Modelling the Intraday Return of Volatility Process in the Australian Equity Market PDF Download
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Author: Andrew Worthington
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 14
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"The data set employed consists of five-minute returns, trading volumes at bid-ask spreads over the period 31 December 2002 to 4 March 2003 for the fifty national and multinational stocks comprising the S&P/ASX 50 index." --p. 1.
Author: Andrew Worthington
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 14
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"The data set employed consists of five-minute returns, trading volumes at bid-ask spreads over the period 31 December 2002 to 4 March 2003 for the fifty national and multinational stocks comprising the S&P/ASX 50 index." --p. 1.
Author: Petko S. Kalev
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Book Description
This study employs firm-specific announcements as a proxy for information flows and investigates the information-volatility relation using high-frequency data from the Australian Stock Exchange. Our analysis reveals a positive and significant impact of the arrival rate of the selected news variable on the conditional variance of stock returns, even after controlling for the potential effects of trading volume and high opening volatility. Furthermore, the inclusion of the news variable in the conditional variance equation of the generalized autoregressive conditional heteroscedastic model also reduces volatility persistence, especially with intraday data. Combined with the evidence that news arrivals display a very strong pattern of autocorrelation, our results are consistent with the Mixture of Distribution Hypothesis, which attributes conditional heteroscedasticity of stock returns to time-dependence in the news arrival process.
Author: Abhay Kumar Singh
Publisher:
ISBN:
Category :
Languages : en
Pages : 7
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On the afternoon of May 6, 2010 Dow Jones Industrial Average (DJIA) plunged about 1000 points (about 9%) in a matter of minutes before rebounding almost as quickly. This was the biggest one day point decline on an intraday basis in the DJIA's history. An almost similar dramatic change in intraday volatility was observed on April 4, 2000 when DJIA dropped by 4.8%. These historical events present very compelling argument for the need of robust econometrics models which can forecast intraday asset volatility. There are numerous models available in the finance literature to model financial asset volatility. Various Autoregressive Conditional Heteroskedastic (ARCH) time series models are widely used for modelling daily (end of day) volatility of the financial assets. The family of basic GARCH models work well for modelling daily volatility but they are proven to be not as efficient for intraday volatility. The last two decades has seen some research augmenting the GARCH family of models to forecast intraday volatility, the Multiplicative Component GARCH (MCGARCH) model of Engle & Sokalska (2012) is the most recent of them. MCGARCH models the conditional variance as the multiplicative product of daily, diurnal, and stochastic intraday volatility of the financial asset. In this paper we use MCGARCH model to forecast intraday volatility of Australia's S&P/ASX-50 stock market, we also use the model to forecast the intraday Value at Risk. As the model requires a daily volatility component, we test a GARCH based estimate and a Realized Variance based estimate of daily volatility component.
Author: Michiel de Pooter
Publisher: Rozenberg Publishers
ISBN: 9051709153
Category :
Languages : en
Pages : 286
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Book Description
This dissertation consists of a collection of studies on two areas in quantitative finance: asset return volatility and the term structure of interest rates. The first part of this dissertation offers contributions to the literature on how to test for sudden changes in unconditional volatility, on modelling realized volatility and on the choice of optimal sampling frequencies for intraday returns. The emphasis in the second part of this dissertation is on the term structure of interest rates.
Author:
Publisher:
ISBN:
Category : Asia
Languages : en
Pages : 796
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Author: Ronald W. Masulis
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
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Author: Jeremias Bekierman
Publisher:
ISBN:
Category :
Languages : en
Pages :
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We propose a mixed frequency stochastic volatility (MFSV) model for the dynamics of intraday asset return volatility. In order to account for long-memory we separate stochastic daily and intraday volatility patterns by introducing a long-run component that changes at daily frequency and a short-run component that captures the remaining intraday volatility dynamics. An additional component captures deterministic intraday patterns. We analyze the stochastic properties of the resulting non-linear state-space model both on the daily and the intraday frequency and show how the model can be estimated in a single step using simulated maximum likelihood based on Efficient Importance Sampling (EIS). We apply the model to intraday returns of five New York Stock Exchange traded stocks. The estimation results indicate distinct dynamic patterns for daily and intradaily volatility components, where about 50% of intraday volatility dynamics are explained by the daily component. In-sample diagnostic tests and an out-of-sample forecasting experiment indicate that already the very basic model specification successfully accounts for the complex dynamic and distributional properties of asset returns both on the intraday and the daily frequency.
Author: Indika Karunanayake
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Author: Tim Brailsford
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 31
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Author: Ronald W. Masulis
Publisher:
ISBN:
Category :
Languages : en
Pages : 52
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