Stock Market Volatility Research Articles

Conditional macroeconomic and stock market volatility under regime switching: Empirical evidence from Africa

<abstract> <p>We used the Markov switching regression model to establish a relationship between the conditional stock market returns and macroeconomic volatilities. Monthly data from thirteen (13) African stock markets and macroeconomic variables (exchange rate, inflation, interest rate, money supply, and crude oil price) from 2003 to 2022 were employed. We confirmed the existence of two distinct regimes: An economic expansion or a "tranquil" state with less volatility and an economic decline or a "crisis" state with high volatility. Our findings indicated that macroeconomic variables significantly affect both expansion and crisis periods. However, the estimated coefficients were more significant in a tranquil than in a crisis state. The findings of the study were consistent with macroeconomic theory and pointed out policy implications.</p> </abstract>

Industry-specific effects of economic policy uncertainty on stock market volatility: A GARCH-MIDAS approach

Industry-specific effects of economic policy uncertainty on stock market volatility: A GARCH-MIDAS approach

A Model on a Comparative Cross-Regime Analysis on Modelling and Forecasting of the Zimbabwe Stock Market Volatility Using ARCH (1), GARCH (1,1) and EGARCH (1,2) as the Extension to Account for Leverage Effects from April 2012 to April 2024

In order to account for the uncertainty underlying financial asset investments, volatility is crucial. Because of this, politicians, mutual fund managers, individual and institutional investors, and regulators of the financial industry are worried about volatility. In the context of the Zimbabwean stock market, this study aims to investigate how well three econometric models ARCH, GARCH, and EGARCH compare in terms of modelling and volatility predictions. The symmetry impact was estimated using the GARCH and ARCH models, while the asymmetric effect was captured by the EGARCH model, the third model. In light of this, this study employed daily averages for the mining and industrial indices over the period of 16 April 2012 to March 2024. This country was regarded in the literature for stock market volatility modelling and forecasting as efficient and having no volatility. The findings indicate that volatility persists, demonstrating that it takes time for the market to properly assimilate information into pricing and that shocks to conditional variance take longer to fade. Additionally, there is an asymmetry, which suggests that positive and negative news have distinct effects on the stock market and that positive news causes more volatility than negative news. Thus, we draw the conclusion that equally good and negative news have greater effects. During the epidemic, investors rely more on the good news to make wise judgments that increase their profits. Taking the outcomes into account, any measure meant to lessen the pandemic’s effects is beneficial for investment.

Time-varying effects of economic policy uncertainty on the volatility of China's stock market: Based on TVP-VAR Model

Time-varying effects of economic policy uncertainty on the volatility of China's stock market: Based on TVP-VAR Model

Structural change, information asymmetry and volatility in Indian stock market: evidence from pre-and-post-COVID-19 outbreak

Structural change, information asymmetry and volatility in Indian stock market: evidence from pre-and-post-COVID-19 outbreak

Pandemic's Ripple Effect: Exploring Dynamic Connectedness of Indian Equity and Commodity Markets

This research investigates volatility spillovers between the Indian equity markets and commodity futures markets. The Nifty 50 index is chosen as a representative of the equity market, based on market capitalization, while Crude Oil, Copper, Zinc, Gold, and Silver are selected to represent the commodity market in India. The selection of each commodity is grounded in considerations of liquidity and physical market size. This research investigates how the volatility in the Indian stock market interacted with the volatility of five major commodity markets throughout the COVID-19 pandemic. In order to ascertain the direction of information flow, the study employs the Granger causality approach. The research investigates how volatility transmits from the Indian equity market to various commodity markets. Employing methodologies inspired by Diebold and Yilmaz (2012) for Time Varying Parameter and Vector Autoregression (TVP VAR) analysis, the study explores these connections. To further delve into the dynamic relationship between these markets, wavelet coherence analysis is also utilized. This methodologically robust approach goes beyond traditional correlation analysis by deconstructing the co-movements across different time scales, offering a more nuanced understanding of the complex relationships and potential transmission mechanisms at play during the unprecedented period of the COVID-19 pandemic. By analyzing the frequency-dependent interactions between these markets, the study aims to shed light on how economic shocks and market fluctuations in one sector might propagate and impact the other, providing valuable insights for investors, policymakers, and researchers alike.

