TY - JOUR

T1 - Robust measurement of (heavy-tailed) risks

T2 - Theory and implementation

AU - Schneider, Judith C.

AU - Schweizer, Nikolaus

PY - 2015/12/1

Y1 - 2015/12/1

N2 - Every model presents an approximation of reality and thus modeling inevitably implies model risk. We quantify model risk in a non-parametric way, i.e., in terms of the divergence from a so-called nominal model. Worst-case risk is defined as the maximal risk among all models within a given divergence ball. We derive several new results on how different divergence measures affect the worst case. Moreover, we present a novel, empirical way built on model confidence sets (MCS) for choosing the radius of the divergence ball around the nominal model, i.e., for calibrating the amount of model risk. We demonstrate the implications of heavy-tailed risks for the choice of the divergence measure and the empirical divergence estimation. For heavy-tailed risks, the simulation of the worst-case distribution is numerically intricate. We present a Sequential Monte Carlo algorithm which is suitable for this task. An extended practical example, assessing the robustness of a hedging strategy, illustrates our approach.

AB - Every model presents an approximation of reality and thus modeling inevitably implies model risk. We quantify model risk in a non-parametric way, i.e., in terms of the divergence from a so-called nominal model. Worst-case risk is defined as the maximal risk among all models within a given divergence ball. We derive several new results on how different divergence measures affect the worst case. Moreover, we present a novel, empirical way built on model confidence sets (MCS) for choosing the radius of the divergence ball around the nominal model, i.e., for calibrating the amount of model risk. We demonstrate the implications of heavy-tailed risks for the choice of the divergence measure and the empirical divergence estimation. For heavy-tailed risks, the simulation of the worst-case distribution is numerically intricate. We present a Sequential Monte Carlo algorithm which is suitable for this task. An extended practical example, assessing the robustness of a hedging strategy, illustrates our approach.

KW - Divergence estimation

KW - Model risk

KW - Risk management

KW - Robustness

KW - Sequential Monte Carlo

KW - Management studies

UR - http://www.scopus.com/inward/record.url?scp=84947423461&partnerID=8YFLogxK

U2 - 10.1016/j.jedc.2015.09.010

DO - 10.1016/j.jedc.2015.09.010

M3 - Journal articles

AN - SCOPUS:84947423461

VL - 61

SP - 183

EP - 203

JO - Journal of Economic Dynamics and Control

JF - Journal of Economic Dynamics and Control

SN - 0165-1889

ER -