The StressVaR: A New Risk Concept for Superior Fund Allocation
Cyril Coste, Raphael Douady, Ilija I. Zovko

TL;DR
The paper introduces StressVaR, a novel risk estimation method using nonlinear factor models, which improves hedge fund risk assessment and portfolio allocation performance compared to traditional VaR measures.
Contribution
It develops a new nonlinear factor-based risk measure, StressVaR, that better captures hidden risks and enhances fund allocation strategies.
Findings
StressVaR shows fewer VaR exceptions and smaller losses during exceptions.
Portfolios using StressVaR outperform market and other VaR-based portfolios.
StressVaR improves risk detection and fund allocation performance.
Abstract
In this paper we introduce a novel approach to risk estimation based on nonlinear factor models - the "StressVaR" (SVaR). Developed to evaluate the risk of hedge funds, the SVaR appears to be applicable to a wide range of investments. Its principle is to use the fairly short and sparse history of the hedge fund returns to identify relevant risk factors among a very broad set of possible risk sources. This risk profile is obtained by calibrating a collection of nonlinear single-factor models as opposed to a single multi-factor model. We then use the risk profile and the very long and rich history of the factors to asses the possible impact of known past crises on the funds, unveiling their hidden risks and so called "black swans". In backtests using data of 1060 hedge funds we demonstrate that the SVaR has better or comparable properties than several common VaR measures - shows less…
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Taxonomy
TopicsMonetary Policy and Economic Impact · Leadership, Behavior, and Decision-Making Studies · Market Dynamics and Volatility
