Details

On Risk Prediction

by Lönnbark, Carl

Abstract (Summary)

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

relevant.

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.

Bibliographical Information:

Advisor:

School:Umeå universitet

School Location:Sweden

Source Type:Doctoral Dissertation

Keywords:SOCIAL SCIENCES; Business and economics; Economics; Econometrics; Finance; Time series; GARCH; Estimation error; Asymmetry; Supply and demand; Econometrics; ekonometri

ISBN:

Date of Publication:01/01/2009

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