Modelling Australian Stock Market Volatility PDF Download
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Author: Indika Karunanayake
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
Author: Indika Karunanayake
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
Author: Tim Brailsford
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 31
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Book Description
Author: Andrew Worthington
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 14
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Book Description
"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: David E. Allen
Publisher:
ISBN: 9780729803106
Category : Corporations
Languages : en
Pages : 34
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Book Description
Author: Wei Chi
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
Despite the increasingly tight economic relationship between China and Australia, little attention has been paid to the analysis of stock market volatility spillover across these two countries. This paper, based on industry data, fills the gap in the literature and provides a clear idea of the channels through which volatility is transmitted across countries. This paper finds that the volatility spillover across these two markets is bidirectional while there is single or insignificant spillover across industries between these two countries. More specifically, the results of the Granger causality test show that the stock market volatility spillover is bidirectional between these two markets in the financial, health care, industrials, information technology, and materials industries. One-way volatility spillover exists in the consumer staples industry and there is insignificant volatility spillover in the energy, telecommunications, and utilities industries between the Chinese and Australian stock markets.
Author: Markus Brueckner
Publisher:
ISBN: 9781922352286
Category :
Languages : en
Pages :
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Author: Kevin Daly
Publisher: Routledge
ISBN: 0429852142
Category : Business & Economics
Languages : en
Pages : 145
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Book Description
Published in 1999. The issue of financial volatility, especially since financial deregulation, has given rise to concerns regarding the effects of increased financial volatility on real economic activity. Two issues represent a substantial challenge to financial economists with respect to these concerns. The first relates to the identification of the causes of increased volatility in financial markets. Identification is a first step towards increasing both financial economists' and policy-makers' understanding of the interrelated causes of financial volatility. The second requires linking the effects of increased financial volatility to the real sector of the economy by examining the channels through which financial volatility influences fundamental economic variables. In order to address these two issues, the analysis initially develops and estimates a model which is capable of explaining the financial and business cycle determinates of movements in the conditional volatility of the Australian All Industrials stock market index. Evidence suggests that a significant linkage exists between the conditional volatility of the money supply. Models are then developed to examine how monetary volatility is transmitted to the volatility of financial asset prices, inflation and real output in an open economy. The results indicate that while financial volatility has increased to some extent since the late 1980s, this has been transferred non-uniformly towards increasing volatility of both real and financial activity.
Author: Joanna (Jia Jia) Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 27
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Book Description
To model the price and price volatilities of the Australian wholesale spot electricity markets, the univariate generalised autoregressive conditional heteroskedasticity (GARCH) models have been applied and the inter-relationships in these markets are modelled using multivariate GARCH models. Stochastic volatility (SV) models, as flexible alternatives to GARCH models, have demonstrated their superiority in many financial applications. However, the use of SV models in the modelling of electricity markets is still quite limited. This paper investigates existing multivariate SV models and proposes new SV models with skew error distributions, to model the price and price volatilities of three pairs of markets, selected from four regional electricity markets in Australia, which are shown to be highly correlated in a previous study (Higgs, 2009). Bayesian approach using Markov chain Monte Carlo (MCMC) method is adopted and model implementation is done using the software OpenBUGS. Empirical results show that the price and volatilities of selected markets are strongly correlated across different pairs of regional markets. Based on Deviance Information Criterion, the models with skew error distributions perform better than those with symmetric distribution.
Author: Bart Frijns
Publisher:
ISBN:
Category :
Languages : en
Pages : 38
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Book Description
In this paper we develop and evaluate the information content of an implied volatility index for the Australian stock market. Using price data on Samp;P/ASX 200 index options and SFE SPI 200 index futures options, we develop implied volatility indices with a time to maturity of three months and one month, respectively. When evaluating the information content of both implied volatility indices we find that the implied volatility index based on the Samp;P/ASX 200 index options with a three-month horizon is most informative in terms of explaining stock market returns and forecasting future volatility. For this implied volatility index we find a significant negative and asymmetric relationship between changes in implied volatility and Samp;P/ASX 200 returns, i.e., stock market prices decline more when implied volatility increases than they increase when implied volatility drops. When evaluating the forecasting power of implied volatility for future market volatility we find that the implied volatility index based on the Samp;P/ASX 200 index options contains important information both insample and out-of-sample. In-sample, the implied volatility index significantly improves the fit of a GJR-GARCH(1, 1) model. Out-of-sample, we find that the implied volatility index significantly outperforms the RiskMetrics and GJR-GARCH(1, 1) model, with its highest forecasting power at the one-month forecasting horizon.
Author: Abhay Kumar Singh
Publisher:
ISBN:
Category :
Languages : en
Pages : 7
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Book Description
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.
