scholarly journals Application of semi-deviation as a proxy for the expected return estimation in the Croatian equity market

2019 ◽  
Vol 5 (1) ◽  
pp. 9-20
Author(s):  
Denis Dolinar ◽  
Davor Zoričić ◽  
Zrinka Lovretin Golubić
Keyword(s):  
Stock Market ◽  
Portfolio Management ◽  
Risk Measures ◽  
Equity Market ◽  
Expected Return ◽  
Deviation Measure ◽  
Out Of Sample ◽  
The Mean ◽  
Risk Parameters ◽  
Mean Variance

AbstractIn the field of portfolio management the focus has been on the out-of-sample estimation of the covariance matrix mainly because the estimation of expected return is much more challenging. However, recent research efforts have not only tried to improve the estimation of risk parameters by expanding the analysis beyond the mean-variance setting but also by testing whether risk measures can be used as proxies for the expected return in the stock market. In this research, we test the standard deviation (measure of total volatility) and the semi-deviation (measure of downside risk) as proxies for the expected market return in the illiquid and undeveloped Croatian stock market in the period from January 2005 until November 2017. In such an environment, the application of the proposed methodology yielded poor results, which helps explain the failure of the out-of-sample estimation of the maximum Sharpe ratio portfolio in earlier research in the Croatian equity market.

THE COMPATIBLE BOND-STOCK MARKET WITH JUMPS

2011 ◽  
Vol 14 (05) ◽  
pp. 723-755 ◽  
Author(s):  
DEWEN XIONG ◽  
MICHAEL KOHLMANN
Keyword(s):  
Stock Market ◽  
Marked Point ◽  
Quadratic Functions ◽  
Marked Point Process ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
No Arbitrage ◽  
The Mean ◽  
Necessary And Sufficient ◽  
Mean Variance

We construct a bond-stock market composed of d stocks and many bonds with jumps driven by general marked point process as well as by an ℝn-valued Wiener process. By composing these tools we introduce the concept of a compatible bond-stock market and give a necessary and sufficient condition for this property. We study no-arbitrage properties of the composed market where a compatible bond-stock market is arbitrage-free both for the bonds market and for the stocks market. We then turn to an incomplete compatible bond-stock market and give a necessary and sufficient condition for a compatible bond-stock market to be incomplete. In this market we consider the mean-variance hedging in the special situation where both B(u, T) and eG(u, y, T)-1 are quadratic functions of T - u. So, we need to extend the notion of a variance-optimal martingale (VOM) as in Xiong and Kohlmann (2009) to the more general market. By introducing two virtual stocks [Formula: see text], we prove that the VOM for the bond-stock market is the same as the VOM for the new stock market [Formula: see text]. The mean-variance hedging problem in this incomplete bond-stock market for a contingent claim [Formula: see text] is solved by deriving an explicit solution of the optimal measure-valued strategy and the optimal cost induced by the optimal strategy of MHV for the stocks [Formula: see text] is computed.

scholarly journals SCALED AND STABLE MEAN-VARIANCE-EVAR PORTFOLIO SELECTION STRATEGY WITH PROPORTIONAL TRANSACTION COSTS

2017 ◽  
Vol 18 (4) ◽  
pp. 561-584 ◽  
Author(s):  
Ebenezer Fiifi Emire ATTA MILLS ◽  
Bo YU ◽  
Jie YU
Keyword(s):  
Transaction Costs ◽  
Portfolio Selection ◽  
Downside Risk ◽  
Estimation Risk ◽  
Expected Return ◽  
Proportional Transaction Costs ◽  
Portfolio Returns ◽  
L2 Norm ◽  
The Mean ◽  
Mean Variance

This paper studies a portfolio optimization problem with variance and Entropic Value-at-Risk (evar) as risk measures. As the variance measures the deviation around the expected return, the introduction of evar in the mean-variance framework helps to control the downside risk of portfolio returns. This study utilized the squared l2-norm to alleviate estimation risk problems arising from the mean estimate of random returns. To adequately represent the variance-evar risk measure of the resulting portfolio, this study pursues rescaling by the capital accessible after payment of transaction costs. The results of this paper extend the classical Markowitz model to the case of proportional transaction costs and enhance the efficiency of portfolio selection by alleviating estimation risk and controlling the downside risk of portfolio returns. The model seeks to meet the requirements of regulators and fund managers as it represents a balance between short tails and variance. The practical implications of the findings of this study are that the model when applied, will increase the amount of capital for investment, lower transaction cost and minimize risk associated with the deviation around the expected return at the expense of a small additional risk in short tails.

scholarly journals Portfolio Selection Models

2017 ◽  
pp. 43-82
Keyword(s):  
Decision Theory ◽  
Portfolio Selection ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Computer Programs ◽  
Var Analysis ◽  
Widespread Acceptance ◽  
Portfolio Manager ◽  
The Mean ◽  
Mean Variance

