scholarly journals 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 (02) ◽  
pp. 90-113
Author(s):  
Thinh Nguyen Quang ◽  
Quy Vo Thi
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Stock Price ◽  
Stock Exchange ◽  
Market Risk ◽  
Risk Models ◽  
Historical Simulation ◽  
Back Testing

This study examines and applies the three statistical value at risk models including variance-covariance, historical simulation, and Monte Carlo simulation in measuring market risk of VN-30 portfolio of Ho Chi Minh stock exchange (HOSE) in Vietnam stock market and some back-testing techniques in assessing the validity of the VaR performance in the timeframe of January 30, 2012–February 26, 2016. The models are constructed from two volatility methods of stock price: SMA and EWMA throughout the five chosen confi-dence level: 90%, 93%, 95%, 97.5%, and 99%. The findings of the study show that the differences among the results of three models are not significant. Additionally, three VaR (Value at Risk) models have generally the similar accepted range assessed in both types of back-tests at all confidence levels considered and at the 97.5% con-fidence level. They can work best to achieve the highest validity level of results in satisfying both conditional and unconditional back-tests. The Monte Carlo Simulation (MCS) has been considered the most appropriate method to apply in the context of VN-30 port-folio due to its flexibility in distribution simulation. Recommenda-tions for further research and investigations are provided according-ly.

scholarly journals Value-at-Risk Estimation of Equity Market Risk in India

2021 ◽  
Vol 9 (1) ◽  
pp. 1-24
Author(s):  
Jitender
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Moving Average ◽  
Equity Market ◽  
Market Risk ◽  
Financial Instrument ◽  
Historical Simulation ◽  
Distributed Models

Abstract The value-at-risk (Va) method in market risk management is becoming a benchmark for measuring “market risk” for any financial instrument. The present study aims at examining which VaR model best describes the risk arising out of the Indian equity market (Bombay Stock Exchange (BSE) Sensex). Using data from 2006 to 2015, the VaR figures associated with parametric (variance–covariance, Exponentially Weighted Moving Average, Generalized Autoregressive Conditional Heteroskedasticity) and non-parametric (historical simulation and Monte Carlo simulation) methods have been calculated. The study concludes that VaR models based on the assumption of normality underestimate the risk when returns are non-normally distributed. Models that capture fat-tailed behaviour of financial returns (historical simulation) are better able to capture the risk arising out of the financial instrument.

Price Limits, the VaR calculation, and a Monte Carlo Simulation

2002 ◽  
Vol 10 (1) ◽  
pp. 81-111
Author(s):  
Jin Yoo
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Level ◽  
Value At Risk ◽  
Stock Price ◽  
Price Limits ◽  
Holding Period ◽  
A Value ◽  
Practical Implications

This paper raises an issue of calculating a value at risk (VaR) of a stock price in the presence of daily price limits, suggests an appropriate methodology for it, and discusses its practical implications. One finding is that the VaR with price limits is never bigger than without. It turns out that the discrepancy between the two VaRs increases as the confidence level rises, the holding period lengthens, the volatility goes up, or the price limits get tighter.

scholarly journals A comparative analysis of Islamic unit trust portfolio using value at risk methodologies

2020 ◽  
Vol 5 (2) ◽  
pp. 12
Author(s):  
Farah Azaliney Mohd Amin ◽  
Nur Qamarina Ghazali ◽  
Nursahira Zainalbidin ◽  
Nur Najwa Alia Kamarudin
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Percentage Error ◽  
Historical Simulation ◽  
Fund Managers ◽  
Equity Funds ◽  
Financial Consultants ◽  
Unit Trust

