TY - JOUR
T1 - Financial prediction with constrained tail risk
AU - Trindade, A. Alexandre
AU - Uryasev, Stan
AU - Shapiro, Alexander
AU - Zrazhevsky, Grigory
PY - 2007/11
Y1 - 2007/11
N2 - A new class of asymmetric loss functions derived from the least absolute deviations or least squares loss with a constraint on the mean of one tail of the residual error distribution, is introduced for analyzing financial data. Motivated by risk management principles, the primary intent is to provide "cautious" forecasts under uncertainty. The net effect on fitted models is to shape the residuals so that on average only a prespecified proportion of predictions tend to fall above or below a desired threshold. The loss functions are reformulated as objective functions in the context of parameter estimation for linear regression models, and it is demonstrated how optimization can be implemented via linear programming. The method is a competitor of quantile regression, but is more flexible and broader in scope. An application is illustrated on prediction of NDX and SPX index returns data, while controlling the magnitude of a fraction of worst losses.
AB - A new class of asymmetric loss functions derived from the least absolute deviations or least squares loss with a constraint on the mean of one tail of the residual error distribution, is introduced for analyzing financial data. Motivated by risk management principles, the primary intent is to provide "cautious" forecasts under uncertainty. The net effect on fitted models is to shape the residuals so that on average only a prespecified proportion of predictions tend to fall above or below a desired threshold. The loss functions are reformulated as objective functions in the context of parameter estimation for linear regression models, and it is demonstrated how optimization can be implemented via linear programming. The method is a competitor of quantile regression, but is more flexible and broader in scope. An application is illustrated on prediction of NDX and SPX index returns data, while controlling the magnitude of a fraction of worst losses.
KW - Asymmetric loss
KW - Constrained regression
KW - Quantile regression
KW - Risk measure
KW - Value-at-risk
UR - http://www.scopus.com/inward/record.url?scp=35448973220&partnerID=8YFLogxK
U2 - 10.1016/j.jbankfin.2007.04.014
DO - 10.1016/j.jbankfin.2007.04.014
M3 - Article
AN - SCOPUS:35448973220
SN - 0378-4266
VL - 31
SP - 3524
EP - 3538
JO - Journal of Banking and Finance
JF - Journal of Banking and Finance
IS - 11
ER -