Gerard Gannon

Structural models: Intra/Inter-day volatility transmission and spillover persistence of the HSI, HSIF and S&P500 futures

Abstract An example is provided in this paper of a structural simultaneous volatility model which captures dominating volatility transmission from the Hang Seng Index Futures to the Hang Seng Index. This structure is different to multivariate GARCH structures by accounting for endogenous volatility effects in the system. Volatility spillovers from the Standard and Poors 500 Index Futures are embedded within the structural system and found to be very important. This structure jointly accounts for intra-day (15 minute) volatility effects and inter-day volatility effects across the Hong Kong asset price processes. Structural error correction effects enter the second round maximum likelihood system estimator providing sharp estimates of identifiable parameters of interest.

First and second order inefficiency in Australasian currency markets

Abstract The purpose of this paper is to present a modified test for common roots in which GARCH effects in the correlating combinations of residuals is accounted for. This procedure extends the multivariate testing framework by accounting for common volatility and allowing for this effect in the test statistics. The example provides evidence of first-and/or second-order inefficiency in the Australasian spot currency markets. Significant links are found between the Australian Dollar and other major currencies trading in Asia Pacific markets. The Australian Dollar in one of the top six traded currencies internationally and speculative activity is very strong.

Comparing Trading Performance of the Constant and Dynamic Hedge Models: A Note

The constant and dynamic hedge models, with the presence of transaction costs are compared for the Share Price Index futures contract trading on the Sydney Futures Exchange. The optimal hedge ratio is estimated by using a dynamic, bivariate two-stage model for the return equation with a dynamic GARCH error structure for the conditional hedge ratios. When portfolio projections are compared based on their profit positions (net of transaction costs), the GARCH hedge model dominates the next best competitor in terms of trading profit.

Simultaneous Volatility Transmission and Spillover Effects

Simultaneous volatility models are developed and shown to be separate from multivariate GARCH estimators. An example is provided that allows for simultaneous and unidirectional volatility and volume of trade effects. These effects are tested using intraday data from the Australian cash index and index futures markets. Overnight volatility spillover effects from the United States S&P500 index futures markets are tested using alternative estimates of this US market volatility. The simultaneous volatility model proves to be robust to alternative specifications of returns equations and to misspecification of the direction of volatility causality.

Contemporaneous intraday volume, option, and futures volatility transmissions across parallel markets

The primary objective of this article is to investigate volatility transmission across three parallel markets operating on the Sydney Futures Exchange (SFE), both within and out of sample. Half-hourly observations are sampled from transaction data for the share price index (SPI) futures, SPI futures options, and 90-day bank accepted bill (BAB) futures markets, and the analysis is carried out using the simultaneous volatility (SVL) system of equations as well as competing volatility models. The results confirm the poor ability of GARCH models to fit intraday data. This study also applies an artificial nesting procedure to evaluate the out-of-sample volatility forecasts. Implied volatility has very limited (if any) predictive power when evaluated in isolation, whereas the SVL model with implied volatility embedded provides incremental information relative to competing model forecasts.

Information effect of economic news: SPI futures

The present study investigates the behaviour of Share Price Index (SPI) futures returns, volatility, and trading volume behaviour around the announcement of Current Account Deficit (CAD), Gross Domestic Product (GDP), and Inflation (CPI). The futures market data are sampled at 1-, 5-, and 10-min intervals at the announcement time. After controlling for risk, a significant positive abnormal return can be earned based on the good news release. However, it is unlikely that traders could make an economic profit by exploiting this effect. In this sense, this futures market returns are found to react efficiently to good news. Volatility behaviour around announcements provides the same conclusion. As for the relationship between returns, volatility, and volume upon information arrival, returns are positively related to trading volume, which is inconsistent with the ‘short sales constraint’ theory. Trading volume is found to increase as the level of volatility rises. The redenomination of the SPI futures and options contract from A

Modelling Regulatory Change V's Volume, of Trading Effects in HSIF and HSI Volatility

100 to A

Evaluation of volatility forecasts in an economic value framework

25 per basis point is found to increase trading volume in excess of that expected due to the redenomination. However, market return and volatility are unaffected by the redenomination.

The Dynamics of Credit Spreads on Latin American Eurobonds

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

Market makers v's the general public: a first look at S&P500 futures trade data

Abstract The GARCH, moving average, previous period volatility and AVV models are compared relative to market implied volatility, first in a simulated market, and then using actual options data. The first set of analysis employs the simulation framework developed in a sequence of papers and published by Engle, Hong, Kane, and Noh (1993). This is an incremental profit methodology designed to rank volatility forecasts within a daily trading framework in an artificial options market. This type of analysis is important for exchanges and regulators when considering daily margin setting. The second set of analysis employs actual option trades in evaluating the ranking of volatility forecasts. This methodology accounts for the observed feature that options on index futures are typically held until maturity in the market considered. This type of analysis is important for institutions and large traders for position setting. The rankings based on daily trades are quite different from those based on positions held until expiration. Australian data on the Share Price Index (SPI) futures and options on SPI futures, traded on the Sydney Futures Exchange (SFE), from 1989 to 1995 are employed in this analysis.