Jonathan Dark

Basis Convergence and Long Memory in Volatility When Dynamic Hedging with Futures

When market returns follow a long memory volatility process, standard approaches to estimating dynamic minimum variance hedge ratios (MVHRs) are misspecified. Simulation results and an application to the S&P 500 index document the magnitude of the misspecification that results from failure to account for basis convergence and long memory in volatility. These results have important implications for the estimation of MVHRs in the S&P 500 example and other markets as well.

Impact of capital control measures on the Malaysian stock market: A multiresolution analysis

Purpose - The purpose of this paper is to examine the extent to which the capital control measures implemented by the Malaysian central bank in late 1998 had an influence on segmenting the Malaysian equity market from other major equity markets. Design/methodology/approach - The S&P 500, the Nikkei 225 Index, the STI Index and the KLSE Composite Index are considered. The discrete wavelet transform technique – “Haar” is employed to decompose the series into various time scales during the pre- and post-capital control periods in Malaysia. The decomposed series are then used to estimate the interdependence between KLSE Composite Index with the other three markets at various time scales. Findings - The empirical findings support three conclusions. First, in the pre-capital control period, Singapore is the most influential market followed by the US across all time scales in transmitting news into Malaysia. Second, after the imposition of capital controls, the spillover effects from Singapore to Malaysia have declined substantially, suggesting a reduced integration between these two markets. Finally, in the post-capital control period, all three markets appear to be imparting a similar but moderate level of influence on the Malaysian market. Research limitations/implications - To explore the return and volatility spillovers, the use of return and volatility series at different time scales provided a greater level of insight into the dynamics than the standard approaches which employ only one series in the time domain. Originality/value - The results from this paper will have potential implications for asset allocation, the pricing of domestic securities, the implementation of global hedging and trading strategies and the evaluation of regulatory proposals to restrict international capital flows.

Exchange Traded Contracts for Difference: Design, Pricing and Effects

Contracts for Difference (CFDs) represent a significant financial innovation in the design of futures contract. Their use in over-the-counter markets has grown significantly and has created controversy in the UK, but to date there have been no published academic studies examining CFD markets. In 2007, the Australian Securities Exchange (ASX) introduced exchange-traded CFDs on individual stocks and other financial instruments. This paper analyzes the contract design and consequences for pricing relationships between CFDs and the underlying stocks. Using the unique database of ASX CFD trades and quotes, we test empirically whether the pricing relationship conforms to theoretical expectations, how spreads in the derivative market are related to those in the physical market, and draw out implications for successful design and trading arrangements for the introduction of new derivative contracts.

Influence Diagnostics for Multivariate GARCH Processes

This article presents diagnostics for identifying influential observations when estimating multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models. We derive influence diagnostics by introducing minor perturbations to the conditional variances and covariances. The derived diagnostics are applied to a bivariate GARCH model of daily returns of the S&P500 and IBM. We find that univariate diagnostic procedures may be unable to identify the influential observations in a multivariate model. Importantly, the proposed curvature-based diagnostic identified influential observations where the correlation between the two series had a major change. These observations were not identified as influential using the univariate diagnostics for each asset separately. When estimating the bivariate GARCH model allowing for weights at influential observations, we found that the time-varying correlations behaved differently from that implied by the model ignoring influential observations. The application therefore highlights the importance of extending univariate diagnostic procedures to multivariate settings.

A comparison of developed and emerging equity market return volatility at different time scales

Purpose - The purpose of this paper is to examine the volatility of daily returns in a sample of developed and emerging equity markets at different time scales through wavelet decomposition. Such information is vital for international investors who have different time horizons for their investment decisions and trading strategies. Design/methodology/approach - The wavelet technique used here allows the return series to be viewed at different frequency by decomposing the series into different time horizons known as time scales. The decomposed return series enable investigation of return variability at different return intervals. Findings - In an analysis at different time scales, there is no evidence to suggest that the return dynamics of developed and emerging markets are different. In both types of markets, return variance is time scale dependent, satisfying a pure power law process, and the variability in returns is more likely to be due to the dynamics at the lower time scales. While emerging markets generally exhibit a higher level of volatility, the relative contribution from each time scale is quite similar to that of the developed markets. Originality/value - The difference in the return dynamics between emerging and developed markets is observed at the lowest time scale. This is an indication that differences in the return dynamics between the two types of markets may be more likely in the short term (high frequency) rather than in the long term. A plausible reason for this is speculative trading. Such information is vital for international investors who have different time horizons for their investment decisions and trading strategies.

A Critique of Minimum Variance Hedging

This paper provides a critique of minimum variance hedging using futures. The paper develops the conventional minimum variance hedge ratio (MVHR) and discusses its estimation. A review of the wide variety of alternative methods used to construct MVHRs is then performed. These methods highlight many of the potential limitations in the conventional framework. The paper argues that the literature should focus more on the assumptions underlying the conventional MVHR, rather than improving the techniques used to estimate the conventional MVHR.

Randomized controlled trial of probiotics after colonoscopy.

Up to 20% of patients have ongoing abdominal symptoms at day 2 and beyond following colonoscopy. It was hypothesized that some of these symptoms are related to alterations in gut microbiota secondary to bowel preparation and would improve with probiotics compared with placebo.

Modeling the Joint Dynamics of Spot and Futures Markets with a Regime Switching Long Memory Volatility Process

The methods used to model the dynamics of spot and futures markets ignore the effects of changes in the cost of carry (COC) on the dynamics of the basis and its rate of convergence. This is of particular importance given the historically low short term interest rates currently experienced in many countries. This paper proposes a bivariate model that exhibits long memory in volatility, basis convergence and regime switches that occur via a latent markov process. Monte carlo simulation reveals that the model parameters can be well estimated via maximum likelihood. The proposed model is supported by applications to equity and currency markets. It is found that regime switches in basis dynamics occur between high and low volatility states associated with high and low absolute values of the COC. In and out of sample forecasts generally support the proposed model, however minimum variance hedge ratio estimation provides mixed support.

A Selectivity Corrected Time Varying Beta Estimator

This paper explores two issues in beta estimation, specifically, time variation and thin trading. In a multivariate GARCH approach, the paper conducts an analysis of the importance of assumptions made about the correlation structure in the multivariate GARCH model. The results of Monte Carlo analysis and an empirical application to Australian stock data demonstrate that it is better to allow for time variation in the correlation structure. The paper then develops a selectivity corrected time varying beta estimator. The results of a Monte Carlo experiment show that the new estimator performs well in handling the censoring in the data. Further, when the model is applied to individual stock data for Australia it provides a model that captures the impacts of censoring and thin trading on time varying beta risk.