Craig Ellis

Decomposing Intraday Dependence in Currency Markets: Evidence from the Aud/Usd Spot Market

The local Hurst exponent, a measure employed to detect the presence of dependence in a time series, may also be used to investigate the source of intraday variation observed in the returns in foreign exchange markets. Given that changes in the local Hurst exponent may be due to either a time-varying range, or standard deviation, or both of these simultaneously, values for the range, standard deviation and local Hurst exponent are recorded and analyzed separately. To illustrate this approach, a high-frequency data set of the spot Australian dollar/US dollar provides evidence of the returns distribution across the 24-hour trading ‘day’, with time-varying dependence and volatility clearly aligning with the opening and closing of markets. This variation is attributed to the effects of liquidity and the price-discovery actions of dealers.

Scaling the Volatility of Credit Spreads: Evidence from Australian Dollar Eurobonds

Many asset pricing models require an annualised risk coefficient which is determined by the linear rescaling of the variance from other time intervals. However, this approach may not be appropriate for dependent time series. This paper investigates the scaling relationships for daily credit spreads, from January 1986 to May 1998, between AAA, AA and A rated Australian dollar denominated Eurobonds with maturities of 2, 5, 7 and 10 years. We find evidence of a term structure and co-movement in credit spreads by maturity. We also find the credit spread return series were time variant, leptokurtic, autocorrelated and exhibited different degrees of negative long-term dependence. The series all displayed similar scaling properties with the estimated standard deviation, based upon a scaling at the square root of time, significantly underestimating the actual level of risk predicted from a normal distribution. These results have implications for credit spread derivatives.

Scaling Relationships of Gaussian Processes

Asset returns conforming to a Gaussian random walk are characterised by the temporal independence of the moments of the distribution. Employing currency returns, this note demonstrates the conditions that are necessary for risk to be estimated in this manner.

Are long-term return anomalies illusions?: Evidence from the spot Yen

Abstract This study investigates the sensitivity of the long-term return anomaly observed in the US

Technical trading system performance in the Australian share market: Some empirical evidence

/Yen currency market to sample and method bias during the period from 11 October 1983 to 21 July 1999. Initially the CUSUM statistic is employed to identify subperiods of sign shifts in the mean returns. Then the dependence in these subperiods is investigated using the Hurst [Hurst, H.E., 1951. Trans. Am. Soc. Civil Eng. 116, 770–799] and the Lo [Lo, A.W., 1991. Econometrica 59 (5), 1279–1313] rescaled range statistics. The results suggest that rejection of the random null is conditional on both the procedure and the period being tested. The Hurst test may incorrectly lead to a rejection of the null hypothesis. However, the Lo test is a more appropriate approach to the data in this study. Thus previous research based on the Hurst test may have inadvertently introduced a method bias.

Real estate 'value' stocks and international diversification

This study incorporates Australian All Ordinaries Share Price Index time series data over the period 1987–1991, and firstly considers the ability of technical trading systems to generate returns greater than a Buy-Hold control. Secondly it aims to test for the weak-form efficiency of the Australian Share Market. Efficiency is considered in both the statistical context and in terms of the trading systems net returns. Statistical test results provide the Australian share market to be weak-form efficient. In confirmation of this result, none of the trading systems employed were able to earn a return greater to the Buy-Hold control strategy once transactions costs were taken into consideration.

Expert System Portfolios of Australian and UK Securitised Property Investments

In recent years there has been an increased interest in the extent to which managers can improve their property portfolio position through international diversification. Much of this interest has centred on the use of various statistical/econometric tests of time‐varying correlations and long‐run equilibrium positions using whole of country property indices. In this paper, a short‐run tactical asset allocation approach to securitized property is adopted. Using neural network methodology, a neural network model that ‘learns’ well‐established rules of portfolio investment is built. The model uses a set of individual property companies across three of the most highly securitized property markets in the world viz. the US, the UK and Australia. The standpoint of a UK investor is adopted and the model is asked to compare portfolios constructed purely from domestic assets with portfolios constructed from internationally held assets allowing for foreign exchange adjustments. When the foreign exchange risk is actively managed, the outcomes from the analysis suggest that the gains from hedging are conditional on both the return to the unhedged position and the volatility of the underlying currency being hedged.

Volatility Scaling in Foreign Exchange Markets

Abstract This paper examines whether a rule-based expert system is capable of outperforming the general property market, as well as randomly constructed portfolios from the market. While neural network expert systems have been used in property research, there appears little in the literature on the application of rule-based expert systems. Secondly, the paper considers whether there are benefits to international investment in real estate securities. The perspective of the analysis is that of an Australian investor investing in both the domestic market and the UK property market. Several results ensue including the failure of the rule-based system to significantly outperform the market or the random portfolios on a risk-adjusted returns basis, and the failure of hedging to secure a positive rate of return to the portfolio.

Scaling the volatility of credit spreads

When distributions are non-Gaussian or display linear dependence it may not be appropriate to annualise the risk coefficient determined by the linear rescaling of the variance from other time intervals. This paper investigates the scaling relationships for daily spot foreign currency returns: the Deutsche mark-U.S. dollar (DMK/USD), the Swiss franc-USD (SWF/USD), the Japanese yen-USD (JPY/USD), and the English pound-USD (GBP/USD), from February 1985 to May 1998. We find that all four series were non-Gaussian and displayed similar scaling properties with the estimated variance, based upon a scaling at the square root of time, significantly underestimating the actual level of risk predicted from a normal distribution. The economic implications of these results were then established by estimating the premiums on a series of foreign currency options using a variation of a Black-Scholes model with varying strike prices. Based on a 180 day maturity, the results suggested that the inappropriate scaling of risk underestimated the price of call and put options for DMK/USD, SWF/USD and JPY/USD, but not for GBP/USD.

The TAFE/VET Pathways Student Experience in Higher Education

The linear rescaling of the variance of an assets return is used by many asset pricing models when an annualised risk coefficient is required. However, this approach may not be appropriate for time series, which are not independent and identically distributed (IID). This paper investigates the scaling relationships for daily credit spreads, from January 1995 to May 1998, between AAA-, AA-, and A-rated Australian dollar denominated Eurobonds with maturities of 2, 5, 7, and 10 years. The credit spread return all display similar scaling properties with the estimated standard deviation, based upon a scaling at the square root of time, significantly underestimating the actual level of risk predicted from a normal distribution. These results have implications for risk managers and trading of credit spread instruments.