Return Volatility Research Articles

Spillovers in the U.S.-China Stock Markets - A Study of Returns and Volatility under Full Sample Periods and Multi-Special Periods

This paper adopts the ARMA-GARCH model and selects the S&P500 and the SSEC to study the return rate and volatility spillover effect of the US stock market(USM) and the Chinese stock market(CSM) in the full sample period, the Chinese stock market crash in 2015 and the duration when coronavirus spread. In both the full sample period and the two specific periods, it is discovered that the USM significantly increases the return on the CSM. While the CSM barely has any impact on the USM. Although the impact is favorable over the whole sample, it is less pronounced during the The pandemic caused by the novel coronavirus and the China's 2015 stock market debacle. In the whole sample, the S&P500 is unaffected by the SSEC's volatility, whereas the SSEC is significantly harmed by the S&P500's volatility. The S&P500's volatility will have a major beneficial influence on the SSEC, but it won't be communicated from the SSEC to the S&P500 during the global spread of COVID-19. Both of the fluctuation transmission to each other were negligible during the 2015 stock market turmoil. For risk management and stock asset allocation, this study can offer important information to financial institutions, everyday individuals, and governments.

China's National Team: A Game Changer in Stock Market Stabilization?

This study investigates the influence of China's national team investors on stock market volatility and crash risks using 2011-2018 data. Findings show increased stock return volatility and decreased crash risks post-2015. The national team reduced risk for pledged shares, while treatment firms reduced R&D investment and diversified business more than control groups. A difference-in-difference approach reveals heightened volatility in firms with national team ownership after 2015, questioning the efficacy of these interventions. This research offers insights into government intervention in financial markets, emphasizing the national team's impact on market stability and economic outcomes.

The impact of position limits on options trading

We provide new evidence on the effects of position limits on options for ETFs on the S&P 500 (SPY contracts), based on market reactions to the pilot program (amendment to CBOE Rule 4.11), whereby position limits were temporarily suspended. Removal of position limits during the pilot period did not harm the underlying markets in terms of volatility. While option volume shocks enhance SPY return volatility shocks, these effects are alleviated when position limits are suspended. Furthermore, during the pilot period, price efficiency improved, based on the market tracking performance of the ETFs.

Outperforming the market: a comparison of Star and NonStar analysts’ investment strategies and recommendations

AbstractWe employ StarMine to investigate the impact of analyst recommendations on stock performance. We test whether star-ranked analysts generate abnormal returns and outperform non-stars in short and long portfolios. Utilizing buy-and-hold calendar-time portfolio methodology, we calculate portfolio alphas using various asset pricing models, including CPM, the Fama and French 3-factor model, and the Carhart 4-factor model. Results indicate that all analyst groups can generate abnormal returns exceeding the market average. Star-ranked analysts outperform non-stars in short portfolios by 0.5523% in monthly alpha, though no significant difference exists in long portfolio alphas. We also conduct regressor endogeneity tests and explore investor sentiment mechanisms by utilizing the GARCH model and frequency-domain causality analysis, with NASDAQ as a proxy for investor sentiment. These tests reveal that the momentum factor is exogenous, and investor sentiments have a statistically significant positive effect on stock return volatility, with changes occurring between 5 and 10 days. This research underscores the value of analyst insights for investors, validates StarMine’s ranking effectiveness, and suggests market participants can benefit from incorporating analyst recommendations into their investment decisions. Our study makes a significant contribution to the existing literature by introducing a novel approach to understanding investor sentiment mechanisms through a causality model.

Influence of Artificial Intelligence on Forecasting Net Asset Value and Return Volatility in Indian Mutual Fund

Predicting the critical net asset value (NAV) in the financial market is difficult for investors and fund agencies. The present study introduces machine learning (ML) and deep learning (DL) models such as linear regression, deep long short-term memory recurrent neural network (DLSTM-RNN), and autoregressive integrated moving averages (ARIMA) for predicting the NAV. The five different equity sectoral technology mutual fund direct growth plans from January 2013 to December 2022 have been collected. The novelty of the current study is deeply examining, which ML or DL model devotedly predicts the NAV closing price. The major key findings of the experimental results proved that the DLSTM-RNN model makes statistically viable predictions, whereby the mean absolute percent error (MAPE) average prediction accuracy value is 0.02. Based on the accuracy of a superior model, we compute the annualized return volatility to compare the risk of investments with annual return periods over different time horizons. The Jarque-Bera statistics of the return volatility over time Gaussian distribution is rejected at the 0.01 level. Statistical paired t-test and Pearson correlation coefficient are used to compare the effects of the proposed three models. In addition, the benchmark portfolio strategy yields a Sharpe ratio of 7.0193 and the maximum drawdown is 0.3743. The AI performed deep LSTM neural network model simulation, especially when using a daily and monthly MAPE strategy giving 81% and 84% highest NAV prediction consistency than the linear regression and ARIMA models.

