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CV a R HEDGING USING QUANTIZATION‐BASED STOCHASTIC APPROXIMATION ALGORITHM
Author(s) -
Bardou O.,
Frikha N.,
Pagès G.
Publication year - 2016
Publication title -
mathematical finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.98
H-Index - 81
eISSN - 1467-9965
pISSN - 0960-1627
DOI - 10.1111/mafi.12049
Subject(s) - cvar , portfolio , portfolio optimization , mathematical optimization , conditional expectation , variance reduction , stochastic control , hedge , expected shortfall , econometrics , mathematics , computer science , variance (accounting) , economics , optimal control , finance , ecology , accounting , biology
In this paper, we investigate a method based on risk minimization to hedge observable but nontradable source of risk on financial or energy markets. The optimal portfolio strategy is obtained by minimizing dynamically the conditional value‐at‐risk (CVaR) using three main tools: a stochastic approximation algorithm, optimal quantization, and variance reduction techniques (importance sampling and linear control variable), as the quantities of interest are naturally related to rare events. As a first step, we investigate the problem of CVaR regression, which corresponds to a static portfolio strategy where the number of units of each tradable assets is fixed at time 0 and remains unchanged till maturity. We devise a stochastic approximation algorithm and study its a.s. convergence and weak convergence rate. Then, we extend our approach to the dynamic case under the assumption that the process modeling the nontradable source of risk and financial assets prices is Markovian. Finally, we illustrate our approach by considering several portfolios in connection with energy markets.