scholarly journals Value at Risk as a Measurement of Market Risk in Emerging Sharia Market: A Comparative Study Between Indexes in Indonesian Stock Exchange

2018 ◽  
Vol 3 (5) ◽  
pp. 94
Author(s):  
B N Suryawati ◽  
L Unsun-Nidhal
Keyword(s):  
At Risk ◽  
Comparative Study ◽  
Value At Risk ◽  
Stock Exchange ◽  
Market Risk

.

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.

scholarly journals Market Risk Model agricolture portfolio value-at-risk

2020 ◽  
Author(s):  
Giulio Carlone
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model ◽  
Stock Exchange ◽  
Market Risk ◽  
Point Of View ◽  
Market Model ◽  
Basel Accords ◽  
Price Distortions ◽  
Portfolio Investments

Abstract Thinking about this current extreme scenario of stock exchange observed in a world scenario perspective and the related choices for worldbank portfolio investments in Agricolture commodity, this study its based in an advanced economic observation and analisys of the Agricolture commodity in a scenario of portfolio diversification without have the market risk default. This study its based in an advanced financial strategy to define the market model composed of London stock exchange agricolture commodity observed first in a London scenario and second in a Europe scenario and finally in a world scenario. The authorities regulation and the requirements used to define , the mathematical point of view and to describe , the market value at risk point of view , have been standardized in this empirical market model. The commodity scenario observed and the empirical market model defined to observe the max price distortions of the agricolture commodity defined and defined to observe the porfolio value at risk , are in this market model well described and standardized. Authorities are interested in the empirical market model to observe the VaR data because they are interested in a bank’s ability to withstand extreme events. VaR is monitored and is sanctioned by regulators defined in the Basel accords. The observed price are used in a variable choice of number of data price observation of five price for week a data price observation of one prices for week and a data price observation of two price for week and further similar strategies .

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: Evidence from Pakistan Stock Exchange

2011 ◽  
Vol 5 (17) ◽  
pp. 7474-7480 ◽  
Author(s):  
Nawaz Faisal ◽  
Afzal Muhammad
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange

scholarly journals Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

2017 ◽  
Vol 65 (5) ◽  
pp. 1687-1694
Author(s):  
Tomáš Konderla ◽  
Václav Klepáč
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Listed Company ◽  
Mixture Distribution ◽  
Normal Mixture ◽  
Development Characteristic ◽  
The Common

The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA‑GARCH model based VaR estimates. The common testing via Kupiec and Christoffersen procedures offer generalization that HMM model performs better that volatility based VaR estimation technique in terms of accuracy, even with the simpler HMM with normal‑mixture distribution against previously used GARCH with many types of non‑normal innovations.

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