Islamic unit trust is a sunrise industry in the Malaysian capital market over the last decades to fulfill the demand from its Muslim investors. Muslim investors are only willing to invest their capital if the investment does not conflict with their religious beliefs, namely Islam. Previously, most of the studies focused to evaluate the performance of unit trust funds relative to the market as a whole. Meanwhile, it is also important for investors to accurately measure their downside risk because it is closely related to their future losses. Thus, Value at Risk (VaR) concept was introduced to calculate monthly risk for an Islamic unit trust portfolio using the three standard approaches which are Delta Normal, Historical Simulation and Monte Carlo Simulation. Results show that Monte Carlo Simulation is the best method to quantify risk exposure as the average Mean Absolute Percentage Error (MAPE) is the lowest compared to the other two methods. The findings also highlight the importance of embedding risk into investment analyses and provide insights to investors who are considering Shariah-compliant equity funds as a potential income-generating instrument. Therefore, financial consultants or fund managers can make informed decisions in setting up a well-diversified unit trust fund’s portfolio for their Muslim investors by applying the concept of VaR and its methodologies.

scholarly journals On the existence of an optimal estimation window for risk measures

2015 ◽  
Vol 4 (1and2) ◽  
pp. 28
Author(s):  
Marcelo Brutti Righi ◽  
Paulo Sergio Ceretta
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Optimal Estimation ◽  
Expected Shortfall ◽  
Estimation Bias ◽  
Optimal Length

We investigate whether there can exist an optimal estimation window for financial risk measures. Accordingly, we propose a procedure that achieves optimal estimation window by minimizing estimation bias. Using results from a Monte Carlo simulation for Value at Risk and Expected Shortfall in distinct scenarios, we conclude that the optimal length for the estimation window is not random but has very clear patterns. Our findings can contribute to the literature, as studies have typically neglected the estimation window choice or relied on arbitrary choices.

scholarly journals EVALUATING MEASURES OF MARKET RISK IN CIRCUMSTANCES OF GLOBAL FINANCIAL CRISIS – EMPIRICAL EVIDENCE FROM FIVE COUNTRIES

2013 ◽  
Vol 1 ◽  
pp. 75-81
Author(s):  
Ivica Terzić ◽  
Marko Milojević
Keyword(s):  
Value At Risk ◽  
Global Financial Crisis ◽  
Market Risk ◽  
Var Models ◽  
Risk Models ◽  
Historical Simulation ◽  
Rolling Window ◽  
Independence Properties ◽  
Window Approach ◽  
Metrics Model

The purpose of this paper is to evaluate performance of value-at-risk (VaR) produced by two risk models: historical simulation and Risk Metrics. We perform three backtest: unconditional coverage, independence and conditional coverage. We present results on both VaR 1% and VaR 5% on a one-day horizon for the following indices: S&P 500, DAX, SAX, PX and Belex 15. Our results show that Historical simulation 500 days rolling window approach satisfies unconditional coverage for all tested indices, while Risk Metrics has many rejection cases. On the other hand Risk Metrics model satisfies independence backtest for three indices, while Historical simulation has rejected more times. Based on our strong criteria to accept accuracy of VaR models only if both unconditional coverage and independence properties are satisfied, results indicate that during the crisis period all tested VaR models underestimate the true level of market risk exposure.

scholarly journals PERHITUNGAN VALUE AT RISK (VaR) DENGAN METODE HISTORIS DAN MONTE CARLO PADA SAHAM SUB SEKTOR ROKOK

2018 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Achmad Dimas ◽  
Muhammad Azhari ◽  
Khairunnisa Khairunnisa
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Time Series Data ◽  
Random Number Generation ◽  
Simulation Method ◽  
Series Data ◽  
Historical Simulation ◽  
Historical Method ◽  
Sample Data

The government’s policy, the Indonesian Ulema Council’s (MUI) fatwa, the rise of cigarette issues and anti-smoking campaigns have been a major challenge for the tobacco industry in managing risks. Through this research, the issues will be measured by VaR to know the risk of the company’s shares of cigarette sub sector by using time series data and analyzed by using the simulation method of Historis and Monte Carlo. The results showed the VaR value of GGRM and HMSP stock with the historical method is 3.28 and 2.54%. While the value of VaR shares GGRM and HMSP with Monte Carlo method is 3.52% and 3.14%. Monte Carlo simulation gives greater result than Historical Simulation, because Monte Carlo simulation do iteration repeatedly by involving random number generation and many synthesize the data so that sample data becomes more which makes the calculation is bigger.

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