One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the...

In this paper, we provide new evidence on the empirical usefulness of various simple seasonal models, and underscore the importance of carefully designing criteria by which one judges alternative models. In particular, we underscore the importance of both choice of forecast or simulation horizon...

This paper investigates the dynamics of the leverage effect over time, using high frequency data. By applying Realized Kernel techniques, a more precise estimate of Realized Correlation ¨C compared to standard subsampled estimators of Realized Correlation ¨C is derived. This new measure avoids...

There is an inherent problem with comparing and ranking competing Value at Risk (VaR) and Expected shortfall (ES) models since we are measuring only a single realization of the underlying data generation process. The question is whether there is any significant statistical difference in the...

This paper concerns goodness-of-fit test for semiparametric copula models. Our contribution is two-fold: we first propose a new test constructed via the comparison between "in-sample" and "out-of-sample" pseudolikelihoods, which avoids the use of any probability integral transformations. Under...

Usually, the time series of electricity prices in different markets show structural changes due to economic conditions related to supply, demand or specific market rules. While some of the proposals for modeling these series are based on mean reversion models inspired by the financial literature...

In this chapter we consider a class of parametric spectrum estimators based on a generalized linear model for exponential random variables with power link. The power transformation of the spectrum of a stationary process can be expanded in a Fourier series, with the coefficients representing...

This paper is concerned with forecasting the Finnish inflation rate. It is being forecast using linear autoregressive and nonlinear neural network models. Perhaps surprisingly, building the models on the nonstationary level series and forecasting with them produces forecasts with a small er root...

Taking advantage of a trades-and-quotes database, the main stylized facts and dynamic properties of a time series related to spot precious metals, that is, gold, silver, palladium, and platinum, are documented. The behavior of spot prices, returns, volume, and selected liquidity measures is...

Inference about predictive ability is usually carried-out in the form of pairwise comparisons between two forecasting methods. Nevertheless, some interesting questions are concerned with families of models and not just with a couple of forecasting strategies. For instance: Are time-series models...