GARCH models are widely used in financial econometrics. However, we show by mean of a simple simulation example that the GARCH approach may lead to a serious model misspecification if the assumption of stationarity is violated. In particular, the well known integrated GARCH effect can be...

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be...

The volatility implied by observed market prices as a function of the strikeand time to maturity form an Implied Volatility Surface (IV S). Practicalapplications require reducing the dimension and characterize its dynamicsthrough a small number of factors. Such dimension reduction is...

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a...

In this paper we study the short term price behavior of December 2008 future prices for EU emissionallowances. We model returns and volatility dynamics of this price showing that a standard ARMA-GARCHframework is not adequate and that the gaussianity assumption is rejected due to the occurrence...

Driven by the rise in computational power, it has become popular to measure integrated variance with high-frequency squared returns. Though the squared return is a natural choice as a variance estimate, it is not the most efficient one for a given interval length. Extreme-value based estima-...

In this paper, we consider an incomplete market framework and explain how to use jointly observed prices of the underlying asset and of some derivatives written on this assetfor an efficient pricing of other derivatives. This question involves two types of moment restrictions, which can be...

High-dimensional regression problems which reveal dynamic behavior are typicallyanalyzed by time propagation of a few number of factors. The inference on thewhole system is then based on the low-dimensional time series analysis. Such highdimensional problems occur frequently in many different...