Stock Market Volatility Research Articles

Understanding the Dynamics of Oil Price Shocks and Stock Market Volatility in Pakistan: A Non-Linear Approach during COVID-19

Purpose: This article examines the complex relationship between oil price shocks and stock market volatility in Pakistan during the COVID-19 outbreak. Design/Methodology/Approach: The study used a Non-Linear Autoregressive Distributed Lag (NARDL) model to meticulously explore how oil price fluctuations, specifically supply and demand shocks, affect Pakistan's stock market. Monthly data sets from 2020 to 2022 were utilized for the analysis. Research Findings: The study's significant findings reveal that oil price shocks, except for a demand shock specific to the oil industry, have varying long- and short-term effects on stock market volatility, leading to asymmetry. This asymmetrical impact of oil supply and demand shocks on stock market volatility is confirmed, with no asymmetry observed from oil-specific demand shocks in the short term. These findings hold crucial implications for policymakers and investors in understanding and managing the economic dynamics of Pakistan post-COVID-19. Theoretical Contribution/Originality: This research stands out for its originality in understanding Pakistan's economic dynamics post-COVID-19. It provides unique insights for policymakers and investors to navigate future uncertainties related to oil price changes and market volatility, thereby sparking new avenues of thought and action in the field. Implications/Originality/Value: The practical implications of this study's findings empower policymakers and investors to make informed decisions to stabilize Pakistan’s economy after the pandemic. Specifically, the research provides guidance on managing the behaviour of stakeholders and banks in response to oil shocks, thereby contributing to the economic resilience of the country.

What causes US equity market sector volatilities during the coronavirus pandemic?

ABSTRACT This paper seeks to identify key variables contributing to sectoral stock market volatilities in the US under the enduring pressure of the COVID-19 pandemic, using a broad array of candidate factors. We adopt a Beta-Skew-t-EGARCH model to capture the time-varying dynamics of the individual sectoral return volatilities. The empirical analysis is performed via an elastic-net regularized regression model. We find that trading volume, volatility of broad US dollar exchange rates, coronavirus infection rates, VIX, Google search trends, US economic policy uncertainty, and the initiation of vaccination programmes are the most common determinants of sectoral volatility.

Code and Data Repository for Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility

Code and Data Repository for Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility

Volatility Spillover from Carbon Prices to Stock Prices: Evidence from China’s Carbon Emission Trading Markets

The carbon emission trading markets represent an emerging domain within China. The primary objective of this study is to explore whether carbon price volatility influences stock market volatility among companies subject to these emission trading regulations. Employing daily returns data from 293 publicly traded companies regulated by these emission trading markets, this study encompasses the national carbon market and eight pilot regional carbon markets spanning from August 2013 to October 2023. The results demonstrate that volatility in regional carbon prices positively impacts the stock volatility of companies in the corresponding emission trading region, indicating a volatility spillover effect. Moreover, this spillover effect is more pronounced in sectors marked by lesser carbon intensity than those with greater carbon intensity. The volatility transmission is more pronounced in coastal areas than in inland regions. However, no notable distinctions in volatility transmission are discerned between the periods before and throughout the COVID-19 pandemic. Vector autoregression analyses substantiate that lagged carbon price fluctuations possess limited predictive capacity for contemporaneous equity market volatility and vice versa. The robustness of these outcomes is fortified by applying the E-GARCH model, which accounts for the volatility clustering phenomenon. As the first investigation into the volatility spillover effect between China’s emission trading market and corresponding stock markets, this study offers valuable insights into the investment strategies of retail investors, the formulation of carbon regulations by policymakers, and the carbon emission strategies of corporate managers.

Let the Laser Beam Connect the Dots: Forecasting and Narrating Stock Market Volatility

Forecasting market volatility, especially high-volatility incidents, is a critical issue in financial market research and practice. Business news as an important source of market information is often exploited by artificial intelligence–based volatility forecasting models. Computationally, deep learning architectures, such as recurrent neural networks, on extremely long input sequences remain infeasible because of time complexity and memory limitations. Meanwhile, understanding the inner workings of deep neural networks is challenging because of the largely black box nature of large neural networks. In this work, we address the first challenge by proposing a long- and short-term memory retrieval (LASER) architecture with flexible memory and horizon configurations to forecast market volatility. Then, we tackle the interpretability issue by devising a BEAM algorithm that leverages a large pretrained language model (GPT-2). It generates human-readable narratives verbalizing the evidence leading to the model prediction. Experiments on a Wall Street Journal news data set demonstrate the superior performance of our proposed LASER-BEAM pipeline in predicting high-volatility market scenarios and generating high-quality narratives compared with existing methods in the literature. History: Accepted by Ram Ramesh, Area Editor for Date Science & Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0055 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0055 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Geopolitical risks, investor sentiment and industry stock market volatility in China: Evidence from a quantile regression approach

