Stock Market Volatility Research Articles

The mediating role of stock market liquidity on the relationship between Investor sentiment and Stock market volatility: An Applied study on Listed Companies in Egyptian stock Exchange

The mediating role of stock market liquidity on the relationship between Investor sentiment and Stock market volatility: An Applied study on Listed Companies in Egyptian stock Exchange

Forecasting Stock Market Volatility via Causal Reasoning

Abstract Studies have shown that Internet financial news has become an important reference for investors in investment behavior. In order to simulate trading experiments that mimic the real stock market, this paper develops a stock volatility prediction model based on causal reasoning. It also gathers and cleans news and stock market data from the Internet, such as opening price, closing price, and change. The findings of the study indicate that the level of stock market volatility can be significantly influenced by online financial news. The proposed model can analyze the effects of news and stock market data in an explainable manner.

The Effect of 2021 Brazil’s “Proposal for Free Market Expansion of the Electricity Sector” on Short-Term Stock Prices and Volatility

We examine the effect of the “Proposal for Free Market Expansion of the Electricity Sector” disclosure on Brazil’s short-term stock market prices and volatility. We employ the Difference-in-Differences and the Doubly Robust Difference-in-Differences methodologies to analyze the proposal’s disclosure effect on daily returns and volatility of electricity sector companies listed on the Brazilian stock exchange. Results show a negative effect of the policy on stocks’ average daily returns and volatility.

Analysing Volatility Dynamics of India and US Stock Market

Abstract: Analysing the risk-return trade-off of financial assets, volatility is a key factor. Volatility promotes market liquidity and demonstrates the effectiveness of the financial system. To have an insights of India And US Market Volatility behaviour, this paper examines the volatility dynamics of India And US Stock market by taking Sensex And S & P 500 daily closing prices for the 2nd January 2003 to 31st December 2019. The authors have two questions to examine about volatility whether India and US stock market volatility shows volatility clustering and volatility persistence or not. ARCH LM test and Ljung Box Q2 statistics were used to check for volatility clustering. And for volatility persistence GARCH (1,1) model is used. The results of the study confirm the presence of volatility clustering in in both India And US stock market. Additionally, Indian stock return volatility shows lower volatility persistence than US. The results of the study are quite useful for the academicians, policy makers and investor community in general

The Motives for Issuing Exchangeable Bonds in the Privatization of State‑Owned Enterprises

Using a unique sample of exchangeable bond issues carried out in seven countries since the 2000s, this paper investigates the role of hybrid debt in the privatization of state‑owned enterprises (SOEs) via government‑controlled investment vehicles. This research shows that so far, sixteen series of exchangeable bonds amounting to approx. USD 25 billion were issued to privatize ten SOEs in the telecommunication, energy, basic materials, industrials, healthcare, and utilities sectors in Europe and Asia. Moreover, in some cases, the privatization of SOEs by means of exchangeables can prove to be a more favorable alternative to traditional methods of selling shares directly on the capital market, for example, via IPOs or SPOs (during periods of deep undervaluation of privatized companies or of high stock market volatility). First, shares can be sold at a later date and at a higher price. Second, the impact on the stock market price of an SOE may be less disruptive to shareholders. Third, the entire privatization process tends to be more flexible for issuers in that they can perceive exchangeables as a source of relatively cheap, long‑term external capital while keeping control over the privatized company until the potential conversion of debt by bondholders.

The Impact of Financial Innovation on Stock Market Volatility: An Empirical Analysis

The Impact of Financial Innovation on Stock Market Volatility: An Empirical Analysis

Retail Financial Innovation and Stock Market Dynamics: The Case of Target Date Funds

ABSTRACTTarget date funds (TDFs) are designed to provide unsophisticated or inattentive investors with age‐appropriate exposures to different asset classes like stocks and bonds. The rise of TDFs has moved a significant share of retirement investors into macrocontrarian strategies that sell stocks after relatively good stock market performance. This rebalancing drives contrarian flows across equity mutual funds held by TDFs, stabilizing their funding, and reduces stock returns for stocks disproportionately held by these funds when stock market returns are relatively high. Continued growth in TDFs and similar investment products may dampen stock market volatility and increase the transmission of shocks across asset classes.

