Long memory is found in the conditional volatilities of financial returns measured at daily or higher frequencies, as well as in residual cross-products in bivariate series. We test for long memory in conditional correlations by extending the fractionally integrated GARCH model to include...

The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian...

The paper outlines and tests, by means of Monte-Carlo simulations, a simple strategy of using existing non-parametric tests for jumps at the daily frequency to identify jumps at higher sampling frequencies. The suggested strategy allow for identification of the number of jumps and jump times...

The properties of an iterative procedure for the estimation of the parameters of an ARFIMA process are investigated in a Monte Carlo study. The estimation procedure is applied to stock returns data for 15 countries

This paper provides an early warning indicator for bubbles in financial markets. The indicator is based on traditional unit root tests, more precisely on the augmented Dickey-Fuller test and may be used in a repeated manner with rolling samples. The performance of the indicator is tested...

Filtering methods are powerful tools to estimate the hidden state of a state-space model from observations available in real time. However, they are known to be highly sensitive to the presence of small misspecifications of the underlying model and to outliers in the observation process. In this...

We consider estimation of the historical volatility of stock prices. It is assumed that the stock prices are represented as time series formed as samples of the solution of a stochastic differential equation with random and time varying parameters; these parameters are not observable directly...

In this paper we provide MATLAB routines for two major used trading rules, the moving average indicator and MACD oscillator as also the GARCH univariate regression with Monte Carlo simulations and wavelets decomposition, which is an update of an older algorithm

We study nonparametric estimation of the volatility function of a diffusion process from discrete data, when the data are blurred by additional noise. This noise can be white or correlated, and serves as a model for microstructure effects in financial modeling, when the data are given on an...