Intraday Volatility Forecast in Australian Equity Market

Abstract

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.

Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH

“…The sigma forecast plot and the VaR backtesting plot for the MC-GARCH(1,1) model under the skewed Student's-t distribution are displayed below in Figures 7 and 8 respectively. As noted in Singh et al (2013), the spikes in the VaR forecasts as shown in the backtest plot in Figure 8 is due to the seasonal component during the opening of each trading day.…”

“…Therefore, it can be concluded that the MC-GARCH models are able to forecast accurately the risk measure ES. As noted in Singh et al (2013), the spikes in the VaR forecasts as shown in the backtest plot in Figure 8 is due to the seasonal component during the opening of each trading day.…”

“…Previous studies have shown that, indeed, the MC-GARCH model is well capable of forecasting intraday volatility and risk metrics. The MC-GARCH model was applied to three equally spaced intervals of 1 min, 5 min and 10 min intraday data of Australia's S&P/ASX-50 stock market by Singh et al (2013). The model yielded satisfactory results for intraday VaR forecast.…”

High frequency volatility forecasting: A new approach using a hybrid ANN‐MC‐GARCH model

“…Subsequent studies have only reinforced the previous findings. Singh et al (2013) applied the MC-GARCH model to three high frequency intraday intervals of 1 min, 5 min and 10 min for Australia's S&P/ASX-50 stock market and found that the model fit well to the intraday returns. This was supported by Diao and Tong (2015) who used the 5-min returns of the CSI 300 index.…”

Forecasting multivariate VaR and ES using MC-GARCH-Copula model

“…Another context where the implementation of VaR and ES models is relatively under-developed is the currency exchange market. Most studies seem to test the performance of VaR and ES models using stocks or stock indices, see for example the studies of Palaro and Hotta (2006), Ruppert and Matteson (2011) or Singh et al (2013). Among all the financial asset markets, the currency market is the largest one in the world in terms of trading volume.…”