On Risk Prediction
This thesis comprises four papers concerning risk prediction.
Paper [I] suggests a nonlinear and multivariate time series model
framework that enables the study of simultaneity in returns and in
volatilities, as well as asymmetric effects arising from shocks. Using
daily data 2000-2006 for the Baltic state stock exchanges and that of
Moscow we find recursive structures with Riga directly depending in
returns on Tallinn and Vilnius, and Tallinn on Vilnius. For volatilities
both Riga and Vilnius depend on Tallinn. In addition, we find evidence
of asymmetric effects of shocks arising in Moscow and in the Baltic states
on both returns and volatilities.
Paper [II] argues that the estimation error in Value at Risk predictors
gives rise to underestimation of portfolio risk. A simple correction is
proposed and in an empirical illustration it is found to be economically
Paper [III] studies some approximation approaches to computing the
Value at Risk and the Expected Shortfall for multiple period asset re-
turns. Based on the result of a simulation experiment we conclude that
among the approaches studied the one based on assuming a skewed t dis-
tribution for the multiple period returns and that based on simulations
were the best. We also found that the uncertainty due to the estimation
error can be quite accurately estimated employing the delta method. In
an empirical illustration we computed five day Value at Risk's for the
S&P 500 index. The approaches performed about equally well.
Paper [IV] argues that the practise used in the valuation of the port-
folio is important for the calculation of the Value at Risk. In particular,
when liquidating a large portfolio the seller may not face horizontal de-
mandcurves. We propose a partially new approach for incorporating
this fact in the Value at Risk and in an empirical illustration we compare
it to a competing approach. We find substantial differences.
Source Type:Doctoral Dissertation
Keywords:SOCIAL SCIENCES; Business and economics; Economics; Econometrics; Finance; Time series; GARCH; Estimation error; Asymmetry; Supply and demand; Econometrics; ekonometri
Date of Publication:01/01/2009