dynamics between equity market volatility and connectedness. The first step estimates the VAR model over rolling windows; this yields a sequence of connectedness-measuring spillover indices. The second step re-estimates the VAR with the spillover index sequence as an endogenous variable. Impulse response analysis from the two-step procedure confirms that volatility shocks increase connectedness. The two-step procedure indicates that volatility shocks from Singapore, Hong Kong, and China affect connectedness the most, whereas Malaysia affects it the least. China’s direct effect on the volatilities of the other markets is small. However, since China has a considerable impact on connectedness, China may have an important indirect effect on the other markets by increasing the speed and ease with which volatility spreads from market to market.

Monetary Policy Instruments and Stock Market Returns Volatility in Nigeria

The study examines monetary policies instruments impact on stock market volatility in Nigeria for the period of 1993-2022. The study identified the independent variables, namely; reserve requirement (RR), cash reserve ratio (CRR), discount rate (DR) and money supply (MS) which were analyzed in relation to stock market volatility proxied with all share index volatility (ASIV). The data was analyzed with descriptive statistics, correlation matrix several diagnostics tests (VIF, validity test, ADF and Johansen tests) and the multiple regression analysis. The findings revealed that RR, CRR and MS have significant effect on ASIV while DR has insignificant effect on ASIV in Nigeria. It was concluded that reserve requirement, discount rate and money supply exert significant effect on Nigerian economy. Thus, it thereby recommended that management of CBN should put modalities in place to aggressively control ASIV in Nigeria. CBN should put measures in place to increase CRR. This will provide banks opportunity to fund economic transaction in the Nigerian economy, thereby reducing ASIV.

Analysis of Portfolio Optimization Performance: Markowitz Model and Index Model in Capital Markets

In the post-pandemic era, marked by economic uncertainty and stock market volatility, investors are turning to Modern Portfolio Theory (MPT) to guide their investment decisions. This study aims to develop a robust mathematical framework for portfolio construction using the Markowitz Model (MM) and the Index Model (IM) with added constraints. The objectives are threefold: (1) to create a portfolio framework that reflects investor preferences using MM and IM with additional constraints, (2) to compare these models against the Gaussian Distribution using Python and Excel, (3) to compare the statistical data and correlation tests of daily logarithmic returns in Python with monthly excess returns in Excel, and (4) to evaluate their performance relative to the traditional Markowitz approach through Monte Carlo simulations.The methodology involves incorporating five additional constraints into the MM and IM models. The analysis uses 20 years of historical daily return data for ten stocks from various sectors, one equity index (S&P 500) as a risk-free rate proxy (1-month Fed Funds rate). Daily logarithmic returns are analyzed using Python, while Excel Solver and Solver Table are employed for monthly excess return data.

Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach

This study comprehensively investigates stock market volatility based on over one hundred monthly macroeconomic variables, applying machine learning models. Methodological contribution integrating the random forest (RF) with the least absolute shrinkage and selection operator methods (LASSO). Importantly, the RF-LASSO model can robustly achieve the best forecasting performance under different circumstances. In addition, we focus on model explanation from different perspectives based on permutation importance and shapley additive explanation (SHAP) methods. This study illuminates novel insights into the realm of stock market volatility, harnessing the transformative potential of machine learning methodologies.

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.

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.

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.