Upper Bounds of Stock Portfolio Investment Risk Using Value at Risk (Case Study of Indonesian Blue-Chip Stocks in 2022)

Articles in Press

Authors

1 Department of Management, Faculty of Economics and Busniness, Diponegoro University, Semarang, Indonesia

2 Department of Accounting, Faculty of Economics and Business, Diponegoro University, Semarang, Indonesia

3 Department of Accounting, Faculty of Economic and Business, Diponegoro University, Semarang, Indonesia.

4 Program of Data Science Study , Faculty of Computer Science, UPN "Veteran" Jawa Timur, Semarang, Indonesia

5 Department of Management, Faculty of Economics and Business, Diponegoro University, Semarang, Indonesia

Abstract

In recent years, stocks become the most preferred asset by Indonesian investors. Besides offering large profits, stock investment also has a risk factor that can occur at any time. One way to minimize risk is to form a stock portfolio. This paper aims to measure the upper bounds of the portfolio loss risk formed by several single assets that are mutually dependent. The upper bound value is chosen because the exact value of portfolio loss risk is difficult to obtained by Convolution or Panjer Recursion methods. The main analysis of this research is formed the upper bounds of stock portfolio investment risk using VaR with Cornish Fisher Expansion aproach by utilized comonotonicity and convex order properties. The portofolio contains of 3 single asset (ARTO.JK, ITMG.JK, and MIKA.JK) which collected from IDX Indonesia from 10/25/21 to 10/21/22. The novelty of this research is combined comonotonicity and convex order properties with VaR-CFE to get upper bounds of portolio risk predicition. The result show that at 95% significance level and 1-day holding period, the upper bounds of VaR-CFE prediction for the portfolio is -0.1394. The social impact of this research can be a benchmark to get accurate risk prediction of their portfolio asset.

Keywords

Iranian Economic Review

Articles in Press, Accepted Manuscript
Available Online from 07 October 2023

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