OPTIMAL ALLOCATION OF BANK CREDITS TO APPLICANTS IN AGRICULTURAL SECTORS

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.

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A Mean-Variance Portfolio Selection Model with Interval-Valued Possibility Measures

Mathematical Problems in Engineering ◽

10.1155/2020/4135740 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Yunyun Sui ◽

Jiangshan Hu ◽

Fang Ma

Keyword(s):

Portfolio Selection ◽

Return Rate ◽

Selection Model ◽

Expected Return ◽

Interval Numbers ◽

Numerical Example ◽

Mean Variance Portfolio ◽

Portfolio Selection Model ◽

Mean Variance ◽

Interval Valued

In recent years, fuzzy set theory and possibility theory have been widely used to deal with an uncertain decision environment characterized by vagueness and ambiguity in the financial market. Considering that the expected return rate of investors may not be a fixed real number but can be an interval number, this paper establishes an interval-valued possibilistic mean-variance portfolio selection model. In this model, the return rate of assets is regarded as a fuzzy number, and the expected return rate of assets is measured by the interval-valued possibilistic mean of fuzzy numbers. Therefore, the possibilistic portfolio selection model is transformed into an interval-valued optimization model. The optimal solution of the model is obtained by using the order relations of interval numbers. Finally, a numerical example is given. Through the numerical example, it is shown that, when compared with the traditional possibilistic model, the proposed model has more constraints and can better reflect investor psychology. It is an extension of the traditional possibilistic model and offers greater flexibility in reflecting investor expectations.

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Gestão Descentralizada de Carteiras

Brazilian Review of Finance ◽

10.12660/rbfin.v1n2.2003.1130 ◽

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.

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

Stats ◽

10.3390/stats4040059 ◽

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).

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

Croatian Review of Economic Business and Social Statistics ◽

10.2478/crebss-2019-0002 ◽

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.

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Expected and Realized Stock Returns: Evidence from India

Asian Journal of Empirical Research ◽

10.18488/journal.1007/2016.6.11/1007.11.270.278 ◽

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.

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Predicting inferences and confidence in inferences: Recognition heuristic vs. its new rival, mean-variance model

PsycEXTRA Dataset ◽

10.1037/e615882011-064 ◽

2009 ◽

Author(s):

Yvetta Simonyan ◽

Daniel G. Goldstein

Keyword(s):

Recognition Heuristic ◽

Variance Model ◽

Mean Variance

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Aplication of Quantitative and Qualitative Methods in Risk Management of Credit Portfolio in the Conditions of the Banking System of the Republic of Tajikistan

Olsztyn Economic Journal ◽

10.31648/oej.3234 ◽

2013 ◽

Vol 8 (3) ◽

pp. 249-268

Author(s):

Basgul Fajzullohonovna Isupova

Keyword(s):

Risk Assessment ◽

Risk Management ◽

Qualitative Methods ◽

Banking System ◽

Potential Losses ◽

The Republic ◽

Credit Portfolio ◽

Quantitative And Qualitative Methods ◽

Coefficient Method

In this article, an analysis of the fundamental methods of risk assessment and risk management of credit portfolio is conducted. In particular, complex and qualitative methods of risk management of credit portfolio studied in details, namely analytical, statistical and coefficient methods. Based on the coefficient method the author proposes a number of standards for the assessment of potential losses in credit activity.

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A VaR-Constrained Mean-Variance Model: Implications for Portfolio Selection and the Basle Capital Accord.

SSRN Electronic Journal ◽

10.2139/ssrn.275894 ◽

2001 ◽

Cited By ~ 2

Author(s):

Gordon J. Alexander ◽

Alexandre M. Baptista

Keyword(s):

Portfolio Selection ◽

Variance Model ◽

Mean Variance

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

Environment and Development Economics ◽

10.1017/s1355770x16000310 ◽

2016 ◽

Vol 22 (2) ◽

pp. 133-155 ◽

Cited By ~ 8

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.

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