scholarly journals Role of International Trade Competitive Advantage and Corporate Governance Quality in Predicting Equity Returns: Static and Conditional Model Proposals for an Emerging Market

2021 ◽  
Vol 14 (3) ◽  
pp. 125
Author(s):  
Erol Muzir ◽  
Cevdet Kizil ◽  
Burak Ceylan
Keyword(s):  
Corporate Governance ◽  
International Trade ◽  
Dynamic Models ◽  
Robust Regression ◽  
Emerging Market ◽  
Static Model ◽  
Conditional Model ◽  
Portfolio Returns ◽  
Governance Quality ◽  
Corporate Governance Quality

This paper aims to develop some static and conditional (dynamic) models to predict portfolio returns in the Borsa Istanbul (BIST) that are calibrated to combine the capital asset-pricing model (CAPM) and corporate governance quality. In our conditional model proposals, both the traditional CAPM (beta) coefficient and model constant are allowed to vary on a binary basis with any degradation or improvement in the country’s international trade competitiveness, and meanwhile a new variable is added to the models to represent the portfolio’s sensitivity to excess returns on the governance portfolio (BIST Governance) over the market. Some robust and Bayesian linear models have been derived using the monthly capital gains between December 2009 and December 2019 of four leading index portfolios. A crude measure is then introduced that we think can be used in assessing governance quality of portfolios. This is called governance quality score (GQS). Our robust regression findings suggest both superiority of conditional models assuming varying beta coefficients over static model proposals and significant impact of corporate governance quality on portfolio returns. The Bayesian model proposals, however, exhibited robust findings that favor the static model with fixed beta estimates and were lacking in supporting significance of corporate governance quality.

Implied volatility and future portfolio returns

2007 ◽  
Vol 31 (10) ◽  
pp. 3183-3199 ◽  
Author(s):  
Prithviraj S. Banerjee ◽  
James S. Doran ◽  
David R. Peterson
Keyword(s):  
Implied Volatility ◽  
Portfolio Returns

scholarly journals CHARACTERISTICS OF OMEGA-OPTIMIZED PORTFOLIOS AT DIFFERENT LEVELS OF THRESHOLD RETURNS

2014 ◽  
Vol 12 (2) ◽  
pp. 245-265 ◽  
Author(s):  
Renaldas Vilkancas
Keyword(s):  
Portfolio Optimization ◽  
Risk Measures ◽  
High Threshold ◽  
Portfolio Performance ◽  
Threshold Values ◽  
Portfolio Returns ◽  
Upside Risk ◽  
And Performance ◽  
Risk Free Rate ◽  
Different Levels

There is little literature considering effects that the loss-gain threshold used for dividing good and bad outcomes by all downside (upside) risk measures has on portfolio optimization and performance. The purpose of this study is to assess the performance of portfolios optimized with respect to the Omega function developed by Keating and Shadwick at different levels of the threshold returns. The most common choices of the threshold values used in various Omega studies cover the risk-free rate and the average market return or simply a zero return, even though the inventors of this measure for risk warn that “using the values of the Omega function at particular points can be critically misleading” and that “only the entire Omega function contains information on distribution”. The obtained results demonstrate the importance of the selected values of the threshold return on portfolio performance – higher levels of the threshold lead to an increase in portfolio returns, albeit at the expense of a higher risk. In fact, within a certain threshold interval, Omega-optimized portfolios achieved the highest net return, compared with all other strategies for portfolio optimization using three different test datasets. However, beyond a certain limit, high threshold values will actually start hurting portfolio performance while meta-heuristic optimizers typically are able to produce a solution at any level of the threshold, and the obtained results would most likely be financially meaningless.

scholarly journals Kelly's Strategy Analysis in Optimizing Investment Portfolios in Foreign Exchange

2019 ◽  
Vol 8 (2S7) ◽  
pp. 245-250
Keyword(s):  
Financial Markets ◽  
Foreign Exchange ◽  
Optimal Portfolio ◽  
Minimal Risk ◽  
Investment Portfolio ◽  
Strategy Analysis ◽  
Average Value ◽  
Portfolio Returns ◽  
Strategy Model ◽  
Investment Portfolios

Investments in financial markets not only pay attention to promising profits, but also need to consider the risks that follow. Risks can be minimized by establishing an investment portfolio. This research was conducted with the aim of analyzing optimal portfolios on foreign exchange investments, so that investments made provide maximum returns at certain risks, or minimal risk on certain returns. The data analyzed in this study are foreign exchange traded at Bank Indonesia. Data analysis is carried out quantitatively using the Kelly Strategy model. The steps: (i) Calculation of individual foreign exchange returns, (ii) Determine the average value of individual foreign exchange returns, (iii) Determine the optimal portfolio using the Kelly strategy approach, and (iv) Determine portfolio returns and risks. Based on the results of the analysis obtained the allocation of weights that provide returns and risks to the optimal portfolio. A 95% USD currency is an optimal portfolio of the five currencies used. So that it can be used as a consideration for investors, in making investment decisions in the foreign exchange being analyzed.

scholarly journals ESTIMATING VALUE-AT-RISK BASED ON NON-NORMAL DISTRIBUTIONS

2015 ◽  
Vol 3 ◽  
pp. 188-195 ◽  
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
At Risk ◽  
Risk Factor ◽  
Value At Risk ◽  
Likelihood Method ◽  
Normal Distributions ◽  
Confidence Levels ◽  
Portfolio Returns ◽  
Distribution Parameters ◽  
Financial Portfolios ◽  
Daily Changes

The article presents a comparative study of parametric linear value-at-risk (VaR) models used for estimating the risk of financial portfolios. We illustrate how to adjust VaR for auto-correlation in portfolio returns. The article presents static and dynamic methodology to compute VaR, based on the assumption that daily changes are independent and identically distributed (normal or non-normal) or auto-correlated in terms of the risk factor dynamics. We estimate the parametric linear VaR over a risk horizon of 1 day and 10 days at 99% and 95% confidence levels for the same data. We compare the parametric VaR and a VaR obtained using Monte Carlo simulations with historical simulations and use the maximum likelihood method to calibrate the distribution parameters of our risk factors. The study investigated whether the parametric linear VaR applies to contemporary risk factor analysis and pertained to selected foreign rates.

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