scholarly journals Portfolio Management of Copula-Dependent Assets Based on P(Y < X) Reliability Models: Revisiting Frank Copula and Dagum Distributions

Stats ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 1027-1050
Author(s):  
Pushpa Narayan Rathie ◽  
Luan Carlos de Sena Monteiro Ozelim ◽  
Bernardo Borba de Andrade
Keyword(s):  
Stock Returns ◽  
Portfolio Management ◽  
Reliability Index ◽  
Relative Effect ◽  
Mathematical Framework ◽  
Variance Model ◽  
Reliability Models ◽  
Frank Copula ◽  
Return Distributions ◽  
Mean Variance

Modern portfolio theory indicates that portfolio optimization can be carried out based on the mean-variance model, where returns and risk are represented as the average and variance of the historical data of the stock’s returns, respectively. Several studies have been carried out to find better risk proxies, as variance was not that accurate. On the other hand, fewer papers are devoted to better model/characterize returns. In the present paper, we explore the use of the reliability measure P(Y<X) to choose between portfolios with returns given by the distributions X and Y. Thus, instead of comparing the expected values of X and Y, we will explore the metric P(Y<X) as a proxy parameter for return. The dependence between such distributions shall be modelled by copulas. At first, we derive some general results which allows us to split the value of P(Y<X) as the sum of independent and dependent parts, in general, for copula-dependent assets. Then, to further develop our mathematical framework, we chose Frank copula to model the dependency between assets. In the process, we derive a new polynomial representation for Frank copulas. To perform a study case, we considered assets whose returns’ distributions follow Dagum distributions or their transformations. We carried out a parametric analysis, indicating the relative effect of the dependency of return distributions over the reliability index P(Y<X). Finally, we illustrate our methodology by performing a comparison between stock returns, which could be used to build portfolios based on the value of the the reliability index P(Y<X).

scholarly journals Gestão Descentralizada de Carteiras

2003 ◽  
Vol 1 (2) ◽  
pp. 243
Author(s):  
Paulo Coutinho ◽  
Benjamin Miranda Tabak
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Portfolio Management ◽  
Asset Returns ◽  
Portfolio Allocation ◽  
Multivariate Normal ◽  
Variance Model ◽  
Negative Exponential Function ◽  
Negative Exponential ◽  
Mean Variance

We use a mean-variance model to analyze the problem of decentralized portfolio management. We find the solution for the optimal portfolio allocation for a head trader operating in <i>n</i> different markets, which is called the optimal centralized portfolio. However, as there are many traders specialized in different markets, the solution to the problem of optimal decentralized allocation should be different from the centralized case. In this paper we derive conditions for the solutions to be equivalent. We use multivariate normal returns and a negative exponential function to solve the problem analytically. We generate the equivalence of solutions by assuming that different traders face different interest rates for borrowing and lending. This interest rate is dependent on the ratio of the degrees of risk aversion of the trader and the head trader, on the excess return, and on the correlation between asset returns.

scholarly journals OPTIMAL ALLOCATION OF BANK CREDITS TO APPLICANTS IN AGRICULTURAL SECTORS

2021 ◽  
Vol 21 (no 1) ◽  
Author(s):  
Abouzar Nahvi ◽  
Mohammad Ghorbani Ghorbani ◽  
Mahmoud Sabouhi ◽  
Arash Dourandish ◽  
Arash Dourandish ◽  
...  
Keyword(s):  
Portfolio Management ◽  
Banking System ◽  
Mixed Strategies ◽  
Expected Return ◽  
Interval Numbers ◽  
Variance Model ◽  
Linear Interval ◽  
Objective Model ◽  
Credit Portfolio ◽  
Mean Variance

Credit portfolio management is one of the fundamental aspects of banking that can lead to the loss of bank revenue if not properly managed. The expected return and risk in the choice of portfolio cannot be accurately predicted. Considering this impossibility and given the limitations faced by the banking system, this article uses the concept of interval numbers in the Fuzzy Set Theory to extend the Markowitz mean variance model to a non-linear interval multi-objective model. Three strategies were presented in this model, including optimistic, pessimistic, and mixed strategies, and the Genetic algorithm was used to solve the model. This model was ultimately examined at Keshavarzi Bank to determine the optimal credit portfolioCredit portfolio management is one of the fundamental aspects of banking that can lead to the loss of bank revenue if not properly managed. The expected return and risk in the choice of portfolio cannot be accurately predicted. Considering this impossibility and given the limitations faced by the banking system, this article uses the concept of interval numbers in the Fuzzy Set Theory to extend the Markowitz mean variance model to a non-linear interval multi-objective model. Three strategies were presented in this model, including optimistic, pessimistic, and mixed strategies, and the Genetic algorithm was used to solve the model. This model was ultimately examined at Keshavarzi Bank to determine the optimal credit portfolio. The results showed that this bank’s risk, thus leading to the proper management of loans.

