scholarly journals Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange

2021 ◽  
Vol 18 (2) ◽  
pp. 273-286
Author(s):  
Le Tuan Anh ◽  
Dao Thi Thanh Binh
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Optimal Portfolio ◽  
Gaussian Copula ◽  
Optimal Portfolios ◽  
Empirical Results ◽  
Portfolio Strategy ◽  
T Copula ◽  
Mean Variance

This paper studies how to construct and compare various optimal portfolio frameworks for investors in the context of the Vietnamese stock market. The aim of the study is to help investors to find solutions for constructing an optimal portfolio strategy using modern investment frameworks in the Vietnamese stock market. The study contains a census of the top 43 companies listed on the Ho Chi Minh stock exchange (HOSE) over the ten-year period from July 2010 to January 2021. Optimal portfolios are constructed using Mean-Variance Framework, Mean-CVaR Framework under different copula simulations. Two-thirds of the data from 26/03/2014 to 27/1/2021 consists of the data of Vietnamese stocks during the COVID-19 recession, which caused depression globally; however, the results obtained during this period still provide a consistent outcome with the results for other periods. Furthermore, by randomly attempting different stocks in the research sample, the results also perform the same outcome as previous analyses. At about the same CvaR level of about 2.1%, for example, the Gaussian copula portfolio has daily Mean Return of 0.121%, the t copula portfolio has 0.12% Mean Return, while Mean-CvaR with the Raw Return portfolio has a lower Return at 0.103%, and the last portfolio of Mean-Variance with Raw Return has 0.102% Mean Return. Empirical results for all 10 portfolio levels showed that CVaR copula simulations significantly outperform the historical Mean-CVaR framework and Mean-Variance framework in the context of the Vietnamese stock exchange.

Portfolio optimization using the generalized reduced gradient nonlinear algorithm

2016 ◽  
Vol 9 (4) ◽  
pp. 570-582 ◽  
Author(s):  
Dima Waleed Hanna Alrabadi
Keyword(s):  
Portfolio Optimization ◽  
Stock Exchange ◽  
Arab World ◽  
Content Type ◽  
Optimization Framework ◽  
Monthly Return ◽  
Reduced Gradient ◽  
The Mean ◽  
Generalized Reduced Gradient ◽  
Mean Variance

Purpose This study aims to utilize the mean–variance optimization framework of Markowitz (1952) and the generalized reduced gradient (GRG) nonlinear algorithm to find the optimal portfolio that maximizes return while keeping risk at minimum. Design/methodology/approach This study applies the portfolio optimization concept of Markowitz (1952) and the GRG nonlinear algorithm to a portfolio consisting of the 30 leading stocks from the three different sectors in Amman Stock Exchange over the period from 2009 to 2013. Findings The selected portfolios achieve a monthly return of 5 per cent whilst keeping risk at minimum. However, if the short-selling constraint is relaxed, the monthly return will be 9 per cent. Moreover, the GRG nonlinear algorithm enables to construct a portfolio with a Sharpe ratio of 7.4. Practical implications The results of this study are vital to both academics and practitioners, specifically the Arab and Jordanian investors. Originality/value To the best of the author’s knowledge, this is the first study in Jordan and in the Arab world that constructs optimum portfolios based on the mean–variance optimization framework of Markowitz (1952) and the GRG nonlinear algorithm.

The Black-Litterman Model for Portfolio Optimization on Vietnam Stock Market

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 99-111
Author(s):  
Bao Quoc Ta ◽  
Thao Vuong
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Market Equilibrium ◽  
Management Model ◽  
Optimal Portfolios ◽  
Allocation Model ◽  
Allocation Process ◽  
Allocation Decisions

The Black-Litterman asset allocation model is an extended portfolio management model to construct optimal portfolios by combining the market equilibrium with investor views into asset allocation decisions. In this paper we apply Black-Litterman model for portfolio optimization on Vietnames stock market. We chose ARIMA methodology utilized in financial econonometrics to predict the views of investor which are used as inputs of the Black-Litterman asset allocation process to find optimal portfolio and weights.

scholarly journals South–South Cooperation in South and East Asia: An Event Study of the China–Pakistan Economic Corridor

2019 ◽  
Vol 21 (1) ◽  
pp. 54-67
Author(s):  
Wing Him Yeung ◽  
Yilisha Pang ◽  
Asad Aman
Keyword(s):  
Stock Market ◽  
East Asia ◽  
Event Study ◽  
Stock Exchange ◽  
Global South ◽  
Short Term ◽  
Empirical Results ◽  
Study Methodology ◽  
The Impact ◽  
Event Study Methodology

South–South cooperation has been on the rise in recent years. One of the latest examples is the China–Pakistan Economic Corridor (CPEC) proposed by the Chinese and Pakistani governments in 2013. Using event study methodology, this article examines the impact of events and announcements associated with CPEC on the Pakistan Stock Exchange in Pakistan and the Shanghai Stock Exchange in China. The first key finding of this article is that the initial announcement associated with CPEC had stronger and positive short-term impact on the Pakistan Stock Exchange in comparison with the impact of subsequent CPEC events on the stock market. The second key finding is that the short-term impact of the CPEC initial announcement was stronger on the Pakistan Stock Exchange than on the Shanghai Stock Exchange, possibly due to the substantial difference in the size of the two economies. The empirical results of this article have important implications for investors, corporations and regulators to the Global South.

