Stock return volatility cluster

Jagajeevan (2012) examined the persistence of volatility, risk-return trade off and asymmetric volatility in returns, on daily and monthly returns on the All Share Price Index of the Colombo stock exchange. He only identified volatility clustering in daily returns, but not in monthly returns.

Time series of financial asset returns often demonstrates volatility clustering. In a time series of stock prices, for instance, it is observed that the variance of returns or log-prices is high for extended periods and then low for extended periods. As such, the variance of daily returns can be high one month (high volatility) and show low variance (low volatility) the next. Mandelbrot noted that when it comes to stock market volatility, large changes tend to follow large changes, of either sign, and small changes tend to follow small changes. In other words, volatility begets volatility, and such periods of large market swings are called a Volatility Cluster. The above results suggest that monthly frequency stock returns might also exhibit significant volatility clustering effects, but that this does not show because many financial series are simply too short. For many countries—for instance emerging markets—long time series of stock returns are still rare. Time series of financial asset returns often exhibit the volatility cluster- ing property: large changes in prices tend to cluster together, resulting in persistence of the amplitudes of price changes. We also show that (a) part of the stable-less-volatility stock return spread is attributable to operating performance, (b) low-volatility stocks have stronger future operating performance, and (c) strong past operating performance can help predict whether a firm will be a low volatility stock in the future, an (d) that controlling for operating performance significantly influences the relationship between stock returns and volatility.

37. VOLATILITY CLUSTERING, LEVERAGE. EFFECTS AND RISK-RETURN TRADE-. OFF IN THE SELECTED STOCK MARKETS. IN THE CEE COUNTRIES.

However, the stock market remains a volatile investment window. Stock return volatility represents the irregularity of stock price changes over a period of time. Market participants (investors, analysts, brokers and dealers) and regulators have more than a passing interest in idiosyncratic volatility of stock returns. The monthly return volatility for a stock is a numerical representation of that stock's risk; the technical term for volatility is standard deviation. A stock with high volatility tends to move more than a stock with lower volatility over the course of a typical month. First, the model produces stock return volatility cluster- ing or GARCH e⁄ects. The market price of risk, which drives the short-run stock return volatility, has three components: endowment risk, sentiment risk and solvency risk. These three compo- nents are persistent, hence the model reproduces volatility clustering. specific or macroeconomic) and changes in stock return volatility is a search into how corporate and public information induces changes in asset prices and values. Generalized AutoRegressive Conditional Heteroskedasticity (GARCH): A statistical model used by financial institutions to estimate the volatility of stock returns. This information is used by banks

A stock's volatility is the variation in its price over a period of time. For example, one stock may have a tendency to swing wildly higher and lower, while another stock may move in much steadier, less turbulent way.

specific or macroeconomic) and changes in stock return volatility is a search into how corporate and public information induces changes in asset prices and values. Generalized AutoRegressive Conditional Heteroskedasticity (GARCH): A statistical model used by financial institutions to estimate the volatility of stock returns. This information is used by banks In equation form, this is: Rn=ln(Cn/(C(n-1)), where Rn is the return of a given stock over the period, ln is the natural log function, Cn is the closing price at the end of the period, and C(n-1) is the closing price at the end of the last period. Jagajeevan (2012) examined the persistence of volatility, risk-return trade off and asymmetric volatility in returns, on daily and monthly returns on the All Share Price Index of the Colombo stock exchange. He only identified volatility clustering in daily returns, but not in monthly returns. Stock market volatility is generally associated with investment risk; however, it may also be used to lock in superior returns. Volatility is most traditionally measured using the standard

the return series such as volatility clustering, leptokurtosis and asymmetric news effect. Though there are a number of studies on stock market volatility, studies 

return series of all ten markets, indicating the presence of volatility clustering, that is, the tendency of large stock price changes to be followed by large stock.

The above results suggest that monthly frequency stock returns might also exhibit significant volatility clustering effects, but that this does not show because many financial series are simply too short. For many countries—for instance emerging markets—long time series of stock returns are still rare.

5 Apr 2015 Empirical Study on Stock Return Volatility in China's Stock Market Some financial time series is often appear volatility clustering phenomenon. Keywords: Macroeconomic factors, stock return volatility, GARCH, VAR (JEL code: C32, C58, G11, fairly known to capture the volatility clustering and volatility. volatility of returns in India's two premier stock indices, namely, BSE SENSEX and To model financial time series and stylized facts such as volatility clustering,  To capture stock returns volatility clustering, leptokurtosis and leverage effects on the share price series, the GARCH models were used. Specifically, the GARCK (   23 Jul 2014 In other words, realized volatility can usefully model the clustering any separate realized volatility and absolute return volatility of each stock. 1 Jan 2014 Results obtained from GARCH models suggest that stock returns volatility of the Nigerian banking sector move in cluster and that volatility per-.

Stock Market Volatility, Clustering, NIFTY returns, India. VIX, CBOE VIX, Kernel K- Means, Gaussian Mixture Model,. Silhouette Index, Dunn Index. 1. 1 Jun 2016 that include; clustering volatility, leptokurtosis, and leverage effect. return volatility for Amman Stock Exchange (ASE) covering the period from