Interday and Intraday Volatility


Book Description

After examining both the interday and intraday return volatility of the Shanghai Composite Stock Index, it was found that the open-to-open return variance is consistently greater than the close-to-close variance. Examining the volatility of interday returns and variance ratio tests with five-minute intervals reveals an L-shaped pattern, or more precisely, two L-shaped patterns, starting with a small hump during both the morning and the afternoon sessions, with the morning session having a much higher interday volatility than the afternoon session. This L-shaped interday volatility is supported by the similarly shaped intraday volatility pattern. This result suggests that the high volatility of intraday returns for the market open is not entirely due to the trading mechanisms (call auction in the market opening) but also due to both the accumulated overnight information and the trading halt effect. The five-minute breaks after the auction and blind auction procedures are the two major driving forces which exaggerate the high intraday volatility observed at the market open.













Intraday Trading Patterns and Day-of-the-Week in Stock Index Options Markets


Book Description

This article studies the intraday patterns of trading volume, volatility, and spreads and day-of-the-week variations for stock index options traded on the Taiwan Futures Exchange (TAIFEX). In addition, we examine the overnight variations in returns, volatility and spreads as well. We find that trading volume of TAIFEX options exhibit a U-shaped pattern. While the volatility at the market open is extremely volatile, the volatility quickly levels off for much of the rest of a trading. The bid-ask spreads pattern for TAIFEX options approximately follows a U-shaped pattern with a small hump immediately after 13:00 hours. The mean returns at Monday open for TAIFEX calls are lower while returns at the end of a trading day are larger. Calls have smaller overnight variations in volatility and bid-ask spreads compared to those in puts.







Intraday Stealth Trading and Volatility


Book Description

The intraday volatility and volume U-shape pattern is well documented in the literature. It describes the common pattern of investor's behavior on the stock markets: investors trade in the beginning and the end of the day more intensive than in the lunch time. However that pattern does not differentiate between trades' sizes and investors characteristics. The stealth trading hypothesis states that informed traders tend to hide their information. There is a need for such behavior at the time of low volatility and they may achieve this by breaking up their trades into smaller parts. At the time of high volatility informed traders are willing to place large orders at the beginning and the end of the trading day because high volatility provides a sufficient camouflage for their information. We examine volatility pattern for small, medium and large trades and consider how durations between trades and spreads differ between trade size categories. Our sample consists of the data from the Warsaw Stock Exchange, which is organized as an order driven market. We show that medium-size trades are associated with relative large cumulative stock price changes, however these results are not robust when liquidity measures and durations between the consecutive trades are taken into account.




The Impact of Short Selling on Intraday Volatility


Book Description

This paper examines the interrelation between short selling and volatility as differing from previous research in that it focuses on intraday activities, rather than the daily price movements. We demonstrate that the effects of short selling activity change during the two sessions of the day and the rest of trading hours. The study also presents evidence that there is a considerable amount of short selling activity in the Istanbul Stock Exchange (ISE), particularly at the beginning of opening sessions, which significantly impacts the volatility of the market for the rest of the trading day.




Empirical Studies on Volatility in International Stock Markets


Book Description

Empirical Studies on Volatility in International Stock Markets describes the existing techniques for the measurement and estimation of volatility in international stock markets with emphasis on the SV model and its empirical application. Eugenie Hol develops various extensions of the SV model, which allow for additional variables in both the mean and the variance equation. In addition, the forecasting performance of SV models is compared not only to that of the well-established GARCH model but also to implied volatility and so-called realised volatility models which are based on intraday volatility measures. The intended readers are financial professionals who seek to obtain more accurate volatility forecasts and wish to gain insight about state-of-the-art volatility modelling techniques and their empirical value, and academic researchers and students who are interested in financial market volatility and want to obtain an updated overview of the various methods available in this area.




Forecasting Daily Stock Volatility


Book Description

Several recent studies advocate the use of nonparametric estimators of daily price variability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample fit analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Forecast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t-1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. The results have implications for value-at-risk analysis.