Fund of Hedge Funds Portfolio Selection: A Multiple-Objective Approach

Mirza

Economic Research-Ekonomska Istraživanja

et al. 2017

Stock markets play an important role in spurring economic growth and development through diversification opportunities. However, diversification cannot be truly achieved if we continue to ignore additional dimensions of risk, namely skewness and kurtosis. This study incorporates higher moments of risk to form a mean-varianceskewness-kurtosis based framework for portfolio optimisation. Inclusion of higher moments in optimisation framework acknowledges the risk of asymmetric returns and fat-tail risk and can help investors in formulating optimal portfolios of stocks which can be significantly divergent from the ones they obtain through the Markowitz meanvariance optimisation. Our results confirm the presence of tradeoff between returns and additional dimensions of risk in Pakistan Stock Exchange (PSX) and strongly suggest including them in the optimisation framework to avoid sub-optimal decisions and to curtail exposure towards higher moments of risks.

Section: Literature Reviewmentioning

confidence: 99%

Portfolio optimisation with higher moments of risk at the Pakistan Stock Exchange

Mirza

Economic Research-Ekonomska Istraživanja

et al. 2017

“…Moreover the presence of kurtosis indicates the higher probability of extreme events. This portfolio selection problem was extended by (Davies et al, 2009) to hedge fund indexes. Jondeau and Rockinger (2006) study confirmed that in case of non-normality the first and second order moment model is ineffective and third moment or four moment can served as good approximation.…”

Section: Literature Reviewmentioning

confidence: 99%

Asset Pricing With Higher Co-Moments and Cvar: Evidence From Pakistan Stock Exchange

Arif

Sohail²

2020

IJEFI

The development of asset pricing model is attributed to Markowitz (1952) which initiated towards Modern Portfolio Theory (MPT). The whole concept of MPT based on normality of returns assumption but in emerging economies volatility of returns is an important issue and sometimes markets only behave in either bullish or bearish patterns. Moreover, the volatility cannot be attributed and explained by variance rather it can be a result of extreme events (profits / losses) referred to as none elliptical distributions of returns. The objective of this study is to incorporate additional dimensions of risk in Markowitz Mean-Variance framework through inclusions of skewness kurtosis and coherent risk measure CVaR to obtain optimal portfolio with PGP approach. The study analyzes the portfolio returns of Mean-Variance (MV), Mean Variance Skewness (MVS), Mean Variance Skewness Kurtosis (MVSK) and Mean CVaR Skewness Kurtosis (MCVaRSK) models by using selected stocks of KSE-100 index over the time period of 2009-2018. The empirical findings suggest that portfolio returns impacted through inclusion of higher moments and CVaR and generated higher returns over the benchmark portfolio. The results of study are immensely useful for the fund managers and investors for stocks selection and construction of alternative portfolios.

International Series in Operations Research &Amp; Management Science

“…Among them, the most widely used is Goal Programming (Abdelaziz et al 2007;Ballestero et al 2012;Bilbao-Terol et al 2013;Davies et al 2009;Inuiguchi and Ramık 2000;Pendaraki et al 2004;Prakash et al 2003;Sharma et al 2006;Tamiz et al 2013) are some recent examples. From year 2000 a wide range of literature on MCDM approaches to portfolio choice and related issues is available.…”

Section: Mcdm and Portfolio Selectionmentioning

confidence: 99%

“…Therefore, a large number of works can be found combining the meanvariance framework with other measures characterizing the returns distribution. Briec and Kerstens (2010), Davies et al (2009), Jondeau and Rockinger (2006), Kerstens et al (2011), andYu andLee (2011) include among the considered criteria skewness and kurtosis. Other authors incorporate to the portfolio selection model risk measures as value at risk and conditional value at risk (Krink and Paterlini 2011;Mansini et al 2007;Roman et al 2007), the mean absolute deviation (Ogryczak 2000), and systematic risk (Rodríguez et al 2011).…”

Section: Mcdm Applications To Srimentioning

confidence: 99%

The Ethical Financial Question and the MCDM Framework

Ballestero

Pérez-Gladish

Garcı́a-Bernabeu

2014

This chapter explains the financial meaning and importance of Socially Responsible Investment (SRI), also called ethical investment. Currently, SRI is a private initiative to invest increasing flows of financial resources in environmentally and socially sustainable activities and to invest nothing in anti-ethical projects. Main SRI agents are banks and institutional investors who are engaged in policies such as sustainable consumption of energy and natural resources, ecosystem protection, advanced medical projects, technological research, education of young entrepreneurs, anti-tobacco campaigns, safety and healthcare in the workplace, and others. These agents think that traditional financial criteria such as profitability and risk should be combined with SRI criteria to select stock portfolios. In SRI, Multiple Criteria Decision Making (MCDM) approaches seem to be helpful as several criteria, not only financial but also environmental, social or governance concerns are taken into account. Regarding MCDM, a brief overview is included in this chapter to introduce this methodology into SRI decision problems.