Robust and Reliable Portfolio Optimization Formulation of a Chance Constrained Problem

Sengupta, Raghu Nandan; Kumar, Rakesh

doi:10.1515/fcds-2017-0004

Cited by 6 publications

(6 citation statements)

References 67 publications

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“…• The non-robust models are solved using T = 39 and q t = 1 39 . Again, to maintain the consistency throughout the paper, the robust models are solved by dividing each in-sample period into 3 equal parts and setting q l t = 1 13 , t = 1, .…”

Section: Window Analysismentioning

confidence: 99%

“…The examination can be reached out to incorporate different sorts of uncertainty sets as well as by considering uncertainty in returns of assets similar to the one proposed by Bertsimas and Sim [5]. Besides, we can likewise investigate the viability of GMD based measures on linear chance-constrained optimization problem using RO in portfolio selection on the lines of research by Sengupta and Kumar [39].…”

Section: 2mentioning

confidence: 99%

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Worst-case analysis of Gini mean difference safety measure

Sehgal¹,

Mehra²

2021

JIMO

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The paper introduces the worst-case portfolio optimization models within the robust optimization framework for maximizing return through either the mean or median metrics. The risk in the portfolio is quantified by Gini mean difference. We put forward the worst-case models under the mixed and interval+polyhedral uncertainty sets. The proposed models turn out to be linear and mixed integer linear programs under the mixed uncertainty set, and semidefinite program under interval+polyhedral uncertainty set. The performance comparison of the proposed models on the listed stocks of Euro Stoxx 50, Dow Jones Global Titans 50, S&P Asia 50, consistently exhibit advantage over their conventional non-robust counterpart models on various risk parameters including the standard deviation, worst return, value at risk, conditional value at risk and maximum drawdown of the portfolio.2010 Mathematics Subject Classification. Primary: 91G10, 91B30, 62G35; Secondary: 90C90, 90C05, 90C11.

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Section: Window Analysismentioning

confidence: 99%

Section: 2mentioning

confidence: 99%

Worst-case analysis of Gini mean difference safety measure

Sehgal¹,

Mehra²

2021

JIMO

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“…For very recent work on chance constraints with applications to portfolio optimization, see also the works of Sun et al and Sengupta and Kumar …”

Section: Transaction Costsmentioning

confidence: 99%

Dynamic option hedging with transaction costs: A stochastic model predictive control approach

Plessen

Puglia

Gabbriellini³

et al. 2017

Intl J Robust & Nonlinear

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This paper proposes stochastic model predictive control as a tool for hedging derivative contracts (such as plain vanilla and exotic options) in the presence of transaction costs. The methodology combines stochastic scenario generation for the prediction of asset prices at the next rebalancing interval with the minimization of a stochastic measure of the predicted hedging error. We consider 3 different measures to minimize in order to optimally rebalance the replicating portfolio: a trade-off between variance and expected value of hedging error, conditional value at risk, and the largest predicted hedging error. The resulting optimization problems require solving at each trading instant a quadratic program, a linear program, and a (smaller-scale) linear program, respectively. These can be combined with 3 different scenario generation schemes: the lognormal stock model with parameters recursively identified from data, an identification method based on support vector regression, and a simpler scheme based on perturbation noise. The hedging performance obtained by the proposed stochastic model predictive control strategies is illustrated on real-world data drawn from the NASDAQ-100 composite, evaluated for a European call and a barrier option, and compared with delta hedging.

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“…Sengupta uses a robust optimization method to solve the linear opportunity limited portfolio optimization problem, in which the financial script and asset loss return distribution is regarded as an extreme value. Compare the risk and return of investments made in deterministic and uncertain and highly volatile financial markets [5]. Kshatriya investigated the process of international portfolio diversification based on a sample of 33 globally traded stock market indices from 2000 to 2012.…”

Section: Introductionmentioning

confidence: 99%

Application and Comparative Study of Optimization Algorithms in Financial Investment Portfolio Problems

Liu

Xiao

2021

Mobile Information Systems

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Portfolio theory mainly studies how to optimize the allocation of assets under the premise of maximizing expected returns and minimizing investment risks. In view of the instability of the financial market, a diversified investment portfolio can help control the loss of the investment portfolio. In addition to paying attention to the safety and return of asset allocation, we cannot ignore the liquidity of assets, that is, their liquidity. Adding high-liquidity products to asset allocation, such as equity investment, can better control the financial cash flow in response to emergencies. One of the ways to make assets flow is to securitize assets and sell them to the market. In order to revitalize the stock assets, good investment efficiency is a necessary choice for financial investment. Various financial products and their derivatives continue to enter people’s vision. There are many financial products in reality, and optimizing the investment portfolio can bring high economic benefits. The purpose of this paper is to study the application of optimization algorithms in financial portfolio problems. (1) Monetary policy remains prudent and neutral. It is not easy to expect flooding, but flexibility is required in complex situations. (2) Financial resources are tilted towards innovation and transformation and capital markets, which is beneficial to the development of capital markets in the medium and long term. (3) Unblocking the transmission mechanism is conducive to lenient credit and tapping the wrong killing opportunities in private enterprise debt. (4) Banks and other financial institutions have moderate pressure to give benefits to entities, but in the long run, the interests of the two are consistent. (5) Finance risk prevention will continue, orderly breaking the rigid exchange and reshaping the financial structure and ecology. (6) The pace of opening up of the financial industry has accelerated, and the bond market investor structure has improved. In this paper, we establish different optimization schemes to compare and study the portfolio problem and then use MATLAB to solve the modeling and programming problem, calculate the highest return rate and the lowest risk value before and after optimization, and then make a comparative analysis to get a better optimization scheme. The results show that the genetic algorithm model is superior to the quadratic programming method in terms of risk control. The minimum risk of portfolio optimization through genetic algorithm has been reduced by about 40%, and the maximum return has increased by about 25%. The comprehensive optimization effect is better than the quadratic planning method and ultimately can obtain higher economic benefits. It can be seen that the optimization algorithm is of great significance for the comparative study of financial portfolio problems.

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Robust and Reliable Portfolio Optimization Formulation of a Chance Constrained Problem

Cited by 6 publications

References 67 publications

Worst-case analysis of Gini mean difference safety measure

Worst-case analysis of Gini mean difference safety measure

Dynamic option hedging with transaction costs: A stochastic model predictive control approach

Application and Comparative Study of Optimization Algorithms in Financial Investment Portfolio Problems

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