The main goal behind the concept of portfolio management is to combine various assets into portfolios and then to manage those portfolios so as to achieve the desired investment objectives. To be more specific, the investors' needs are mostly defined in terms of profit and risk, and the portfolio manager makes a sound decision aimed ether to maximize the return or minimize the risk. The Mean-Variance and Mean-VaR analysis has gained widespread acceptance among practitioners of asset allocation. Although they are the simplest models of investment, sometimes they are sufficiently rich to be directly useful in applied problems and decision theory. Here you will learn how to apply these analyses in practice using computer programs and spreadsheets.

scholarly journals A remark on some extensions of the mean-variance portfolio selection models

2021 ◽  
Vol 16 (3) ◽  
pp. 26-34
Author(s):  
Enrico Moretto ◽  
Keyword(s):  
Portfolio Management ◽  
Proper Time ◽  
Research Question ◽  
The Third ◽  
Management Techniques ◽  
The Common ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
The Right ◽  
Mean Variance

Quantitative risk management techniques should prove their efficacy when financially turbulent periods are about to occur. Along the common saying “who needs an umbrella on a sunny day?”, a theoretical model is really helpful when it carries the right suggestion at the proper time, that is when markets start behaving hecticly. The beginning of the third decade of the 21st century carried along a turmoil that severely affected worldwide economy and changed it, probably for good. A consequent and plausible research question could be this: which financial quantitative approaches can still be considered reliable? This article tries to partially answer this question by testing if the mean-variance selection model (Markowitz [16], [17]) and some of his refinements can provide some useful hints in terms of portfolio management.

scholarly journals Mean-Variance Portfolio Optimization on Islamic Stocks by Using Non Constant Mean and Volatility Models and Genetic Algorithm

2018 ◽  
Vol 7 (3.20) ◽  
pp. 366
Author(s):  
Endang Soeryana Hasbullah ◽  
Nurfadhlina Bt Abdul Halim ◽  
Sukono . ◽  
Adam Sukma Putra ◽  
Abdul Talib Bon
Keyword(s):  
Genetic Algorithm ◽  
Stock Market ◽  
Moving Average ◽  
Lagrangian Multiplier ◽  
Potential Candidate ◽  
Investment Portfolio ◽  
Expected Return ◽  
Volatility Models ◽  
Mean Variance Portfolio ◽  
Mean Variance

The risk in stock market has taken an sinificant issue in investment of stock market, including Investment in some Islamic stocks. In order to minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several Islamic stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to Islamic stocks by using mean and volatility is not constant approaches. Non constant mean analyzed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analyzed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique followed by the Genetic Algorithm (GA). The expected result is to get the proportion of investment in each Islamic stock analyzed. Based on the result, we got that GA give a proportion of portfolio optimum selection with the best expected return. However, The GA has more potential candidate of solution that give the investor an alternative of their optimum portfolio selection. in this paper, we only present the best solution which has the highest fitness to the model. 

scholarly journals Strategic Asset Allocation Of Credit Guarantors

2015 ◽  
Vol 31 (5) ◽  
pp. 1823
Author(s):  
Dong-Woo Rhee ◽  
Hyoung-Goo Kang ◽  
Soo-Hyun Kim
Keyword(s):  
Public Policy ◽  
Asset Allocation ◽  
Minimum Variance ◽  
Risk Return ◽  
Out Of Sample ◽  
Sample Test ◽  
Prior Literature ◽  
The Mean ◽  
Risk Contribution ◽  
Mean Variance

<p>How to manage the portfolio of credit guarantors is important in practice and public policy, but has not been investigated well in the prior literature. We empirically compare four different approaches in managing credit guarantor portfolios. The four approaches are equal weighted, minimum variance, mean variance optimization and equal risk contribution methods. In terms of risk return ratio, the mean variance optimization model performs best in out-of-sample test. This result contrasts with previous findings against mean variance optimization. Our results are robust. The results do not change as the characteristics of guarantee portfolio vary.</p>

Markowitz Theory in Portfolio Optimization for Heavy Tailed Assets

2019 ◽  
Vol 1 (3) ◽  
pp. 1-14
Author(s):  
Wong Ghee Ching ◽  
Che Mohd Imran Che Taib
Keyword(s):  
Portfolio Optimization ◽  
Composite Index ◽  
Minimum Variance ◽  
Optimization Techniques ◽  
Expected Return ◽  
Markowitz Model ◽  
The Mean ◽  
Heavy Tailed ◽  
Mean Variance Portfolio ◽  
Mean Variance

This paper aims at solving an optimization problem in the presence of heavy tail behavior of financial assets. The question of minimizing risk subjected to a certain expected return or maximizing return for a given expected risk are two objective functions to be solved using Markowitz model. The Markowitz based strategies namely the mean variance portfolio, minimum variance portfolio and equally weighted portfolio are proposed in conjunction with mean and variance analysis of the portfolio. The historical prices of stocks traded at Bursa Malaysia are used for empirical analysis. We employed CAPM in order to investigate the performance of the Markowitz model which was benchmarked with risk adjusted KLSE Composite Index. We performed a backtesting study of portfolio optimization techniques defined under modern portfolio theory in order to find the optimal portfolio. Our findings showed that the mean variance portfolio outperformed the other two strategies in terms of performance of investment for heavy tailed assets.