From an industry perspective, we apply the quantile regression to investigate the impact of investor sentiment (IS) and China’s/U.S. geopolitical risks (GPR) on Chinese stock market volatility. Considering the structural break of the stock market, we find that China’s and U.S. GPR/IS and their interaction effects have no significant impact on China’s stock market volatility at the market level. However, there has an asymmetric dependence between China’s and U.S. GPR/IS and stock market volatility, and the dependence structure is changing. At the industry level, the impact of geopolitical risk on industry stock market volatility is highly heterogeneous, and its significance mostly occurs in the upper and lower tails. Second, China’s and U.S. GPR/IS can exacerbate industry stock market volatility in bullish and bearish markets. In addition, China’s and U.S. GPR/IS and their interaction effects are heterogeneous and asymmetric, and the effects changes with the break point. Finally, compared with China’s GPR, the U.S. GPR has a larger impact on the industry stock market. The interactive effects of the U.S. GPR and IS can influence more industry stock market volatility.

An Empirical Investigation on the Stock Market Volatility using TGARCH Model with Special Reference to BRIC Countries

An Empirical Investigation on the Stock Market Volatility using TGARCH Model with Special Reference to BRIC Countries

Behavioral finance impacts on US stock market volatility: an analysis of market anomalies

Abstract This study investigates the impacts of behavioral finance on stock market volatility. The primary aims are to explain the reasons behind changes in the S&P 500 price within the context of behavioral finance and to analyze investor behavior in response to these changes. To achieve this, the research employs time-series analysis over a 10-year period, focusing on the S&P 500, real interest rates, consumer confidence, market volatility and credit default swaps while considering the effects of behavioral biases. The findings reveal several significant correlations: rising real interest rates negatively affect stocks due to loss aversion and sentiment. Conversely, higher consumer confidence tends to positively influence the stock market, driven by herding behavior and optimism. Additionally, market volatility shows a negative correlation with the S&P 500, influenced by risk aversion, recency bias and herding behavior. Moreover, an increase in credit default swap rates leads to stock market declines, primarily influenced by risk perception, loss aversion and herding behavior.

Nonlinear behavior of tail risk resonance and early warning: Insight from global energy stock markets

This article calculates the tail risk of the global energy market and explores the nonlinear behavior of tail risk resonance at the global, regional and national levels from a dynamic perspective. Moreover, we apply various machine learning models to perform the experimental training on the drivers that affect tail risk resonance to establish a risk warning system. By simulation training and evaluation, we screen the optimal risk warning model and identify the important drivers of risk resonance in the energy market. The findings indicate that the energy system has a strong tail risk resonance phenomenon, and the occurrence of extreme events as well as a collapse in oil prices can significantly increase the system's risk resonance. The European region is the dominant risk emitter and the American region is the main risk receiver. The Russian-Ukrainian war significantly increased the risk resonance across regions. The Argentine energy market is the main receiver of tail risk and the US energy market is the primary sender of tail risk. By incorporating the risk factors into several machine learning models, the Random Forest model is found to provide effective early warning of tail risks. Additionally, importance analysis reveals that inflation levels, stock market volatility and trade policy changes are important factors affecting risk resonance in the energy market and have nonlinear effects on the risk resonance of the energy system.

Energy price shocks and stock market volatility in an energy-importing country

We examine volatility dynamics in the Korean market using heterogeneous autoregressive models with exogenous covariates. The COVID-19 pandemic and the Russia–Ukraine War have caused substantial fluctuations in energy prices. We assess how these energy shocks affect stock market-implied volatility in Korea, a representative energy-importing country. During the pre-pandemic period, domestic and U.S. market factors affect Korea's volatility dynamics, whereas crude oil and natural gas futures prices have little explanatory power for these dynamics. In contrast, during the pandemic (war), oil (natural gas) futures prices become the key explanatory variable, and other market factors lose their explanatory power.

Systemic risk prediction using machine learning: Does network connectedness help prediction?

