Interaction Between the London and New York Stock Markets During Common Trading Hours

This paper examines the spill-overs in both returns and volatility between the London and New York stock markets during overlapping trading hours. Using high-frequency data for the FTSE 100 and S&P 500 stock index futures, we estimate the seasonal patterns in volatility using the Flexible Fourier Form specification of Gallant (1981). For both markets, volatility is estimated to be higher in the morning and late afternoon, as compared to the rest of the day. The estimated seasonals are used to adjust the returns before conducting the lead-lag analysis. The results indicate that both markets influence each other, although the impact of the US on the UK is clearly stronger

Estimating Daily Volatility from Intraday Data

This study proposes a new approach to the estimation of daily volatility. This approach is efficient (in the sense of using all available intraday price data) and unbiased (in the sense of accounting for the high levels of autocorrelation found in intraday price data). Empirical analysis of this new estimator on All Ordinaries Index Futures shows that it is less biased and more efficient than traditional volatility estimators. Furthermore this new approach confirms the GARCH(1,1) specification of the time series behaviour of daily volatility; namely that daily volatility follows an ARMA( 1,1) process through time

Bayesian Soft Target Zones

Several authors have postulated econometric models for exchange rates restricted to lie within known target zones. However, it is not uncommon to observe exchange rate data with known limits that are not fully `credible'; that is, where some of the observations fall outside the stated range. An empirical model for exchange rates in a soft target zone where there is a controlled probability of the observed rates exceeding the stated limits is developed in this paper. A Bayesian approach is used to analyse the model, which is then demonstrated on Deutschemark-French franc and ECU-French franc exchange rate data

A Threshold Error Correction Model for Intraday Futures and Index Returns

Index-futures arbitragers only enter into the market if the deviation from the arbitrage relation is large enough to compensate for transaction costs and associated interest rate and dividend risks. We estimate the band around the theoretical futures price within which arbitrage is not profitable for most arbitragers, using a threshold autoregression model. Combining these thresholds with an error correction model, we can make a distinction between the effects of arbitragers and infrequent trading on index and futures returns

Bayesian Arbitrage Threshold Analysis

A Bayesian estimation procedure is developed for estimating multiple regime (multiple threshold) vector autoregressive models appropriate for deviations from financial arbitrage relationships. This approach has clear advantages over classical stepwise threshold autoregressive analysis. Whereas classical procedures first have to identify thresholds and then perform piecewise autoregressions, we simultaneously estimate threshold and autoregression parameters. To illustrate the Bayesian procedure, we estimate a no-arbitrage band within which index futures arbitrage is not profitable despite (persistent) deviations from parity

Mixtures of Tails in Clustered Automobile Collision Claims

Knowledge of the tail shape of claim distributions provides important actuarial information. This paper discusses how two techniques commonly used in assessing the most appropriate underlying distribution can be usefully combined. The maximum likelihood approach is theoretically appealing since it is preferable to many other estimators in the sense of best asymptotic normality. Likelihood based tests are, however, not always capable of discriminating among non-nested classes of distributions. Extremal value theory offers an attractive tool to overcome this problem. A much larger set of distribution classes is nested by their tail parameter. This paper shows that both estimation strategies can be usefully combined when the data generating process is characterized by strong clustering in time and size. We find that the extreme value theory is a useful starting point in detecting the appropriate distribution class. Once that has been achieved, the likelihood-based EM-algorithm is proposed to capture the clustering phenomena. Clustering is particularly pervasive in actuarial data. An empirical application to a four-year data set of Dutch automobile collision claims is therefore used to illustrate the approach

Diversification Meltdown or the Impact of Fat Tails on Conditional Correlation?

A perceived increase in correlation during turbulent market conditions implies a reduction in the benefits arising from portfolio diversification. Unfortunately, it is exactly then that these benefits are most needed. To determine whether diversification truly breaks down, we investigate the robustness of a popular conditional correlation estimator against alternative distributional assumptions. Analytical results show that the apparent meltdown in diversification could be a result of assuming normally distributed returns. A more realistic assumption - the bivariate Student-t distribution - suggests that there is little empirical support for diversification meltdown