Why Does Stock Market Volatility Change Over Time? A Time-Varying Variance Decomposition for Stock Returns

2005 ◽  
Author(s):  
John T. Scruggs ◽  
Federico Nardari
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Variance Decomposition ◽  
Market Volatility ◽  
Time Varying ◽  
Stock Market Volatility ◽  
Change Over Time ◽  
Over Time

scholarly journals Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach

Risks ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 89
Author(s):  
Muhammad Sheraz ◽  
Imran Nasir
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Approximate Entropy ◽  
Market Volatility ◽  
Comparative Approach ◽  
Stock Market Volatility ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Garch Modeling

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.

Effects of Daylight-Saving Time Changes on Stock Market Returns and Stock Market Volatility: Rebuttal

2013 ◽  
Vol 112 (1) ◽  
pp. 89-99 ◽  
Author(s):  
Mark J. Kamstra ◽  
Lisa A. Kramer ◽  
Maurice D. Levi
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Daylight Saving Time ◽  
Daylight Saving ◽  
Time Changes ◽  
Saving Effect

In a 2011 reply to our 2010 comment in this journal, Berument and Dogen maintained their challenge to the existence of the negative daylight-saving effect in stock returns reported by Kamstra, Kramer, and Levi in 2000. Unfortunately, in their reply, Berument and Dogen ignored all of the points raised in the comment, failing even to cite the Kamstra, et al. comment. Berument and Dogen continued to use inappropriate estimation techniques, over-parameterized models, and low-power tests and perhaps most surprisingly even failed to replicate results they themselves reported in their previous paper, written by Berument, Dogen, and Onar in 2010. The findings reported by Berument and Dogen, as well as by Berument, Dogen, and Onar, are neither well-supported nor well-reasoned. We maintain our original objections to their analysis, highlight new serious empirical and theoretical problems, and emphasize that there remains statistically significant evidence of an economically large negative daylight-saving effect in U.S. stock returns. The issues raised in this rebuttal extend beyond the daylight-saving effect itself, touching on methodological points that arise more generally when deciding how to model financial returns data.

Forecasting Stock Market Volatility: Evidence from Six Emerging Markets

2013 ◽  
Vol 14 (2) ◽  
pp. 68-93
Author(s):  
Naliniprava Tripathy ◽  
Ashish Garg
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Stock Price ◽  
Market Volatility ◽  
Future Market ◽  
Investment Strategies ◽  
Stock Market Volatility ◽  
Efficient Management ◽  
Asymmetric Garch

This paper forecasts the stock market volatility of six emerging countries by using daily observations of indices over the period of January 1999 to May 2010 by using ARCH, GARCH, GARCH-M, EGARCH and TGARCH models. The study reveals the positive relationship between stock return and risk only in Brazilian stock market. The analysis exhibits that the volatility shocks are quite persistent in all country’s stock market. Further the asymmetric GARCH models find a significant evidence of asymmetry in stock returns in all six country’s stock markets. This study confirms the presence of leverage effect in the returns series and indicates that bad news generate more impact on the volatility of the stock price in the market. The study concludes that volatility increases disproportionately with negative shocks in stock returns. Hence investors are advised to use investment strategies by analyzing recent and historical news and forecast the future market movement while selecting portfolio for efficient management of financial risks to reap benefits in the stock markets.

scholarly journals Effects of Daylight Savings Time Changes on Stock Market Volatility

2010 ◽  
Vol 106 (2) ◽  
pp. 632-640 ◽  
Author(s):  
M. Hakan Berument ◽  
Nukhet Dogan ◽  
Bahar Onar
Keyword(s):  
United States ◽  
Stock Market ◽  
Stock Returns ◽  
Conditional Variance ◽  
Time Effect ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Stock Volatility ◽  
Time Effects ◽  
Time Changes

The presence of daylight savings time effects on stock returns and on stock volatility was investigated using an EGARCH specification to model the conditional variance. The evidence gathered from the major United States stock markets for the period between 1967 and 2007 did not support the existence of the daylight savings time effect on stock returns or on volatility. Returns on the first business day following daylight savings time changes were not lower nor was the volatility higher, as would be expected if there were an effect.

Modeling Conditional Volatility of the Indian Stock Markets

2005 ◽  
Vol 30 (3) ◽  
pp. 21-38 ◽  
Author(s):  
Madhusudan Karmakar
Keyword(s):  
Stock Market ◽  
Realized Volatility ◽  
Market Volatility ◽  
Garch Models ◽  
Time Varying ◽  
Stock Market Volatility ◽  
Conditional Volatility ◽  
Volatility Clustering ◽  
Share Prices ◽  
Out Of Sample

Traditional econometric models assume a constant one period forecast variance. However, many financial time series display volatility clustering, that is, autoregressive conditional heteroskedasticity (ARCH). The aim of this paper is to estimate conditional volatility models in an effort to capture the salient features of stock market volatility in India and evaluate the models in terms of out-ofsample forecast accuracy. The paper also investigates whether there is any leverage effect in Indian companies. The estimation of volatility is made at the macro level on two major market indices, namely, S&P CNX Nifty and BSE Sensex. The fitted model is then evaluated in terms of its forecasting accuracy on these two indices. In addition, 50 individual companies' share prices currently included in S&P CNX Nifty are used to examine the heteroskedastic behaviour of the Indian stock market at the micro level. The vanilla GARCH (1, 1) model has been fitted to both the market indices. We find: a strong evidence of time-varying volatility a tendency of the periods of high and low volatility to cluster a high persistence and predictability of volatility. Conditional volatility of market return series from January 1991 to June 2003 shows a clear evidence of volatility shifting over the period where violent changes in share prices cluster around the boom of 1992. Though the higher price movement started in response to strong economic fundamentals, the real cause for abrupt movement appears to be the imperfection of the market. The forecasting ability of the fitted GARCH (1, 1) model has been evaluated by estimating parameters initially over trading days of the in-sample period and then using the estimated parameters to later data, thus forming out-of-sample forecasts on two market indices. These out-of-sample volatility forecasts have been compared to true realized volatility. Three alternative methods have been followed to measure three pairs of forecast and realized volatility. In each method, the volatility forecasts are evaluated and compared through popular measures. To examine the information content of forecasts, a regression-based efficiency test has also been performed. It is observed that the GARCH (1, 1) model provides reasonably good forecasts of market volatility. While turning to 50 individual underlying shares, it is observed that the GARCH (1, 1) model has been fitted for almost all companies. Only for four companies, GARCH models of higher order may be more successful. In general, volatility seems to be of a persistent nature. Only eight out of 50 shares show significant leverage effects and really need an asymmetric GARCH model such as EGARCH to capture their volatility clustering which is left for future research. The implications of the study are as follows: The various GARCH models provide good forecasts of volatility and are useful for portfolio allocation, performance measurement, option valuation, etc. Given the anticipated high growth of the economy and increasing interest of foreign investors towards the country, it is important to understand the pattern of stock market volatility in India which is time-varying, persistent, and predictable. This may help diversify international portfolios and formulate hedging strategies.

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