Addressing stock market time series trends and volatility using optimised DE-LSTM model

Addressing stock market time series trends and volatility using optimised DE-LSTM model

Understanding rate of return dynamics of cryptocurrencies: an experimental campaign

In recent years, cryptocurrencies have been considered as an asset by public investors and received much research attention. It is a volatile asset, thus predicting its prices is not easy due to the dependence on multiple external factors. Machine learning models are becoming popular for cryptocurrency price predictions, while also considering social media data. In this article, we analyze the rate of return of three cryptocurrencies (Bitcoin, Ether, Binance) from an investor point of view. We also consider three traditional external variables: S&P 500 stock market index, gold price, and volatility index. The rate of return prediction is based on three stages. First, we analyze the correlation between the cryptocurrency returns and the traditional external variables. Next, we focus on the influential social media variables (from Twitter, Reddit, and Wikipedia). Later, we use these variables to improve prediction accuracy. Third, we test how the standard time series models (such as ARIMA and SARIMA) and four machine learning models (such as RNN, LSTM, GRU and Bi-LSTM) predict one-day rate of return. Finally, we also analyze the risk of investing in each cryptocurrencies using value risk statistics. Overall, our result shows no correlation between cryptocurrency returns and three traditional external variables. Second, we found that overall LSTM model is the best, GRU is the second-best prediction model, while the impact of the social media variables varies depending on the cryptocurrencies. Finally, we also found that investment in gold offers better returns than cryptocurrency during Covid-19-like situations.

The Impact of the Fed’s Monetary Policy in 2022 on China’s Stock Market: Evidence from SSEC and SZSE

International financial markets have been abuzz with worry over the Fed’s interest rate rise strategy. The Fed’s interest rate raises policy’s implementation will have wide-ranging effects, particularly for an emerging market nation like China. The influence of the Fed’s interest rate hike policy on the Chinese market will be examined in this essay in several different ways, including stock market yields, volatility, economic growth, and market expectations. The results of this study imply that the Chinese stock market has been negatively affected over the long term by the Fed’s interest rate hike. In particular, the returns of the Shanghai Securities Composite Index and Shenzhen Securities Composite Index were significantly negatively impacted by the Fed’s rate hike, with the SZSE index being more adversely impacted. Additionally, this study discovered that the Fed’s interest rate hike had no effect on the SZSE Index’s market volatility throughout the study period but had a considerable impact on the market volatility of the SSEC Index. This implies that a Fed rate hike may result in greater SSEC Index market volatility while having a negligible effect on SSEC Index market volatility. In order to stabilize market mood and reduce risks, the government and authorities should closely monitor any potential effects of the Fed’s interest rate hike on the Chinese stock market. To adjust to the evolving global financial climate, investors should carefully evaluate market risks and develop prudent investment strategies.

The impact of COVID-19 on stock markets: A systematic literature review

This research investigates the disruptive impact of COVID-19 on the financial performance of stock markets in the United States of America (USA), Asia (China, Japan, South Korea, India), and the Middle East and North Africa (MENA) region context being the sample region. The research aim is to reveal the impact of COVID-19 on stock market returns in the sample regions. prior research suggests that 74 percent (Rakshit & Neog, 2022) of the global stock markets reported negative stock returns, increased volatility, and cumulative abnormal returns during the pandemic. The research considers an empirical approach that employs event studies, panel data regression models, and paired sample t-tests. In so doing this research considers the objective impact of the COVID-19 phenomenon. The results suggest that only 5 percent suggested stock returns indicated positive effects on stock market return and volatility, while 21 percent indicated mixed positive and negative impacts. The pandemic led to negative stock market returns in the sample region. The research reveals the negative impact was primarily due to government intervention in stock markets. The research adds support to current findings that encourage governments to swiftly adopt proactive measures to minimize the disruptive impact of stock markets.

Examining the Moderating Role of the Automated Trading System on the Financial Performance of the Ghana Stock Exchange

The study investigates the role of the automated trading system on stock market capitalization, liquidity, efficiency, returns and volatility on the Ghana stock exchange. The project solely focus on secondary data from the internet databases of the Ghana Stock Exchange (GSE), and Bank of Ghana (BOG). The data is evaluated using a multivariate regression approach and includes monthly time series data from 2000 to 2020 as well as the volume of stock traded before and after the exchange is automated. The study finds that market performance has an impact on the automated trading system, and it also finds that the amount of stock traded is positively impacted by the automated trading system, exchange rates, and interest rates. Once more, the study draws the conclusion which asserts the economic indicator variable of the nation are marked via erratic and significant fluctuations in conversion rates. According to the report, the exchange’s transaction guidelines need to alter in order to provide shareholders the knowledge and confidence to make trading decisions on their own, without the assistance of a licensed stockbroker.