The historical transition of return transmission, volatility spillovers, and dynamic conditional correlations: A fresh perspective and new evidence from the US, UK, and Japanese stock markets

<abstract> <p>This paper quantitatively investigated the historical transition of return transmission, volatility spillovers, and correlations between the US, UK, and Japanese stock markets. Applying a vector autoregressive (VAR)-dynamic conditional correlation (DCC)-multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model, we derived new evidence for four historical periods between 1984 and 2024. First, we found that the return transmission from the US to the other markets has historically become stronger, whereas recently, the return transmission from the UK to the US has disappeared. Second, we clarified that volatility spillovers from the US to the other markets have historically become stronger, whereas recently, volatility spillovers from the UK to the US have also disappeared. Third, our analyses of the historical constant correlations and DCCs revealed that stock market connectedness has gradually tightened between the US and Japan and between the UK and Japan, whereas recently, the connectedness between the US and UK has weakened. Fourth, our VAR-DCC analyses also revealed that volatility spillovers between the US, UK, and Japanese stock markets have been asymmetric. Fifth, we further showed that the skew-<italic>t</italic> errors incorporated into our VAR-DCC model are effective in estimating the dynamic stock return linkages between the US, the UK, and Japan. Finally, based on our findings, we derived many significant and beneficial interpretations and implications for historically and deeply considering return transmission, volatility spillovers, and DCCs between international stock markets.</p> </abstract>

Homogenous influence of heterogeneous emotions on investor behavior

The use of text readability indicators and sentiment factors to asset pricing is practical in investment risk management yield pursuit. We analyzed the polarity of textual sentiment of Management Discussion and Analysis (MD&A) in 10-Ks for firms in the U.S. by constructing Sentiment Entropy, a new sentiment indicator based on L&M Dictionary, which captures the same characteristics of positive and negative emotions from the perspective of polarity. The empirical results show that the polarity of sentiments increases investment risk measured by return volatility, which differs from the traditional one-way emotion indicator. We found through a mediator effect test that emotional polarity significantly increases investment risk by stimulating large changes in trading volume, which is consistent with conventional wisdom. Challenging conventional findings, we find that positive and negative emotions in disclosures may have homogenous effects on the market, providing inspirations for understanding the role of textual sentiment on investor behavior.

Risk-return-volume causality on the Croatian stock market

Purpose: Causality between stock returns, volatility and traded volume for 10 most liquid stocks from Zagreb Stock Exchange (ZSE) is examined in this paper. Methodology: The paper relies on historical daily data regarding return, standard deviation and turnover for the period from 2015 to 2021. Vector Autoregressive Models (VARs) were estimated for each stock in-dividually. Based on estimated VAR models, Granger-causality tests were performed to estimate causality between trading volume, stock returns and volatility for most liquid stocks from the Croatian stock market. Results: Results strongly confirm that traded volume Granger causes volatility. Return remained irrelevant in terms of predicting traded volume and volatility of stock returns. Conclusion: Causality from return to volatility or causality from volatility to return can be confirmed only in shorter periods. Traded volume causes volatility for the majority of stocks regardless of how volatility was calculated. Causality from volatility to return and causality from volatility to volume are valid for half of the sample and need to be further investigated.

Comparing the Performance of GARCH Family Models in Capturing Stock Market Volatility in India

In recent times, the prediction of stock market volatility has emerged as a central focus in the domain of financial econometrics. This paper presents an empirical analysis aimed at modelling the volatility of the Indian stock market, particularly focusing on the NSE NIFTY 50, by utilizing various GARCH models. The investigation explores the volatility of stock returns, considering the daily closing prices, and examines the influence of two external factors: Crude oil prices and the INR/USD exchange rate. The inquiry employs data encompassing the period from January 1, 2012, to December 31, 2022, for all three variables. The manuscript delves into an array of univariate GARCH models, encompassing both symmetric and asymmetric models, and assesses their performance by utilizing metrics such as the Akaike Information Criterion, Schwartz Bayesian Information Criterion, and Log Likelihood. To assess the predictive accuracy of these models, statistical error measures such as Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are employed. The findings strongly suggest that the EGARCH model is the most effective in predicting the variations of the NIFTY index. Furthermore, the research highlights the significant impact of exchange rates and crude oil prices in relation to the volatility of the stock market in India.

Analysis of IPO Return Volatility—Based on Initial Stock Data from the ChiNext Market

Analysis of IPO Return Volatility—Based on Initial Stock Data from the ChiNext Market

Analyzing the Day of the Week Effect: A Study on Stock Market Returns

The study investigates the Day of the Week (DOW) effect on stock market returns in Malaysia, Indonesia, and the Dow Jones Islamic Market Index (DJIM). Utilizing GARCH models to examine stock return volatility, the study identifies significant calendar anomalies in stock returns, particularly lower returns on Mondays and higher returns on Fridays. These findings challenge the Efficient Market Hypothesis (EMH), suggesting predictable patterns in markets, particularly in emerging economies. This research provides insights into investor behavior and trading strategies, offering practical implications for market practitioners and academics.