Expected and Realized Stock Returns: Evidence from India

2017 ◽  
Vol 6 (11) ◽  
pp. 270-278
Author(s):  
Pritpal Singh Bhullar ◽  
Pradeep K Gupta
Keyword(s):  
Stock Returns ◽  
Real World ◽  
Investment Risk ◽  
Expected Return ◽  
Variance Model ◽  
Markowitz Portfolio ◽  
Market Beta ◽  
Actual Return ◽  
The Relationship ◽  
Mean Variance

Markowitz Portfolio theory is based on the expected return and risk but investors are more interested in realized return. The considerations, expected return as realized return and variance as investment risk, of Markowitz’s mean – variance model enable the researchers or scholars to further explore on the validity of Markowitz theory. The present study makes an attempt to unfold a new idea in investment scenario where Markowitz theory is empirically tested on realized return and risk as well as on realized return and expected return in the context of India. The findings show that a large variation in Expected Return is explained by the risk (Market Beta) alone and this risk and Expected return are significantly negatively related. However, the risk (Market Beta) and Realized return are insignificantly related. Further, a very low variation in the Realized (Actual) Return is explained by the Expected Return and the Expected Return and the Realised Return are insignificantly positively related. Thus, it is considered that the Markowitz model is not possible to implement in the real world even though the relationship holds good. This study acts as one of the guiding tools for investors in transforming their new age investment philosophy.

scholarly journals Market-based instruments for risk-averse farmers: rubber agroforest conservation in Jambi Province, Indonesia

2016 ◽  
Vol 22 (2) ◽  
pp. 133-155 ◽  
Author(s):  
Utkur Djanibekov ◽  
Grace B. Villamor
Keyword(s):  
Income Inequality ◽  
Risk Aversion ◽  
Real Options ◽  
Risk Averse ◽  
Variance Model ◽  
Income Uncertainty ◽  
Real Options Approach ◽  
Pigovian Taxes ◽  
Rubber Agroforest ◽  
Mean Variance

AbstractThis paper investigates the effectiveness of different market-based instruments (MBIs), such as eco-certification premiums, carbon payments, Pigovian taxes and their combination, to address the conversion of agroforests to monoculture systems and subsequent effects on incomes of risk-averse farmers under income uncertainty in Indonesia. For these, the authors develop a farm-level dynamic mean-variance model combined with a real options approach. Findings show that the conservation of agroforest is responsive to the risk-aversion level of farmers: the greater the level of risk aversion, the greater is the conserved area of agroforest. However, for all risk-averse farmers, additional incentives in the form of MBIs are still needed to prevent conversion of agroforest over the years, and only the combination of MBIs can achieve this target. Implementing fixed MBIs also contributes to stabilizing farmers’ incomes and reducing income risks. Consequently, the combined MBIs increase incomes and reduce income inequality between hardly and extremely risk-averse farmers.

Portfolio optimization with transaction costs: a two-period mean-variance model

2014 ◽  
Vol 233 (1) ◽  
pp. 135-156 ◽  
Author(s):  
Ying Hui Fu ◽  
Kien Ming Ng ◽  
Boray Huang ◽  
Huei Chuen Huang
Keyword(s):  
Transaction Costs ◽  
Portfolio Optimization ◽  
Variance Model ◽  
Mean Variance

scholarly journals Multi-time state mean-variance model in continuous time

2021 ◽  
Vol 27 ◽  
pp. 92
Author(s):  
Shuzhen Yang
Keyword(s):  
Boundary Conditions ◽  
Continuous Time ◽  
Optimal Strategy ◽  
Investment Portfolio ◽  
Variance Model ◽  
Investment Plan ◽  
Terminal Time ◽  
Variance Risk ◽  
Maximum Drawdown ◽  
Mean Variance

The objective of the continuous time mean-variance model is to minimize the variance (risk) of an investment portfolio with a given mean at the terminal time. However, the investor can stop the investment plan at any time before the terminal time. To solve this problem, we consider to minimize the variances of the investment portfolio in the multi-time state. The advantage of this multi-time state mean-variance model is the minimization of the risk of the investment portfolio within the investment period. To obtain the optimal strategy of the model, we introduce a sequence of Riccati equations, which are connected by jump boundary conditions. In addition, we establish the relationships between the means and variances in the multi-time state mean-variance model. Furthermore, we use an example to verify that the variances of the multi-time state can affect the average of Maximum-Drawdown of the investment portfolio.

scholarly journals Changes in the Portfolio Management and Construction under the Pandemic Era

2021 ◽  
Vol 275 ◽  
pp. 01005
Author(s):  
Ruipeng Tan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stock Returns ◽  
Financial Market ◽  
Portfolio Management ◽  
Portfolio Construction ◽  
Sample Data ◽  
Before And After ◽  
Reduce Risk ◽  
The Impact

This paper focuses on comparing portfolio management and construction before and after the coronavirus. First, this paper presents the importance of building up portfolios for investors to diversify their risks. Theories on portfolio management are discussed in this section to show how they have been developed to help on investing and reduce risk. Then, the paper moves on to show the impact of the pandemic on the financial market and portfolio management. Sample data on tech stock returns are collected to perform a Monte Carlo simulation on portfolio construction to find out the efficient portfolio before and after the COVID-19 outbreak. The efficient portfolio is build based on the Markowitz theory to find the combination. Comparisons between these portfolio constructions are made to find out the changes in portfolio management and construction under the pandemic era. In conclusion, this paper presents how pandemic has changed and impacted the investments and lists recommendations on future portfolio management and construction.

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