OPTIMIZING PORTFOLIO RETURN WITH NAÏVE DIVERSIFICATION-BASED MODELLING

This study aims at applying naïve diversification-based modeling in formation of optimal portfolios and to test the superiority of these portfolios against its sectoral indexes. The population of this study are all companies listed on the Indonesia Stock Exchange which are grouped into 10 sectors, namely: Agriculture; Basic Industry; Consumer; Finance; Infrastructure; Manufacture; Mining; Miscelanous Industry; Property; and Trade. The sample of this company is Top 10 Constituents in each company sector listed in the fact sheet per sector, published by the Indonesia Stock Exchange. The analytical tools used were paired sample statistics, paired sample correlations and significance tests. The results shows that portfolio formed with naïve diversification modeling shows its superiority compared to its sectoral portfolio. The correlation test shows moderate significance relationship between returns and standard deviation of sectoral portfolios with naïve diversification-based portfolios, while beta shows no meaningful relationship between sectoral portfolios and portfolios with naïve diversification modeling. Discrimination tests show the significance of returns and standard deviations between sectoral and naïve diversification modeling-based portfolios. While in line with the correlation test, there is no significant difference between the beta of the two portfolios, so it appears that the volatility of the two portfolios cannot be separated from overall market movement. For bearish market conditions, the level of portfolio loss using naïve diversification modeling is lower than sector-based portfolios in the Indonesia Stock Exchange.Keywords:investment, sector indexes, simplified, portfolio modelling

Macroeconomic shocks and credit risk stress testing the Iranian banking sector

PurposeThe Iranian banking industry has been greatly affected by dramatic changes in macroeconomic conditions over the past several decades owing to volatile oil revenues, changing fiscal and monetary policies, and the imposition of US sanctions. The main objective of this paper is to estimate potential credit losses in the Iranian banking sector due to macroeconomic shocks and assess the minimum economic capital requirements under the baseline and distressed scenarios. The paper also contrasts the applications of linear and nonlinear models in estimating the impacts of macroeconomic shocks on financial institutions.Design/methodology/approachThe paper uses a multistage approach to derive the portfolio loss distribution for banks. In the first step, the dynamic relationship between the selected macroeconomic variables are estimated using a VAR model to generate the stress scenarios. In the second step, the default probabilities are estimated using a quantile regression model and the results are compared with those of the conventional linear models. Finally, the default probabilities are simulated for a one-year time horizon using Monte-Carlo method and the portfolio loss distribution is calculated for hypothetical portfolios. The expected loss includes the loss given default for loans drawn randomly and uniformly distributed and exposed at default values when loans are assigned a fixed value.FindingsThe results indicate that the loss distributions under all scenarios are skewed to the right, with the linear model results being very similar to those of quantile at the 50% quantile, but very unlike those at the 10% and 90% quantiles. Specifically, the quantile model for the 90% (10%) quantile generates estimates of minimum economic capital requirement that are considerably higher (lower) than those using the linear model.Research limitations/implicationsThe study has focused on credit risk because of lack of data on other types of risk at individual bank level. The future studies can estimate the aggregate economic capital using a risk aggregation approach and a panel data (not presently available), which could further improve the accuracy of the estimates.Practical implicationsThe fiscal and monetary authorities in developing countries, specially oil-exporting countries, can follow the risk assessment approach to assess the health of their banking system and adapt policies to mitigate the impacts of large macroeconomic shocks on their financial markets.Originality/valueThis is the first paper estimating the portfolio loss distribution for the Iranian banks under turbulent macroeconomic conditions using linear and nonlinear models. The case study can be applied to other developing and emerging countries, particularly those highly dependent on natural resources, prone to extreme macroeconomic shocks.

Forecasting plausible scenarios and losses in interest rate targeting using mathematical optimization

This study proposes a mathematical optimization model for simultaneously forecasting plausible market scenarios and portfolio losses. Interest rates, volatilities and correlation coefficients can be modeled by the DCC-GARCH. A constraint condition is set by the Mahalanobis distance for deciding an acceptable range of change in interest rates. An objective function is set as a hypothetical market portfolio loss from delta, gamma and vega. The mathematical optimization model becomes a nonlinear programming problem for which it is difficult to find appropriate solutions. Therefore, the study introduces an original heuristic approach for preventing the signs of solutions from unintentionally becoming inverse. The study finds that, compared to a stressful scenario in Japan, the forecasting scenarios and losses are plausible.