The global financial crisis not only highlights the important role of financial network connectedness, but also urges regulators pay more attention to systemic risk. We study whether network connectedness helps systemic risk prediction by using machine learning techniques, including k-nearest neighbor (KNN), support vector regression (SVR), extreme gradient boosting (XGB), and feedforward neural network (FNN). Based on prediction results, we use the fingerprint model to deliver interpretability evaluations of financial variables and network connectedness. We find that (1) the linear and nonlinear effects of stock market volatility and network connectedness effectively interpret systemic risk; (2) the interaction effects between stock market volatility and other drivers have a stronger ability to predict system risk in SVR, KNN, and XGB approaches, while network connectedness performs more profoundly in FNN approach; and (3) the interaction effect between stock market volatility and network connectedness is evident in all approaches.

Impact of Macroeconomic Variables on Stock Market Volatility in Kenya

Purpose: The aim of the study was to assess the impact of macroeconomic variables on stock market volatility in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The impact of macroeconomic variables on stock market volatility in Kenya reveals several key findings. Firstly, studies suggest that variables such as inflation rate, exchange rate fluctuations, and interest rates significantly influence stock market volatility in the country. Specifically, an increase in inflation tends to amplify stock market volatility, while fluctuations in exchange rates contribute to increased volatility, especially in the short term. Additionally, interest rate movements affect stock market volatility, with lower interest rates generally associated with higher stock market volatility. Furthermore, factors like GDP growth rate and oil price fluctuations also exhibit varying degrees of influence on stock market volatility in Kenya. Implications to Theory, Practice and Policy: Efficient market hypothesis, behavioral Finance Theory and Asset Pricing Models may be use to anchor future studies on assessing the impact of macroeconomic variables on stock market volatility in Kenya. Investors can benefit from a deeper understanding of how macroeconomic variables impact stock market volatility by incorporating this knowledge into their risk management strategies. Policymakers should consider the implications of macroeconomic policies on stock market volatility when formulating economic strategies.

Exploring the Impact of China's Internal Circulation Strategy on Its Stock Market under Deglobalization

Abstract In May 2020, China formulated the Internal Circulation Strategy (ICS) to address the risks of global economic downturns caused by deglobalization. This study is the first to empirically examine the impacts of China's ICS on its stock market performance, focusing on investor behavior. Using data from the Baidu search index and the Shanghai and Shenzhen stock exchanges for the period 27 July 2020 to 5 May 2023, the results reveal that stock returns are negatively associated with investor attention to China's ICS, while stock volatilities are positively associated. This suggests that the ongoing and profound shift in economic strategy might have raised concerns among investors. Furthermore, our analysis of heterogeneity finds that the negative association with stock market returns is statistically significant only during times of high policy uncertainty or geopolitical risk. The positive association with stock market volatility is statistically significant only during times of high geopolitical risk. This indicates that the stability of the internal and external environment plays a crucial role in alleviating investor concerns. We also observe mixed impacts on different sectors of the stock markets, with some sectors unaffected while others primarily experiencing a decline in returns.

Forecasting the Volatility of the Russian Stock Market in the Context of International Economic Sanctions

The article is devoted to the study of trends in the development of the Russian stock market in the context of international economic sanctions. The purpose of the study is to make forecasts of the volatility of the Russian stock market using a scenario approach. Statistical data of the Moscow Stock Exchange were used for calculations. The authors have made a forecast of the volatility of the stock exchange market of the Russian Federation. The basis of the forecast calculations is the dynamics of the Moscow Exchange Index (IMOEX), taken as a key indicator of the Russian organized securities market, for the period from June 2013 to July 2022. Based on the basic historical dynamics of the Moscow Stock Exchange Index, negative (international economic sanctions are being tightened) and positive (implies the easing and/or lifting of some sanctions) scenarios for the development of the stock market of the Russian Federation are compiled. The scientific novelty is the authors’ assessment of the convergence of the volatility forecast under negative and positive scenarios to a certain level of volatility in 2023. The results of the calculations showed that under different scenarios of the situation, volatility tends to the same value at different assumed values of the Moscow Exchange Index, which allowed us to draw a new and practically significant conclusion that over time the economy of the Russian Federation stabilizes regardless of the tightening or easing of international economic sanctions — this may be due to the implementation of the country has a policy of import substitution, the formation of national production in most areas of the economy and the development of the domestic market. The work carried out by the authors contributes to the development of theoretical and applied economics in terms of making forecasts for the development of the stock market and using the results of forecasting to make economically sound decisions.

The Role of Macroeconomic Variables in Forecasting Equity Market Volatility in the East African Community Using Garch-Midas Model

This study delves into the dynamic relationship between macroeconomic variables and equity market volatility in the East African Community. The research employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model coupled with the Mixed Data Sampling (MIDAS) approach. Through a comparative process, it is found that the different macroeconomic variables exhibit heterogeneous effects on the different countries in the East African community that is macroeconomic factors significantly explain the variation in stock market volatility in Uganda and including these factors in the GARCH-MIDAS model improved its forecasting ability, however, in Kenya it was found that majority of the macroeconomic variables had insignificant effects on stock market volatility and didn’t improve the forecasting ability of the GARCH-MIDAS model.

