scholarly journals Monte Carlo Portfolio Optimization for General Investor Risk-Return Objectives and Arbitrary Return Distributions: A Solution for Long-Only Portfolios

2010 ◽  
Author(s):  
William Thornton Shaw
Keyword(s):  
Monte Carlo ◽  
Portfolio Optimization ◽  
Risk Return ◽  
Return Distributions

Portfolio Optimization using Rank Correlation

2014 ◽  
pp. 1866-1879
Author(s):  
Chanaka Edirisinghe ◽  
Wenjun Zhou
Keyword(s):  
Portfolio Optimization ◽  
Rank Correlation ◽  
Financial Assets ◽  
Risk Averse ◽  
Out Of Sample ◽  
Return Distributions ◽  
Sample Testing ◽  
Correlation Measures ◽  
Computational Properties ◽  
Critical Challenge

A critical challenge in managing quantitative funds is the computation of volatilities and correlations of the underlying financial assets. We present a study of Kendall's t coefficient, one of the best-known rank-based correlation measures, for computing the portfolio risk. Incorporating within risk-averse portfolio optimization, we show empirically that this correlation measure outperforms that of Pearson's in our out-of-sample testing with real-world financial data. This phenomenon is mainly due to the fat-tailed nature of stock return distributions. We also discuss computational properties of Kendall's t, and describe efficient procedures for incremental and one-time computation of Kendall's rank correlation.

scholarly journals Investing with Cryptocurrencies—a Liquidity Constrained Investment Approach*

2019 ◽  
Vol 18 (2) ◽  
pp. 280-306 ◽  
Author(s):  
Simon Trimborn ◽  
Mingyang Li ◽  
Wolfgang Karl Härdle
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Management ◽  
Liquidity Constraints ◽  
Dark Side ◽  
Weight Optimization ◽  
Trade Off ◽  
Risk Return ◽  
Out Of Sample ◽  
Bounded Risk ◽  
Object Of Study

Abstract Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have low liquidity compared to traditional assets, one needs to take into account liquidity issues when adding them to a portfolio. We propose a Liquidity Bounded Risk-return Optimization (LIBRO) approach, which is a combination of risk-return portfolio optimization under liquidity constraints. Cryptocurrencies are included in portfolios formed with stocks of the S&P 100, US Bonds, and commodities. We illustrate the importance of the liquidity constraints in an in-sample and out-of-sample study. LIBRO improves the weight optimization in the sense that it only adds cryptocurrencies in tradable amounts depending on the intended investment amount. The returns greatly increase compared to portfolios consisting only of traditional assets. We show that including cryptocurrencies in a portfolio can indeed improve its risk–return trade-off.

scholarly journals A guide to Monte Carlo simulation concepts for assessment of risk-return profiles for regulatory purposes

2020 ◽  
Vol 10 (2) ◽  
pp. 273-293 ◽  
Author(s):  
Stefan Graf ◽  
Ralf Korn
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Classification ◽  
Approximation Methods ◽  
Bootstrap Methods ◽  
Risk Return ◽  
Simulation Based ◽  
The Future ◽  
Forward Looking

Abstract Various regulatory initiatives (such as the pan-European PRIIP-regulation or the German chance-risk classification for state subsidized pension products) have been introduced that require product providers to assess and disclose the risk-return profile of their issued products by means of a key information document. We will in this context outline a concept for a (forward-looking) simulation-based approach and highlight its application and advantages. For reasons of comparison, we further illustrate the performance of approximation methods based on a projection of observed returns into the future such as the Cornish–Fisher expansion or bootstrap methods.

scholarly journals Financial Portfolio Optimization using Monte Carlo and Operation Research

2020 ◽  
Vol 175 (34) ◽  
pp. 43-46
Author(s):  
Noureen M. Noaman ◽  
Mohamed A. El-dosuky ◽  
Abdelrahman Karawia
Keyword(s):  
Monte Carlo ◽  
Portfolio Optimization ◽  
Operation Research ◽  
Financial Portfolio

scholarly journals Robust and Reliable Portfolio Optimization Formulation of a Chance Constrained Problem

2017 ◽  
Vol 42 (1) ◽  
pp. 83-117 ◽  
Author(s):  
Raghu Nandan Sengupta ◽  
Rakesh Kumar
Keyword(s):  
Portfolio Optimization ◽  
Convex Combination ◽  
Extensive Simulation ◽  
Second Order Cone ◽  
Risk Appetite ◽  
Optimization Formulation ◽  
Return Distributions ◽  
Portfolio Decision ◽  
Portfolio Optimization Problem ◽  
Made In

AbstractWe solve a linear chance constrained portfolio optimization problem using Robust Optimization (RO) method wherein financial script/asset loss return distributions are considered as extreme valued. The objective function is a convex combination of portfolio’s CVaR and expected value of loss return, subject to a set of randomly perturbed chance constraints with specified probability values. The robust deterministic counterpart of the model takes the form of Second Order Cone Programming (SOCP) problem. Results from extensive simulation runs show the efficacy of our proposed models, as it helps the investor to (i) utilize extensive simulation studies to draw insights into the effect of randomness in portfolio decision making process, (ii) incorporate different risk appetite scenarios to find the optimal solutions for the financial portfolio allocation problem and (iii) compare the risk and return profiles of the investments made in both deterministic as well as in uncertain and highly volatile financial markets.

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