We conduct out-of-sample density forecast evaluations of the affine jump diffusion models for the S&P 500 stock index and its options’ contracts. We also examine the time-series consistency between the model-implied spot volatilities using options & returns and only returns. In particular, we...

The nonlinear modelization has experimented a great resurgence of the hand of Chaos Theory, which shown the possibility of obtaining complex behaviors produced endogenously by the dynamics of the model, without the necessity to include exogenous random shocks. On the other hand, the importance...

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures...

Intraday data of 26 German stocks are used to investigate whether the information contained in trading volume and number of trades as well as in various specifications of overnight returns can improve one-step-ahead volatility forecasts. For this purpose, a HAR model of the realized range...

We consider a method for producing multivariate density forecasts that satisfy moment restrictions implied by economic theory, such as Euler conditions. The method starts from a base forecast that might not satisfy the theoretical restrictions and forces it to satisfy the moment conditions using...

Electricity price time series usually exhibit some form of nonstationarity, corresponding to long-term behavior, one or more periodic components as well as dependence on calendar effects. As a result, modeling electricity prices requires accounting for both long-term and periodic components. In...