A least squares estimation approach for the estimation of a GARCH (1,1) model is developed. The asymptotic properties of the estimator are studied given mild regularity conditions, which require only that the error term has a conditional moment of some order. We establish the consistency, asymptotic normality and the law of iterated logarithm for our estimate. The finite sample properties are a...

We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables in the information set, including implied volatility backed out from option prices. Realized volatility is separated into its continuous and jump components, and the heterogeneous autoregressive (HAR) model is applied with implied volatility as an additional forecasting variabl...

This paper proposes a Gaussian estimator for nonlinear continuous time models of the short term interest rate. The approach is based on a stopping time argument that produces a normalizing transformation facilitating the use of a Gaussian likelihood. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over the discrete approximation metho...

In this paper we apply the wavelets methodology to the analysis of the comovements of for some MENA countries from June 1997 until March 2005. We decompose weekly stock market returns into di¤erent time scale components using the non-decimated discrete wavelet transform and then analyze the relationships among these variables at the di¤erent time scales. Keywords : Stock market returns, Wavelet...

The use of GARCH models with stable Paretian innovations in financial modeling has been recently suggested in the literature. This class of processes is attractive because it allows for conditional skewness and leptokurtosis of financial returns without ruling out normality. This contribution illustrates their usefulness in predicting the downside risk of financial assets in the context of mode...

The present paper seeks to cast scepticism on the validity and value of the results of all earlier studies in India on energy demand analysis and forecasting based on time series regression, on three grounds. (i) As these studies did not care for model adequacy diagnostic checking, indispensably required to verify the empirical validity of the residual whiteness assumptions underlying the very ...

We perform a series of Monte Carlo experiments in order to evaluate the impact of data transformation on forecasting models, and ̄nd that vector error-corrections dominate di®erenced data vector autoregressions when the correct data transformation is used, but not when data are incorrectly tansformed, even if the true model contains cointegrating restrictions. We argue that one reason for this ...

This paper studies the information content of some Ifo indicators. In particular, we investigate whether two Ifo indicators, one on the current business situation, the other on current production development, provide information on revisions of German industrial production. A new feature of our analysis is the construction and use of a real-time dataset. We conclude that the Ifo indicators play...

This paper reviews the analysis of the threshold autoregressive, smooth threshold autoregressive, and Markov switching autoregressive models from the Bayesian perspective. For each model we start by describing a baseline model and discussing possible extensions and applications. Then we review the choice of prior, inference, tests against the linear hypothesis, and conclude with models selectio...

Multiresolution wavelet analysis is a natural way to decompose economic time series into components of various frequencies: long-run trend, business-cycle component, and high frequency noise. This paper illustrates the method on real GNP and inflation. The business-cycle component of the wavelet-filtered series closely resembles the series filtered by the approximate bandpass filter (Baxter and...