Economic uncertainty and stock market asymmetric volatility: analysis based on the asymmetric GARCH-MIDAS model

PurposeWe aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.Design/methodology/approachWe construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.FindingsNegative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.Research limitations/implicationsMany GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.Practical implicationsOur conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.Social implicationsFirst, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.Originality/valueThis study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

Herding behaviour and sentiment: evidence from emerging markets

PurposeBased on the theoretical framework, this paper analyzes the sentiment-herding relationship in emerging stock markets (ESMs). First, it aims to examine the effect of investor sentiment on herding. Second, it seeks the direction of causality between sentiment and herding time series.Design/methodology/approachThe present study applies the Exponential Generalized Auto_Regressive Conditional Heteroskedasticity (EGARCH) model to capture the volatility clustering of herding on the financial market and to investigate the role of the investor sentiment on herding behaviour. Then the vector autoregression (VAR) estimation uses the Granger causality test to determine the direction of causality between the investor sentiment and herding. This study uses a sample consisting of stocks listed on the Shanghai Composite index (SSE) (348 stocks), the Jakarta composite index (JKSE) (118 stocks), the Mexico IPC index (14 stocks), the Russian Trading System index (RTS) (12 stocks), the Warsaw stock exchange General index (WGI) (106 stocks) and the FTSE/JSE Africa all-share index (76 stocks). The sample includes 5,020 daily observations from February 1, 2002, to March 31, 2021.FindingsThe research findings show that the sentiment has a significant negative impact on the herding behaviour pointing out that the higher the investor sentiment, the lower the herding. However, the results of the present study indicate that a higher investor sentiment conducts a higher herding behaviour during market downturns. Then the outcomes suggest that during the crisis period, the direction is one-way, from the investor sentiment to the herding behaviour.Practical implicationsThe findings may have implications for universal policies of financial regulators in EMs. We have found evidence that the Emerging investor sentiment contributes to the investor herding behaviour. Therefore, the irrational investor herding behaviour can increase the stock market volatility, and in extreme cases, it may lead to bubbles and crashes. Market regulators could implement mechanisms that can supervise the investor sentiment and predict the investor herding behaviour, so they make policies helping stabilise stock markets.Originality/valueThe originality of this paper lies in investigate the sentiment-herding relationship during the Surprime crisis and the Covid-19 epidemic in the EMs.

Week-End Anomaly and Stock Market Volatility Analysis of Indian Sustainability Indices

Week-End Anomaly and Stock Market Volatility Analysis of Indian Sustainability Indices

Stock Market Volatility Prediction Based on Robust GBM-GRU Model

In this study, a corrective learning technique is proposed to enhance the time series forecasting accuracy of traditional intelligent algorithms and address issues with engineering data adaptability. The financial world makes substantial use of volatility, and forecasting stock market volatility has great significance. Because stock price time series are nonstationary and nonlinear, predicting stock market volatility is a significant difficulty. In this study, a simulation experiment using an approach for corrective learning is carried out. Over a 3-year period, a LightGBM-GRU model is created to predict the volatility of 126 equities from diverse industries over the length of the subsequent 10 min, utilizing LightGBM and GRU as base models and correctors, respectively. Even traditional and effective ensemble learning models can benefit from the corrective learning technique, which simultaneously improves accuracy and generalization, according to performance metrics like RMSPE, MAE, MSE, and RMSE. The research emphasizes how the contradiction within intelligent algorithms has changed from one of restricted generality to different engineering difficulties in the era of algorithm enrichment and large data. The corrective learning strategy offers a fresh method for simulating data.

The Role of Macroeconomic Variables in Forecasting Equity Market Volatility in the East Aftican Community

This study delves into the dynamic relationship between macroeconomic variables and equity market volatility in the East African Community. The research employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model coupled with the Mixed Data Sampling (MIDAS) approach. Through a comparative process, it is found that the different macroeconomic variables exhibit heterogeneous effects on the different countries in the East African community that is macroeconomic factors significantly explain the variation in stock market volatility in Uganda and including these factors in the GARCH MIDAS model improved its forecasting ability, however, for Kenya it was found that majority of the macroeconomic variables had insignificant effects on stock market volatility and didn’t improve the forecasting ability of the GARCHMIDAS model.