Effects of Conditional Oil Volatility on Exchange Rate and Stock Markets Returns

Saudi Arabia experienced extreme volatility in oil price booms during a short period due to COVID-19, which traded for 16 days. The oil price boom period has received significant attention as it is essential to a nation’s economic sector. The objective of this study was to examine how changes in oil volatility under specific conditions affect the stock market return indices and exchange rate of Saudi Arabia. The Saudi Arabia capital market was used as the case study, while the average daily exchange rate and stock market were employed as the primary variable. This study covers the period from March 21, 2014, to November 19, 2020. The wavelet methodology was used for data analysis. The study's findings revealed that oil is not a safe haven for the bond market in Saudi Arabia. Similarly, oil-primarily functions as a safe haven for a short period that last for 16 days. The findings revealed that oil price fluctuation has no impact and a safe haven effect on the various deals in the Saudi Arabian capital market. It also suggests that market participants focus on oil price trading as a significant tool for determining the volatility of stock market returns in underdeveloped countries.

An Empirical Study on the Dynamics of NIFTY 50 Due to the Behavior of Macro Economic Variables

In this paper we studied the correlation between Nifty 50, the National Stock Exchange of India's (NSE) benchmark stock index and macroeconomic variables of Indian economy which may influence it. Our analysis uses monthly data from 1.1.2017 to 1.4.2023, published by various financial statistical platforms. We applied descriptive analysis to summarize or describe the attributes of our data set. To estimate the effect of our explanatory variables (inflation rate and interest rate) on the dependent variable (Nifty 50), we applied regression techniques. The results of this study discover that neither “Inflation rate” nor “Interest Rate” is found as a good regressor. Stock markets are essential in fostering capital formation, advancing economic progress and thus to the expansion and development of the economy, so is crucial to search for variables that influence the volatility of stock market and its development. Majority of past research go on to say that there is no universal agreement on the variables influencing the progression and fluctuations of stock market. Therefore, it is crucial to search for variables that have a big impact on its volatility.

An examination of impact of gold and oil prices on the stock markets indices during COVID-19

Understanding stock market volatility has remained significantly important because of its diverse implications. The current pandemic COVID-19 has brutally exaggerated the financial market across the board. This study inspects the volatility of Pakistan, India and USA stock markets due to pandemic of COVID-19 using the GARCH model. Furthermore, the price of oil and gold is used as exogenous variables in the conditional mean equation of the GARCH (1,1) model to assess the volatility of stock indices KSE-100 (Pakistan), BSESN (India) and NASDAQ (USA). Moreover, the data covering the period from 1st June 2018 to 31st December 2022 are divided into pre-COVID-19 (1st June 2018 to 30th January 2020) and during-COVID-19 (31st January 2020 to 31st December 2022. Findings reveal that the stock market dependency of NASDAQ experienced oil and gold impact followed by pre-COVID-19 and during-COVID-19 periods, respectively. Whereas, the Indian stock market had a significant impact on gold and oil during the COVID-19 period but the Pakistani stock market was not influenced by oil and gold in the entire considered study period. In addition, NASDAQ market and BSESN have the highest risk-return trade-off and are more persistent followed by pre-COVID-19 and during-COVID-19 periods, respectively.

Forecast Research on the Fluctuation Risk of Green Financial Market in China based on the GARCH-LSTM Mixed Model

With the rapid development of China’s green financial market, there is also the problem of uncertain risk of fluctuation in the transaction price of green financial products. Therefore, it is imperative to give play to the role of price fluctuations in early warning of risks in green financial markets. Based on the closing price data of green stock market and green bond market from April 19, 2018 to April 18, 2023, this paper combines the traditional time series model and deep learning model to fit and forecast the yield volatility of green financial market, and quantifies the risk value of green financial market. The results show that: (1) Both the green stock market and the green bond market have the characteristics of leptokurtosis and fat-tail, volatility aggregation and long-term memory to varying degrees. (2) The volatility risk in the green financial market has obvious differentiation characteristics, and the volatility in the green stock market is significantly higher than that in the green bond market. (3) The prediction accuracy of LSTM long-term and short-term memory model is significantly higher than that of GARCH family model. The GARCH-LSTM mixed model can further improve the prediction accuracy. According to the research results, the combination of modern artificial intelligence algorithm and classical econometric theory can become an important means to identify the volatility risk of green financial market, and put forward meaningful suggestions for the risk management of green financial market.

Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure

This paper investigates the relationship between investors' attention, as measured by Google search queries, and equity implied volatility during the COVID-19 outbreak. Recent studies show that search investors' behavior data is an extremely abundant repository of predictive data, and investor-limited attention increases when the uncertainty level is high. Our study using data from thirteen countries across the globe during the first wave of the COVID-19 pandemic (January–April 2020) examines whether the search “topic and terms” for the pandemic affect market participants’ expectations about future realized volatility. With the panic and uncertainty about COVID-19, our empirical findings show that increased internet searches during the pandemic caused the information to flow into the financial markets at a faster rate and thus resulting in higher implied volatility directly and via the stock return-risk relation. More specifically for the latter, the leverage effect in the VIX becomes stronger as Google search queries intensify. Both the direct and indirect effects on implied volatility, highlight a risk-aversion channel that operates during the pandemic. We also find that these effects are stronger in Europe than in the rest of the world. Moreover, in a panel vector autoregression framework, we show that a positive shock on stock returns may soothe COVID-related Google searches in Europe. Our findings suggest that Google-based attention to COVID-19 leads to elevated risk aversion in stock markets.

Clustering and anomalies of USA stock market volatility index data

Actuality. Investing money is an important way to improve the financial condition of both an individual and society as a whole. The problem of understanding financial data and making decisions regarding the investment of money in a certain project at this moment in time is relevant. The object of the study is the process of establishing the dependence of the value of the asset on time. The subject of research is mathematical models of data processing, data clustering and anomaly detection. The purpose of this work is to develop a method for effective investment of money using data processing and analysis methods for CVOE volatility index values in the USA, determination of clusters based on actions with assets, as well as checking the presence of anomalous data. Research results. Official data of volatility index values were selected and prepared for further analysis by removing incomplete sets and further normalization. Clustering of time series was carried out and the array was divided into five homogeneous groups. Clusters determine the ranges of the volatility index, which reflect the different sentiments of investors in the market and encourage appropriate actions with assets: to sell, to wait of index increasing, to buy, to remove money from developing projects and invest in stable ones, to wait of index decreasing. Segmentation of data, application of window function, centroids for segments were determined and signal reconstruction was carried out. Data anomaly points were identified. A comparative analysis was carried out based on the results of constructed initial data, reconstructed data and reconstruction error.

The effect of uncertainty on stock market volatility and correlation

In this study, we use an extension of the heterogeneous autoregressive model to investigate the influence of time-varying risk aversion and macroeconomic, financial, and economic policy uncertainty measures on stock market volatility and correlation. Based on the findings, there is a stronger predictive ability of these variables at the monthly frequency than at the daily frequency. We also highlight the importance of risk aversion, which, alongside fundamental factors, reflects investor sentiment in predicting stock market volatility. Meanwhile, although uncertainty variables, such as economic uncertainty and financial uncertainty, are important, the widely used variable, economic policy uncertainty, is not helpful for predicting stock market volatility. Moreover, there is evidence of higher economic value and reduced portfolio risk when including risk aversion and economic uncertainty in international portfolio analysis.

The Volatility of Stock Market in Indonesia During Covid-19 Crisis : Evidence from Consumer Goods Industry

This study aims to measure the volatility of stock in consumer goods industry. The analysis in this study utilize index value of consumer goods sectoral stock in 260 days. Using the paired t-test, it was discovered that stock index of consumer goods has significant change after the announcement of Covid-19 first case in Indonesia. The volatility of stock index was raised during the pandemic as measured from its standard deviation. Furthermore, the estimation of GARCH model revealed that consumer goods stock experienced a great volatility during Covid-19 crisis. The value of volatility persistence is 0,917 or close to 1, it means that the volatility was long-lasting as long as the economic crisis. The result of study could give the information for stock investor to be careful with their investment fund during the crisis. Meanwhile, the consumer goods companies can observe this result as consideration in determining the effective strategy in managing the company’s finances in the midst of an economic crisis.