Impact of National Culture on Stock Markets’ Returns and Volatility: Evidence from BRICS-P

This study aims to investigate how national cultures affect stock market returns across the BRICS economies and Pakistan. The conceptual framework is how national cultures affect stock return and volatility with six dimensions (PDI, UAI, MAS, LTO, IDV, and IVR) and two control variables, the exchange rate and interest rate. The data samples are extracted from Six BRICS’P nations, including Brazil, Russia, India, China, South Africa, and Pakistan for the period from January 1, 2011, through December 31, 2021. Regression analysis using panel data was utilized in this study to examine the effects of the major factors both individually and collectively. The results of panel data analysis indicate that National culture has little effect on returns from all six countries. Furthermore, only MAS has a negative substantial impact on return in the national culture dimension. Volatility is adversely affected by national culture. National cultures affect stock market volatility and returns globally, but because it analyses long-term data and Hofstede’s culture modifies the data over the years.

Modelling stock market volatility using asymmetric GARCH models: evidence from BRICS stock markets

Modelling stock market volatility using asymmetric GARCH models: evidence from BRICS stock markets

Portfolio Optimization Strategy Based on Risk Diffusion Model in Emerging Industry Development

Abstract In this paper, we first sort out the formula of the premium principle and the algorithm of the diffusion model and then study the strategy problem about optimal investment consumption and insurance purchase when investors invest in new developing industries under the risk diffusion model. In real financial markets, there are two types of uncertainty regarding asset prices: normal fluctuations and abnormal shocks. The risk diffusion model is used to plan the optimal investment strategy based on this basis. In the end, three tests are executed, including two numerical simulations and one investment analysis that determines the investor’s age. The computational results show that the optimal strategy in the first set of simulations is the 56% increase in investment volume A(x) at the parameter σ = 0.1. The standard deviation of the investor’s objective in the second set of simulations is 9.287%, and the investor’s assets invested in risky securities should be 1.071. In the third set of tests, as the investor’s age increases, the value of the investor’s investment in risky assets continues to decline from 2.0 after 30 years, and by the time it reaches 40 years, it is already close to 0.25, and there is a continued decline, converging to 0. Investors can invest in providing effective reference data by investing in the portfolio optimization strategy in this paper, which predicts stock market volatility and vibration.

Применение нейронных сетей архитектуры LSTM для моделирования волатильности фондового рынка

Introduction. Stock market volatility simulation and forecast are relevant issues which could contribute into lower risks and higher revenues of the market transactions. These days, AI-based methods, including deep neural networks, are quite promising for volatility simulation.

Does investors’ sentiment influence stock market volatility? Evidence from India during pre- and post-Covid-19 periods

Does investors’ sentiment influence stock market volatility? Evidence from India during pre- and post-Covid-19 periods

Stock Market Volatility Forecasting: Can Interval Data Improve it?

Stock Market Volatility Forecasting: Can Interval Data Improve it?

Modelling Stock Market Volatility using Asymmetric GARCH Models: Evidence from BRICS stock markets

Modelling Stock Market Volatility using Asymmetric GARCH Models: Evidence from BRICS stock markets

Research on dynamic spillover effect of stock market volatility in Beijing-Tianjin-Hebei region

To enhance economic cooperation in the Bohai Sea and Beijing-Tianjin-Hebei region, China introduced the Beijing-Tianjin-Hebei integration plan in 2014. Over the past decade, coordinated development has established a solid foundation, emphasizing economic and spatial structure optimization. The region’s development has now achieved comprehensive, qualitative, and profound progress. Recognizing economic integration as crucial, this study analyzes the 2020-2023 stock data of listed firms in the region, utilizing the DY spillover index to examine sectoral volatility spillover effects, both statically and dynamically. Results reveal bidirectional, asymmetric spillovers, with heterogeneous spillover levels. Industry, raw materials, and information technology are key spillover sectors, significantly influencing others and inducing synchronization. Property and finance sectors are primarily recipients, influenced by others. Energy and communication services exhibit strong bidirectional spillovers. Volatility spillover strength is influenced by factors such as inter-sector correlation, market risk, macroeconomic changes. Studying these factors helps grasp volatility spillover patterns and understand sectoral volatility-factor relationships.

Impact of Global Economic Policy Uncertainty and Stock Market Volatility on Korea’s Trade

Impact of Global Economic Policy Uncertainty and Stock Market Volatility on Korea’s Trade