JOURNAL OF ACCOUNTING, FINANCE, ECONOMICS, AND SOCIAL SCIENCES

Print ISSN  :  2708-616X     |  Online ISSN  :   2708-6178   |  DOI: https://doi.org/10.62458/160224

Volume 5 |  Number 1  |   January – June 2020   |  DOI: https://doi.org/10.62458/jafess.160224.5(1)36-48

Applying Value-at-Risk on A Portfolio Investment in The Cambodia Securities Exchange

Received : February 2020   |   Revised: April 2020   |   Accepted:  June 2020

 

Lim Siphat, PhD.
CamEd Business School, Cambodia
Email: [email protected]

ABSTRACT

Value-at-Risk (VaR) is a very famous and popular model which has been widely used to measure the potential exposure of the value of loss of an underlying asset or an investment portfolio at a certain confidence level and holding period. The main objective of this paper is the implement all of the three approaches applicable to estimate VaR namely non-parametric, parametric, and Monte-Carlo simulation VaR on the synthetic investment portfolio which consists of HKL’s bond and five stocks listing and trading in CSX besides the securities the portfolio also includes the FX and commodity, such as, gold and crude oil. At the position date the initial market value of this portfolio is KHR 591,514,539. With the confidence level of 95% and the holding period of 1 day VaR is KHR 6,198,453, KHR 5,523,467 and KHR 5,354,189 estimated by the non-parametric, parametric and Monte-Carlo simulation respectively. This research also indicates that the non-parametric VaR is very simple to implement; therefore, this approach is highly recommended for the investors who intention is the estimate the risk exposure of the value of the assets or portfolio. On the other, the parametric and Monte-Carlo simulation approaches, which is perceivably more difficult than the non-parametric, are highly recommended for the study which intention is to seek high accuracy.

Keywords: VaR, CSX, Monte-Carlo Simulation, investment portfolio.

 

Read full text

Cite this article with   Scribbr   or   QuillBot

Except where otherwise noted, content in JAFESS and CamEd OAR © 2016 by CamEd Business School is licensed under CC BY 4.0