scholarly journals Continuous-Time Mean-Variance Portfolio Selection under the CEV Process

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Hui-qiang Ma
Keyword(s):  
Portfolio Selection ◽  
Continuous Time ◽  
Stock Price ◽  
Efficient Frontier ◽  
Optimal Portfolio ◽  
Portfolio Strategy ◽  
Cev Process ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance

We consider a continuous-time mean-variance portfolio selection model when stock price follows the constant elasticity of variance (CEV) process. The aim of this paper is to derive an optimal portfolio strategy and the efficient frontier. The mean-variance portfolio selection problem is formulated as a linearly constrained convex program problem. By employing the Lagrange multiplier method and stochastic optimal control theory, we obtain the optimal portfolio strategy and mean-variance efficient frontier analytically. The results show that the mean-variance efficient frontier is still a parabola in the mean-variance plane, and the optimal strategies depend not only on the total wealth but also on the stock price. Moreover, some numerical examples are given to analyze the sensitivity of the efficient frontier with respect to the elasticity parameter and to illustrate the results presented in this paper. The numerical results show that the price of risk decreases as the elasticity coefficient increases.

scholarly journals A Comparison Of Mean-Variance And Mean-Semivariance Optimisation On The JSE

2014 ◽  
Vol 30 (6) ◽  
pp. 1587 ◽  
Author(s):  
Jiten Vasant ◽  
Laurent Irgolic ◽  
Ryan Kruger ◽  
Kanshukan Rajaratnam
Keyword(s):  
Financial Crisis ◽  
Optimal Portfolio ◽  
Fixed Income ◽  
Optimal Portfolios ◽  
Crisis Period ◽  
Recent Financial Crisis ◽  
Mean Variance

<p>This study investigates the effectiveness of semivariance versus mean-variance optimisation on a risk-adjusted basis on the JSE. We compare semivariance and mean-variance optimisation prior to, during and after the recent financial crisis period. Additionally, we investigate the inclusion of a fixed-income asset in the optimal portfolio. The results suggest that semivariance optimisation on the JSE in a pure equity case produces lower absolute returns, yet superior risk-adjusted returns. Further investigation suggests that semivariance metrics are effective within a certain range of portfolio sizes and diminishes in benefit once portfolios become larger. A fixed income asset scenario tested under the hypothesis of semivariance optimisation favoured greater bond weightings in optimal portfolios.<em> </em><strong></strong></p>

scholarly journals Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

2019 ◽  
Vol 8 (3) ◽  
pp. 7818-7822
Keyword(s):  
Portfolio Optimization ◽  
Covariance Matrix ◽  
Stock Exchange ◽  
Optimal Portfolio ◽  
Risk Level ◽  
Mean Absolute Deviation ◽  
Investment Portfolio ◽  
Absolute Deviation ◽  
Singular Covariance Matrix ◽  
The Mean

Investing in the stock sector, investors often face risk problems. Usually, forming an investment portfolio is done to minimize risk. In this research, investment portfolio optimization is discussed. The data analyzed are 8 shares traded on the capital market in Indonesia through the Indonesia Stock Exchange (IDX). Optimization is performed using the Mean-Absolute Deviation model with the singular covariance matrix to determine the optimal weights. The results of portfolio optimization Mean-Absolute Deviation model with singular covariance matrix method, was obtained optimal portfolio weights that is of 17.22% for BBCA shares; 26.64% for TKIM shares; 9.96% for BBRI shares; 9.96% for BBNI shares; 8.70% for BMRI shares; 3.75% for ADRO shares; 6.52% for GGRM shares; and 17.25% for UNTR shares. Where the optimal portfolio composition is obtained the expected rate of return (expected return) of 0.18% with a portfolio risk level (standard deviation) of 0.07%.

ASYMMETRY OF RETURNS IN THE AUSTRALIAN STOCK MARKET

2006 ◽  
Vol 17 (01) ◽  
pp. 147-153
Author(s):  
SUSAN M. GUNNER ◽  
LOUISE BROOKS ◽  
ROBIN G. STORER
Keyword(s):  
Stock Market ◽  
Power Law ◽  
Stock Exchange ◽  
Optimal Portfolios

We use econophysics techniques to investigate the characteristics of the distribution of returns from the All Ordinaries Index and from optimal portfolios constructed from individual stocks on the Australian Stock Exchange. We find in general that the tails of the distributions are asymmetric and that the negative tail favours a power-law behaviour while the positive tail is more Gaussian.

OPTIMAL LOGARITHMIC UTILITY AND OPTIMAL PORTFOLIOS FOR AN INSIDER IN A STOCHASTIC VOLATILITY MARKET

2005 ◽  
Vol 08 (03) ◽  
pp. 301-319 ◽  
Author(s):  
CHRISTIAN-OLIVER EWALD
Keyword(s):  
Portfolio Optimization ◽  
Stochastic Volatility ◽  
Incomplete Markets ◽  
Insider Trading ◽  
Malliavin Calculus ◽  
Optimal Portfolio ◽  
Stochastic Volatility Model ◽  
Heston Model ◽  
Optimal Portfolios ◽  
Volatility Model

We combine methods for portfolio optimization in incomplete markets which are due to Karatzas et al. [6] with methods proposed by Nualart based on Malliavin Calculus to model insider trading within a stochastic volatility model. We compute the optimal portfolio within a certain set of insider strategies for a general stochastic volatility model but also apply the methods to explicit examples. We further discuss how the Heston model fits into this context.

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