scholarly journals Can Time-Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility?

2013 ◽  
Vol 68 (3) ◽  
pp. 987-1035 ◽  
Author(s):  
JESSICA A. WACHTER
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Time Varying ◽  
Stock Market Volatility ◽  
Rare Disasters

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.

scholarly journals Time Varying Volatility in the Indian Stock Market

2004 ◽  
Vol 29 (4) ◽  
pp. 25-42 ◽  
Author(s):  
Harvinder Kaur
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Weekend Effect ◽  
Market Volatility ◽  
Time Varying ◽  
Stock Market Volatility ◽  
The Us ◽  
Forecast Models ◽  
Indian Stock Market ◽  
Volatility Forecast

This paper investigates the nature and characteristics of stock market volatility in India. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. Various volatility estimators and diagnostic tests indicate volatility clustering, i.e., shocks to the volatility process persist and the response to news arrival is asymmetrical, meaning that the impact of good and bad news is not the same. Suitable volatility forecast models are used for Sensex and Nifty returns to show that: The ‘day-of-the-week effect’ or the ‘weekend effect’ and the ‘January effect’ are not present while the return and volatility do show intra-week and intra-year seasonality. The return and volatility on various weekdays have somewhat changed after the introduction of rolling settlements in December 1999. There is mixed evidence of return and volatility spillover between the US and Indian markets. The empirical findings would be useful to investors, stock exchange administrators and policy makers as these provide evidence of time varying nature of stock market volatility in India. Specifically, they need to consider the following findings of the study: For both the indices, among the months, February exhibits highest volatility and corresponding highest return. The month of March also exhibits significantly higher volatility but the magnitude is lesser as compared to February. This implies that, during these two months, the conditional volatility tends to increase. This phenomenon could be attributed to probably the most significant economic event of the year, viz., presentation of the Union Budget. The investors, therefore, should keep away from the market during March after having booked profits in February itself. The surveillance regime at the stock exchanges around the Budget should be stricter to keep excessive volatility under check. Similarly, the month of December gives high positive returns without high volatility and, therefore, offers good opportunity to the investors to make safe returns on Sensex and Nifty. On the contrary, in the month of September, i.e., the time when the third quarter corporate results are announced, volatility is higher but corresponding returns are lower. The situation is, therefore, not conducive to investors. The ‘weekend effect’ or the ‘Monday effect’ is not present. For other weekdays, the ‘higher (lower) the risk, higher (lower) the return’ dictum does not hold consistently and Wednesday provides higher returns with lower volatility making it a good day to invest. The domestic investors and the stock exchange administrators do not need to lose sleep over gyrations in the major US markets since there is no conclusive evidence of consistent relationship between the US and the domestic markets. The volatility forecast models presented for Sensex and Nifty can be used to forecast future volatility of these indices.

A mathematical model for volatility flocking with a regime switching mechanism in a stock market

2015 ◽  
Vol 25 (07) ◽  
pp. 1299-1335 ◽  
Author(s):  
Hyeong-Ohk Bae ◽  
Seung-Yeal Ha ◽  
Yongsik Kim ◽  
Sang-Hyeok Lee ◽  
Hyuncheul Lim ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Stock Market ◽  
Regime Switching ◽  
Market Volatility ◽  
Time Varying ◽  
Stock Market Volatility ◽  
Financial Applications ◽  
Proposed Model ◽  
The Common ◽  
Geometric Brownian Motions

We present a mathematical model for stock market volatility flocking. Our proposed model consists of geometric Brownian motions with time-varying volatilities coupled with Cucker–Smale (C–S) flocking and regime switching mechanisms. For all-to-all interactions, we assume that all assets' volatilities are coupled to each other with a constant interaction weight, and we show that the common volatility emerges asymptotically and discuss its financial applications. We also provide several numerical simulations and compare them to existing analytical results.

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