Portfolio Optimization Model with and without Options under Additional Constraints

Khodamoradi, Tahereh; Salahi, Maziar; Najafi, Ali Reza

doi:10.1155/2020/8862435

Cited by 3 publications

(1 citation statement)

References 58 publications

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“…In this section, we give important definitions and properties on stochastic processes, Euler theorem on the homogeneous functions. We refer the readers to [10]- [17] for stochastic processes applied to finance and to [18] for homogeneous functions. These results turn out to be necessary for our study.…”

Section: Methodsmentioning

confidence: 99%

Management of a Complex Portfolio of Assets with Stochastic Drifts and Volatilities

Yaméogo¹,

Ouédraogo²,

Barro³

2022

OJS

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In financial analysis risk quantification is essential for efficient portfolio management in a stochastic framework. In this paper we study the value at risk, the expected shortfall, marginal expected shortfall and value at risk, incremental value at risk and expected shortfall, the marginal and discrete marginal contributions of a portfolio. Each asset in the portfolio is characterized by a trend, a volatility and a price following a three-dimensional diffusion process. The interest rate of each asset evolves according to the Hull and White model. Furthermore, we propose the optimization of this portfolio according to the value at risk model.

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Section: Methodsmentioning

confidence: 99%

Management of a Complex Portfolio of Assets with Stochastic Drifts and Volatilities

Yaméogo¹,

Ouédraogo²,

Barro³

2022

OJS

View full text Add to dashboard Cite

show abstract

Optimization-based tail risk hedging of the S&P 500 index

Kwon

2023

The Engineering Economist

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Intelligent option portfolio model with perspective of shadow price and risk-free profit

2023

Financ Innov

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Since Markowitz proposed modern portfolio theory, portfolio optimization has been being a classic topic in financial engineering. Although it is generally accepted that options help to improve the market, there is still an improvement for the portrayal of their unique properties in portfolio problems. In this paper, an intelligent option portfolio model is developed that allows selling options contracts to earn option fees and considers the high leverage of options in the market. Deep learning methods are used to predict the forward price of the underlying asset, making the model smarter. It can find an optimal option portfolio that maximizes the final wealth among the call and put options with multiple strike prices. We use the duality theory to analyze the marginal contribution of initial assets, risk tolerance limit, and portfolio leverage limit for the final wealth. The leverage limit of the option portfolio has a significant impact on the return. To satisfy the investors with different risk preferences, we also give the conditions for the option portfolio to gain a risk-free return and replace the Conditional Value-at-Risk. Numerical experiments demonstrate that the intelligent option portfolio model obtains a satisfactory out-of-sample return, which is significantly positively correlated with the volatility of the underlying asset and negatively correlated with the forecast error of the forward price. The risk- free option model is effective in achieving the goal of no drawdown and gaining satisfactory returns. Investors can adjust the balance point between returns and risks according to their risk preference.

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Portfolio Optimization Model with and without Options under Additional Constraints

Cited by 3 publications

References 58 publications

Management of a Complex Portfolio of Assets with Stochastic Drifts and Volatilities

Management of a Complex Portfolio of Assets with Stochastic Drifts and Volatilities

Optimization-based tail risk hedging of the S&P 500 index

Intelligent option portfolio model with perspective of shadow price and risk-free profit

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