It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of up-or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework to assess the economic forecast value when loss functions (or success measures) are properly formulated to account for realized signs and realized magnitudes of directional movements. We discuss a general approach to evaluate (directional) forecasts which is simple to implement, robust to outlying or unreasonable forecasts and which provides an economically interpretable loss/success functional framework. As such, the measure of directional forecast value is a readily available alternative to the commonly used squared error loss criterion.
In this paper we adopt a principal components analysis (PCA) to reduce the dimensionality of the term structure and employ autoregressive models (AR) to forecast principal components which, in turn, are used to forecast swap rates. Arguing in favor of structural variation, we propose data driven, adaptive model selection strategies based on the PCA/AR model. To evaluate ex-ante forecasting performance for particular rates, different forecast features such as mean squared errors, directional accuracy and big hit ability are considered. It turns out that relative to benchmark models, the adaptive approach offers additional forecast accuracy in terms of directional accuracy and big hit ability.
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. ABSTRACT In this paper we investigate the profitability of 'skewness trades' and 'kurtosis trades' based on comparisons of implied state price densities versus historical densities. In particular, we examine the ability of SPD comparisons to detect structural breaks in the options market behaviour. While the implied state price density is estimated by means of the Barle and Cakici Implied Binomial Tree algorithm using a cross section of DAX option prices, the historical density is inferred by a combination of a non-parametric estimation from a historical time series of the DAX index and a forward Monte Carlo simulation.
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