Heuristic selection of portfolio based on coefficient of optimism

GIS Business ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
Dilip Roy ◽  
Soma Panja Chowdhury
Keyword(s):  
Risk Taking ◽  
Heuristic Method ◽  
Value Systems ◽  
Decision Making Process ◽  
Expected Return ◽  
Human Value ◽  
Optimal Criterion ◽  
The Mean ◽  
Mean Variance ◽  
Selection Of

The mean-variance method developed by Markowitz (1959) was aimed at obtaining optimizing portfolios. But selection of portfolio in the real world mostly deviates from this optimal criterion. In this paper we have considered this issue from an altogether different aspect and developed means for aiming at nearly optimum portfolio. We considered the risk taking propensity as the main driving force and presented a heuristic method to reach the near to the optimal state. For doing so, we have introduced the coefficient of optimism in the decision making process and simultaneously considered conditional optimum portfolio and corresponding heuristic portfolio. In the extreme situations three different human value systems can be considered as optimistic, pessimistic and risk planner. To examine the closeness between the heuristic and optimum portfolios we have carried out empirical analysis covering ten years data of fifteen companies from Nifty (2000-09). Regarding the choice of companies we have adopted random selection technique. From empirical study we have found that for moderate values of the coefficient of optimism a heuristic investor’s decision nearly coincides with the corresponding optimum portfolio. However, for extreme situations i.e. optimistic and pessimistic situations heuristic portfolio differs from optimum portfolio. Keywords: Expected return, risk, optimum portfolio, heuristic portfolio, coefficient of optimism.

CONIC PORTFOLIO THEORY

2016 ◽  
Vol 19 (03) ◽  
pp. 1650019 ◽  
Author(s):  
DILIP B. MADAN
Keyword(s):  
Optimization Problems ◽  
Risk Measures ◽  
Market Value ◽  
Acceptable Risks ◽  
Out Of Sample ◽  
Bid Price ◽  
Distorted Expectation ◽  
Risk Acceptability ◽  
Two Price Economy ◽  
Mean Variance

Portfolios are designed to maximize a conservative market value or bid price for the portfolio. Theoretically this bid price is modeled as reflecting a convex cone of acceptable risks supporting an arbitrage free equilibrium of a two price economy. When risk acceptability is completely defined by the risk distribution function and bid prices are additive for comonotone risks, then these prices may be evaluated by a distorted expectation. The concavity of the distortion calibrates market risk attitudes. Procedures are outlined for observing the economic magnitudes for diversification benefits reflected in conservative valuation schemes. Optimal portfolios are formed for long only, long short and volatility constrained portfolios. Comparison with mean variance portfolios reflects lower concentration in conic portfolios that have comparable out of sample upside performance coupled with higher downside outcomes. Additionally the optimization problems are robust, employing directionally sensitive risk measures that are in the same units as the rewards. A further contribution is the ability to construct volatility constrained portfolios that attractively combine other dimensions of risk with rewards.

scholarly journals Analisis Portofolio Investasi dengan Metode Multi Objektif

Jurnal Varian ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 6-12
Author(s):  
Gilang Primajati ◽  
Ahmad Zuli Amrullah ◽  
Ahmad Ahmad
Keyword(s):  
Minimum Variance ◽  
Optimal Portfolio ◽  
Risk Level ◽  
Risk Averse ◽  
Expected Return ◽  
Risky Assets ◽  
Variant Method ◽  
The Mean ◽  
Mean Variance ◽  
Constraint Method

In the formation of an efficient portfolio, many methods can be used. Of course with its own assumptions and advantages. In the process, reasonable investor assumptions tend to be risk averse. Investors who are risk averse are investors who, when faced with two investments with the same expected return, will choose an investment with a lower risk level. If an investor has several efficient portfolio choices, then the most optimal portfolio will be chosen. Optimal portfolio with mean-variance efficient portfolio criteria, investors only invest in risky assets. Investors do not include risk free assets in their portfolios. Mean-variance efficient portfolio is defined as a portfolio that has a minimum variance among all possible portfolio that can be formed, at the mean level of the same expected return. The mean variant method of the two constraints can be used as a basis in determining the optimal portfolio weight by minimizing the risk of portfolio return with two constraints. In this article the problem referred to is symbolized by lamda and beta. With this two-constraint method, the results obtained are more detailed so that they can describe the results of a sharper analysis for an investor.

Sign in / Sign up

Export Citation Format

Share Document