TY - JOUR
T1 - A stochastic programming model for dynamic portfolio management with financial derivatives
AU - Barro, Diana
AU - Consigli, Giorgio
AU - Varun, Vivek
N1 - Funding Information:
We dedicate this research project to the late, mostly esteemed Professor Giorgio Szëgo, whose outstanding scientific heritage we are sure will last for long. Giorgio Consigli acknowledges the support from Khalifa University of Science and Technology, Grant FSU-2022-010 award 000634-00001, project no. 8474000393. Diana Barro acknowledges support from the Venice Center for Risk Analytics (VERA) at Ca’ Foscari University of Venice.
Funding Information:
We dedicate this research project to the late, mostly esteemed Professor Giorgio Szëgo, whose outstanding scientific heritage we are sure will last for long. Giorgio Consigli acknowledges the support from Khalifa University of Science and Technology , Grant FSU-2022-010 award 000634-00001 , project no. 8474000393 . Diana Barro acknowledges support from the Venice Center for Risk Analytics (VERA) at Ca’ Foscari University of Venice.
Publisher Copyright:
© 2022
PY - 2022/7
Y1 - 2022/7
N2 - Stochastic optimization models have been extensively applied to financial portfolios and have proven their effectiveness in asset and asset-liability management. Occasionally, however, they have been applied to dynamic portfolio problems including not only assets traded in secondary markets but also derivative contracts such as options or futures with their dedicated payoff functions. Such extension allows the construction of asymmetric payoffs for hedging or speculative purposes but also leads to several mathematical issues. Derivatives-based nonlinear portfolios in a discrete multistage stochastic programming (MSP) framework can be potentially very beneficial to shape dynamically a portfolio return distribution and attain superior performance. In this article we present a portfolio model with equity options, which extends significantly previous efforts in this area, and analyse the potential of such extension from a modeling and methodological viewpoints. We consider an asset universe and model portfolio set-up including equity, bonds, money market, a volatility-based exchange-traded-fund (ETF) and over-the-counter (OTC) option contracts on the equity. Relying on this market structure we formulate and analyse, to the best of our knowledge, for the first time, a comprehensive set of optimal option strategies in a discrete framework, including canonical protective puts, covered calls and straddles, as well as more advanced combined strategies based on equity options and the volatility index. The problem formulation relies on a data-driven scenario generation method for asset returns and option prices consistent with arbitrage-free conditions and incomplete market assumptions. The joint inclusion of option contracts and the VIX as asset class in a dynamic portfolio problem extends previous efforts in the domain of volatility-driven optimal policies. By introducing an optimal trade-off problem based on expected wealth and Conditional Value-at-Risk (CVaR), we formulate the problem as a stochastic linear program and present an extended set of numerical results across different market phases, to discuss the interplay among asset classes and options, relevant to financial engineers and fund managers. We find that options’ portfolios and trading in options strengthen an effective tail risk control, and help shaping portfolios returns’ distributions, consistently with an investor's risk attitude. Furthermore the introduction of a volatility index in the asset universe, jointly with equity options, leads to superior risk-adjusted returns, both in- and out-of-sample, as shown in the final case-study.
AB - Stochastic optimization models have been extensively applied to financial portfolios and have proven their effectiveness in asset and asset-liability management. Occasionally, however, they have been applied to dynamic portfolio problems including not only assets traded in secondary markets but also derivative contracts such as options or futures with their dedicated payoff functions. Such extension allows the construction of asymmetric payoffs for hedging or speculative purposes but also leads to several mathematical issues. Derivatives-based nonlinear portfolios in a discrete multistage stochastic programming (MSP) framework can be potentially very beneficial to shape dynamically a portfolio return distribution and attain superior performance. In this article we present a portfolio model with equity options, which extends significantly previous efforts in this area, and analyse the potential of such extension from a modeling and methodological viewpoints. We consider an asset universe and model portfolio set-up including equity, bonds, money market, a volatility-based exchange-traded-fund (ETF) and over-the-counter (OTC) option contracts on the equity. Relying on this market structure we formulate and analyse, to the best of our knowledge, for the first time, a comprehensive set of optimal option strategies in a discrete framework, including canonical protective puts, covered calls and straddles, as well as more advanced combined strategies based on equity options and the volatility index. The problem formulation relies on a data-driven scenario generation method for asset returns and option prices consistent with arbitrage-free conditions and incomplete market assumptions. The joint inclusion of option contracts and the VIX as asset class in a dynamic portfolio problem extends previous efforts in the domain of volatility-driven optimal policies. By introducing an optimal trade-off problem based on expected wealth and Conditional Value-at-Risk (CVaR), we formulate the problem as a stochastic linear program and present an extended set of numerical results across different market phases, to discuss the interplay among asset classes and options, relevant to financial engineers and fund managers. We find that options’ portfolios and trading in options strengthen an effective tail risk control, and help shaping portfolios returns’ distributions, consistently with an investor's risk attitude. Furthermore the introduction of a volatility index in the asset universe, jointly with equity options, leads to superior risk-adjusted returns, both in- and out-of-sample, as shown in the final case-study.
KW - Derivatives pricing
KW - Equity and volatility risk
KW - Financial engineering
KW - Multistage stochastic programming
KW - Optimal risk control
KW - Option strategies
UR - http://www.scopus.com/inward/record.url?scp=85125673272&partnerID=8YFLogxK
U2 - 10.1016/j.jbankfin.2022.106445
DO - 10.1016/j.jbankfin.2022.106445
M3 - Article
AN - SCOPUS:85125673272
SN - 0378-4266
VL - 140
JO - Journal of Banking and Finance
JF - Journal of Banking and Finance
M1 - 106445
ER -