Influence of stock return volatility on corporate leverage: evidence from an emerging economy

Influence of stock return volatility on corporate leverage: evidence from an emerging economy

Assessing the Effectiveness of Implied Volatility in Predicting Realised Return Volatility for Informative Decision-Making: Insights from the Nifty Bank Index

Implied volatility (IV) is crucial in option pricing models and serves as an essential tool for volatility traders to make informed decisions. However, its effectiveness in predicting realized return volatility is still debated. This study investigates the efficiency of implied volatility in forecasting realized return volatility in the Indian financial markets, specifically using Bank Nifty index options and also assesses the predictive capability of implied volatility against the realised volatility estimator. Utilizing data spanning five years, from January 2018 to December 2022. Finding of this study reveal that implied volatility significantly forecasts realized volatility, highlighting its efficacy as a forecasting tool. Moreover, historical volatility fails to enhance predictive power when combined with implied volatility. Nonetheless, caution should be exercised in generalizing these results to other markets or time periods, as further research is warranted. The study contributes to the ongoing discourse on implied volatility efficiency, offering practical insights for options traders and adding to the body of knowledge in financial economics.

A Hybrid GARCH and Deep Learning Method for Volatility Prediction

Volatility prediction plays a vital role in financial data. The time series movements of stock prices are commonly characterized as highly nonlinear and volatile. This study is aimed at enhancing the accuracy of return volatility forecasts for stock prices by investigating the prediction of their price volatility through the integration of diverse models. Thus, the study integrated four powerful methods: seasonal autoregressive (AR) integrated moving average (MA), generalized AR conditional heteroskedasticity (ARCH) family models, convolutional neural network (CNN), and bidirectional long short‐term memory (LSTM) network. The hybrid model was developed using the residuals generated by the seasonal AR integrated MA model as input for the generalized ARCH model. Following this, the estimated volatility obtained was utilized as an input feature for both the hybrid CNNs and bidirectional LSTM models. The model’s forecasting performance was assessed using key evaluation metrics, including mean absolute error (MAE) and root mean squared error (RMSE). Compared to other hybrid models, our new proposed hybrid model demonstrates an average reduction in MAE and RMSE of 60.35% and 60.61%, respectively. The experimental results show that the model proposed in this study has good performance and accuracy in predicting the volatility of stock prices. These findings offer valuable insights for financial data analysis and risk management strategies.

Modeling and Forecasting Return Volatilities of Inter-Capital Market Indices using GARCH-Fractional Cointegration Model Variation

This research compares modeling and forecasting the volatility of the IHSG, N225, and BSESN30 capital market indices using the GARCH variation model against the GARCH-fractional cointegration variation. The data used is secondary data obtained from www.investing.com from 01/01/2012 to 04/30/2023. Based on the performance measurement using the sMAPE criterion, the best model for forecasting the period 05/01/2023 to 05/31/2023 is the std-ALLGARCH (1,2)-fractional cointegration model for IHSG, the std-ALLGARCH(1,1) model for N225, and the sstd-ALLGARCH (1,2) model for BSESN30. This empirical finding means that the Japanese and Indian capital markets affect the volatility of the Indonesian capital market.

Creative Destruction, Stock Return Volatility, and the Number of Listed Firms

Creative Destruction, Stock Return Volatility, and the Number of Listed Firms

Do Oil Price Forecast Disagreement of Survey of Professional Forecasters Predict Crude Oil Return Volatility?

Do Oil Price Forecast Disagreement of Survey of Professional Forecasters Predict Crude Oil Return Volatility?

The Impact of COVID-19 on Commodity and S&P 500 Sector Return Volatility

The Impact of COVID-19 on Commodity and S&P 500 Sector Return Volatility

Response of stock return volatility to money market rates in Nigeria

Response of stock return volatility to money market rates in Nigeria

Modeling the Volatility of World Energy Commodity Prices Using the GARCH-Fractional Cointegration Model

Energy commodity prices usually fluctuate, non-linear and non-stationary. These stylish facts pose a big challenge in predicting the volatility of energy commodity prices because they usually contain long memory. In the energy market, energy commodities are empirically cointegrated, and this characteristic is a consideration for combining GARCH with Fractional Cointegration. This study aims to model and compare the GARCH and GARCH-Fractional Cointegration on the price return volatility of each energy commodity. The results show that the GARCH-Fractional Cointegration model is better for long-memory non-stationary data, while the GARCH model is better for long-memory stationary data.