Applying three VaR (value at risk) approaches in measuring market risk of stock portfolio: The case study of VN-30 stocks basket in HOSE

2017 ◽  
Vol 24 (2) ◽  
pp. 90-113
Author(s):  
VO THI QUY ◽  
NGUYEN QUANG THINH
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Market Risk ◽  
Stock Portfolio

scholarly journals VALUE AT RISK IN STOCK PORTFOLIO USING T-COPULA: Case Study of PT. Indofood Sukses Makmur, Tbk. and Bank Mandiri (Persero), Tbk.

2019 ◽  
Vol 12 (2) ◽  
pp. 175
Author(s):  
Qorina Rara Sartika ◽  
Tatik Widiharih ◽  
Moch Abdul Mukid
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
T Copula ◽  
Stock Portfolio

RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)

2021 ◽  
Vol 14 (2) ◽  
pp. 125-136
Author(s):  
Tarno Tarno ◽  
Trimono Trimono ◽  
Di Asih I Maruddani ◽  
Yuciana Wilandari ◽  
Rianti Siwi Utami
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Value At Risk ◽  
Stock Price ◽  
Holding Period ◽  
Return Variance ◽  
Stock Portfolio ◽  
Portfolio Return ◽  
Model Approach

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model is  Ensemble ARMA-GARCH with MSE 1.3231×10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.

scholarly journals Mean-value at risk portfolio selection problem using clustering technique : A case study

2019 ◽  
Author(s):  
Sheshma Kiran Kumari ◽  
P. Kumar ◽  
J. Priya ◽  
S. Surya ◽  
A. K. Bhurjee
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Mean Value ◽  
Selection Problem ◽  
Portfolio Selection Problem ◽  
Clustering Technique

MEASURING THE MARKET RISK OF FREIGHT RATES: A VALUE-AT-RISK APPROACH

2008 ◽  
Vol 11 (05) ◽  
pp. 447-469 ◽  
Author(s):  
TIMOTHEOS ANGELIDIS ◽  
GEORGE SKIADOPOULOS
Keyword(s):  
At Risk ◽  
Hedge Funds ◽  
Value At Risk ◽  
Statistical Tests ◽  
Market Risk ◽  
Freight Rate ◽  
Freight Rates ◽  
Rate Risk ◽  
Risk Approach ◽  
Non Parametric

The fluctuation of shipping freight rates (freight rate risk) is an important source of market risk for all participants in the freight markets including hedge funds, commodity and energy producers. We measure the freight rate risk by the Value-at-Risk (VaR) approach. A range of parametric and non-parametric VaR methods is applied to various popular freight markets for dry and wet cargoes. Backtesting is conducted in two stages by means of statistical tests and a subjective loss function that uses the Expected Shortfall, respectively. We find that the simplest non-parametric methods should be used to measure freight rate risk. In addition, freight rate risk is greater in the wet cargoes markets. The margins in the growing freight derivatives markets should be set accordingly.

scholarly journals Market Risk Measurement

2020 ◽  
Author(s):  
Emese Lazar ◽  
Ning Zhang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Preliminary Analysis ◽  
Risk Measures ◽  
Market Risk ◽  
Capital Requirements ◽  
Risk Measurement ◽  
Regulatory Framework ◽  
Bank Capital ◽  
Bank Capital Requirements

This chapter presents a preliminary analysis on how some market risk measures dramatically increased during the COVID-19 pandemic, with measures computed over longer horizons experiencing more pronounced effects. We provide examples when regulatory market risk measurement proved to be suboptimal, overestimating risk. A further issue was the large number of Value-at-Risk ‘exceptions’ during the first few months of the crisis, which normally leads to overinflated bank capital requirements. The current regulatory framework should address these problems by suggesting improvements to the calculation of risk measures and/or by modifying the rules which determine capital requirements to make them appropriate and realistic in crisis situations.

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