The impact of the covid-19 pandemic on the stock markets of some countries in the MENA region: An assessment with GARCH modeling

This study examines the impact of the COVID-19 pandemic on stock market volatility for stock indices of a few MENA countries. The analysis consists of several elements: first, we assess the impact of this infectious disease on the standard deviations of stock market variables; second, we analyze the influence of the number of new daily cases and the number of new daily deaths on the standard deviations of these returns; and third, we assess the impact of the coronavirus on stock market volatility for these market variables using GARCH models. We find that the minimum value for most indices occurred in March 2020. Additionally, the results of the regression analysis reveal that the COVID-19 dummy variable, the number of daily new cases, and the number of new daily deaths have all had a significant negative impact on stock market volatility. Finally, the GARCH model reveals that the coefficients of COVID-19 pandemic in the conditional variance equation had a significant positive impact on the conditional variance for stock indices, further indicating that the COVID-19 outbreak increased market volatility.

The Relationship Between Investor Sentiment and Stock Market Price

With the birth of behavioral finance, more and more researchers have brought the individual psychology of investors and the behaviors that it fosters into the theoretical research scope of finance. From the perspective of micro individual behavior, they try to explain the causes of complex phenomena in the financial market with psychological factors such as behavioral motivation. This paper mainly analyzes the impact of investor sentiment on the price volatility of China's stock market. On the basis of combing the existing theoretical literature, based on the daily Chinese investor sentiment index published by the National Development Research Institute of Peking University and the return of the CSI 300 Index, the research hypothesis is put forward. First, the GARCH model is constructed to calculate the daily volatility of the return of the CSI 300 Index and the Granger causality is used to test the relationship between investor sentiment, stock price return and volatility. Secondly, the paper empirically analyzes the relationship between investor sentiment and stock price volatility by constructing VAR model, and finds that investor sentiment will indeed have the same impact on the price volatility of China's stock market, and the stock price volatility will react on investor sentiment and form an interactive impact, which will gradually weaken over time. Finally, from investors The government and other aspects put forward suggestions to further improve the operation of China's stock market.

Exploring the impact of online news sentiment and relevance on stock market risks: A signalling theory perspective

AbstractBased on the frame theory and signalling theory, this article uses financial technology (fintech) to analyse online news sentiment and proposes the relevant hypothesis of the influence of news relevance on stock market risk, and conducts an empirical study on the VAR model of the stock return rate by using the news relevance and news sentiment data of the Uqer database. The results show a two‐way Granger causality between news sentiment and stock returns. News relevance is not Granger causality of yield but Granger causality of news sentiment. In addition, combined with the impulse response, variance decomposition, and relevance analysis, it is found that the news relevance degree positively affects news sentiment and then indirectly affects the stock return rate. Our findings indicate that news relevance is an essential variable in analysing online text sentiment and stock market volatility, and online news will increase the volatility of stock market risk. These results contribute to the financialization literature and guide fintech enterprises to balance market risk by combining signalling and frame theory to improve news relevance.

Dynamic correlation and hedging strategy between Bitcoin prices and stock market during the Russo-Ukrainian war

This paper examines the dynamic relationship between stock market and Bitcoin volatilities during the recent geo-political event of the Russo-Ukrainian war. Using the ADCC–GARCH model, empirical results show that volatility of the two markets has rose significantly during the event. More interestingly the dynamic correlation increases during the Russia’s invasion of Ukraine indicating that Bitcoin has failed to play the role of safe haven during this period. In portfolio context, our finding reveals that adding Bitcoin to portfolio of stocks reduce the risk without lowering the expected returns. Finally, referring to hedging ratio, we find that hedging strategy leads higher cost during the war.

Default risk transmission in the travel and leisure industry

Default risk in the Travel and Leisure (T&L) industry remains understudied despite its implications for the industry’s health and stability. This paper investigates the transmission of default risk among US T&L firms over various credit horizons from July 22, 2008 to December 9, 2022, paying special attention to the impact of COVID-19. The short-, medium-, and long-term default risk factors are extracted from the Credit Default Spread (CDS) curve of the US T&L industry then used within a connectedness approach. The results reveal considerable default risk transmission, particularly in the long-term. Default risk transmission has spiked across all horizons since the pandemic, reflecting the deterioration in credit quality of T&L firms under the pandemic. Analysis of the drivers of default risk transmission shows that several macro-financial variables, especially news market sentiment and stock market volatility induced by the pandemic, have an important explanatory role.