GMM Estimation for Long Memory Latent Variable Volatility and Duration Models

We study the rate of convergence of moment conditions that have been commonly used in the literature for Generalised Method of Moments (GMM) estimation of short memory latent variable volatility models. We show that when the latent variable possesses long memory, these moment conditions have an n^{1/2-d} rate of convergence where 0GMM, long memory, stochastic volatility and durations

The Variance Ratio Statistic at large Horizons

We make three contributions to using the variance ratio statistic at large horizons. Allowing for general heteroscedasticity in the data, we obtain the asymptotic distribution of the statistic when the horizon k is increasing with the sample size n but at a slower rate so that k/n¨0. The test is shown to be consistent against a variety of relevant mean reverting alternatives when k/n¨0. This is in contrast to the case when k/n¨ƒÂ>0, where the statistic has been recently shown to be inconsistent against such alternatives. Secondly, we provide and justify a simple power transformation of the statistic which yields almost perfectly normally distributed statistics in finite samples, solving the well known right skewness problem. Thirdly, we provide a more powerful way of pooling information from different horizons to test for mean reverting alternatives. Monte Carlo simulations illustrate the theoretical improvements provided.Mean reversion, frequency domain, power transformations

On testing the adequacy of stable processes under conditional heteroscedasticity

We consider a recently proposed method of estimating the tail index and testing the goodness-of-fit of dependent stable processes. Through Monte Carlo simulations, we evaluate the ability of the procedure to distinguish between stable and non-stable processes in the presence of non-linear dependence and to estimate the tail index of the distribution. We then apply the test to black market East European exchange rates, whose distributional and tail behaviour has been analysed previously in the literature. After adjusting for seasonality, we conclude, unlike the earlier analysis, that a stable process cannot be rejected as a model for some of the currencies. Estimates of the tail index for these currencies are also obtained.Statistics Working Papers Serie

The Variance Ratio Statistic at Large Horizons

We make three contributions to using the variance ratio statistic at large horizons. Allowing for general heteroscedasticity in the data, we obtain the asymptotic distribution of the statistic when the horizon k is increasing with the sample size n but at a slower rate so that k=n ! 0. The test is shown to be consistent against a variety of relevant mean reverting alternatives when k=n ! 0. This is in contrast to the case when k=n ! – > 0; where the statistic has been recently shown to be inconsistent against such alternatives. Secondly, we provide and justify a simple power transformation of the statistic which yields almost perfectly normally distributed statistics in finite samples, solving the well known right skewness problem. Thirdly, we provide a more powerful way of pooling information from different horizons to test for mean reverting alternatives. Monte Carlo simulations illustrate the theoretical improvements provided. --Mean reversion,Frequency domain,Power transformation

Spectral tests of the martingale hypothesis under conditional heteroscedasticity

We study the asymptotic distribution of the sample standardized spectral distribution function when the observed series is a conditionally heteroscedastic martingale difference. We show that the asymptotic distribution is no longer a Brownian bridge but another Gaussian process. Furthermore, this limiting process depends on the covariance structure of the second moments of the series. We show that this causes test statistics based on the sample spectral distribution, such as the CramÃÂér von-Mises statistic, to have heavily right skewed distributions, which will lead to over-rejection of the martingale hypothesis in favour of mean reversion. A non-parametric correction to the test statistics is proposed to account for the conditional heteroscedasticity. We demonstrate that the corrected version of the CramÃÂér von-Mises statistic has the usual limiting distribution which would be obtained in the absence of conditional heteroscedasticity. We also present Monte Carlo results on the finite sample distributions of uncorrected and corrected versions of the CramÃÂér von-Mises statistic. Our simulation results show that this statistic can provide significant gains in power over the Box-Ljung-Pierce statistic against long-memory alternatives. An empirical application to stock returns is also provided.Statistics Working Papers Serie

Estimation of Mis-Specified Long Memory Models

We study the asymptotic behaviour of frequency domain maximum likelihood estimators of mis-specified models of long memory Gaussian series. We show that even if the long memory structure of the time series is correctly specified, mis-specification of the short memory dynamics may result in parameter estimators which are slower than pn consistent. The conditions under which this happens are provided and the asymptotic distribution of the estimators is shown to be non-Gaussian. Conditions under which estimators of the parameters of the mis-specified model have the standard pn consistent and asymptotically normal behaviour are also provided. --

Tracing the Source of Long Memory in Volatility

We study the effects of trade duration properties on dependence in counts (number of transactions) and thus on dependence in volatility of returns. A return model is established to link counts and volatility. We present theorems as well as a conjecture relating properties of durations to long memory in counts and thus in volatility. We then apply several parametric duration models to empirical trade durations and discuss our findings in the light of the theorems and conjecture.

Forecasting Realised Volatility using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment

We study the modelling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalising the volatility in high frequency data is proposed, that allows for slowly varying seasonality. Using both simulated as well as real data, we compare the forecasting performance of the LMSV model for forecasting realised volatility to that of a linear long memory model fit to the log realised volatility. The performance of the new seasonal adjustment is also compared to a recently proposed procedure using real data. --

Propagation of Memory Parameter from Durations to Counts

We establish sufficient conditions on durations that are stationary with finite variance and memory parameter $d \in [0,1/2)$ to ensure that the corresponding counting process $N(t)$ satisfies $\textmd{Var} \, N(t) \sim C t^{2d+1}$ ($C>0$) as $t \rightarrow \infty$, with the same memory parameter $d \in [0,1/2)$ that was assumed for the durations. Thus, these conditions ensure that the memory in durations propagates to the same memory parameter in counts and therefore in realized volatility. We then show that any Autoregressive Conditional Duration ACD(1,1) model with a sufficient number of finite moments yields short memory in counts, while any Long Memory Stochastic Duration model with $d>0$ and all finite moments yields long memory in counts, with the same $d$. Finally, we present a result implying that the only way for a series of counts aggregated over a long time period to have nontrivial autocorrelation is for the short-term counts to have long memory. In other words, aggregation ultimately destroys all autocorrelation in counts, if and only if the counts have short memory.Long Memory Stochastic Duration, Autoregressive Conditional Duration, Rosenthal-type Inequality.

On the Asymptotic Power of the Variance Ratio Test

The variance ratio test statistic, which is based on k-period differences of the data, is commonly used in empirical finance and economics to test the random walk hypothesis. We obtain the asymptotic power function of the variance ratio test statistic when the differencing period k is increasing with the sample size n such that k/n → δ > 0. We show that the test is inconsistent against a variety of mean reverting alternatives, confirm the result in simulations, and then characterise the functional form of the asymptotic power in terms of δ and these alternatives