scholarly journals An analytical method of estimating Value-at-Risk on the Belgrade Stock Exchange

2009 ◽  
Vol 54 (183) ◽  
pp. 119-138 ◽  
Author(s):  
Milica Obadovic ◽  
Mirjana Obadovic
Keyword(s):  
At Risk ◽  
Failure Rate ◽  
Confidence Level ◽  
Analytical Method ◽  
Value At Risk ◽  
Risk Evaluation ◽  
Risk Model ◽  
Stock Exchange ◽  
Confidence Levels ◽  
The Given

This paper presents market risk evaluation for a portfolio consisting of shares that are continuously traded on the Belgrade Stock Exchange, by applying the Value-at-Risk model - the analytical method. It describes the manner of analytical method application and compares the results obtained by implementing this method at different confidence levels. Method verification was carried out on the basis of the failure rate that demonstrated the confidence level for which this method was acceptable in view of the given conditions.

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 Return distribution and value at risk estimation for BELEX15

2011 ◽  
Vol 21 (1) ◽  
pp. 103-118 ◽  
Author(s):  
Dragan Djoric ◽  
Emilija Nikolic-Djoric
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Var Model ◽  
Ratio Test ◽  
Sample Period ◽  
Return Distribution ◽  
Model Accuracy ◽  
Confidence Levels

The aim of this paper is to find distributions that adequately describe returns of the Belgrade Stock Exchange index BELEX15. The sample period covers 1067 trading days from 4 October 2005 to 25 December 2009. The obtained models were considered in estimating Value at Risk ( VaR ) at various confidence levels. Evaluation of VaR model accuracy was based on Kupiec likelihood ratio test.

Estimation of Value-at-Risk Based on ARFIMA-FIAPARCH-SKST Model

2012 ◽  
Vol 601 ◽  
pp. 464-469
Author(s):  
Bin Tan
Keyword(s):  
At Risk ◽  
Financial Market ◽  
Long Memory ◽  
Value At Risk ◽  
Risk Model ◽  
Leverage Effect ◽  
Test Accuracy ◽  
Stylized Facts ◽  
Confidence Levels ◽  
Significant Difference

This paper focus mainly on some important stylized facts in financial market, such as long memory, asymmetry and leverage effect, and so on, and apply ARFIMA-APARCH-SKST model to measure dynamic Value at Risk, at the same time, ARMA-EGARCH(APARCH)-SKST, ARFIMA- FIEGARCH-SKST are used to compare empirical effect of different risk model, at last, we apply LRT method to test accuracy of risk model. Our results indicate that all models used in this paper can measure dynamic VaR at 95%, 99% and 99.5% confidence levels, and there is no significant difference for different risk model for different stock markets. Moreover, we find also that long memory is not more valuable stylized fact than asymmetry for SSEC and S&P500.

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

Multifractal Value at Risk model

2016 ◽  
Vol 451 ◽  
pp. 113-122 ◽  
Author(s):  
Hojin Lee ◽  
Jae Wook Song ◽  
Woojin Chang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Model

scholarly journals Collective Value-At-Risk (Colvar) In Life Insurance Collection

2018 ◽  
Vol 7 (3.7) ◽  
pp. 25
Author(s):  
Abdul Talib Bon ◽  
Muhammad Iqbal Al-Banna Ismail ◽  
Sukono . ◽  
Adhitya Ronnie Effendie
Keyword(s):  
At Risk ◽  
Standard Deviation ◽  
Life Insurance ◽  
Value At Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Model Development ◽  
Insurance Company ◽  
Insurance Claims ◽  
Level Of Significance

Analysis of risk in life insurance claims is very important to do by the insurance company actuary. Risk in life insurance claims are generally measured using the standard deviation or variance. The problem is, that the standard deviation or variance which is used as a measure of the risk of a claim can not accommodate any claims of risk events. Therefore, in this study developed a model called risk measures Collective Modified Value-at-Risk. Model development is done for several models of the distribution of the number of claims and the distribution of the value of the claim. Collective results of model development Modified Value-at-Risk is expected to accommodate any claims of risk events, when given a certain level of significance  

scholarly journals PENDEKATAN METODE MARKOWITZ UNTUK OPTIMALISASI PORTOFOLIO DENGAN RISIKO EXPECTED SHORTFALL (ES) PADA SAHAM SYARIAH DILENGKAPI GUI MATLAB

2022 ◽  
Vol 10 (4) ◽  
pp. 508-517
Author(s):  
Umiyatun Muthohiroh ◽  
Rita Rahmawati ◽  
Dwi Ispriyanti
Keyword(s):  
At Risk ◽  
Risk Analysis ◽  
Confidence Level ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Optimal Portfolio ◽  
Optimal Portfolios

A portfolio is a combination of two or more securities as investment targets for a certain period of time with certain conditions. The Markowitz method is a method that emphasizes efforts to maximize return expectations and can minimize stock risk. One method that can be used to measure risk is Expected Shortfall (ES). ES is an expected measure of risk whose value is above Value-at-Risk (VaR). To make it easier to calculate optimal portfolios with the Markowitz method and risk analysis with ES, an application was made using the Matlab GUI. The data used in this study consisted of three JII stocks including CPIN, CTRA, and BSDE stocks. The results of the portfolio formation with the Markowitz method obtained an optimal portfolio, namely the combination of CPIN = 34.7% and BSDE = 65.3% stocks. At the 95% confidence level, the ES value of 0.206727 is greater than the VaR value (0.15512).  

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.

scholarly journals ESTIMATING VALUE-AT-RISK BASED ON NON-NORMAL DISTRIBUTIONS

2015 ◽  
Vol 3 ◽  
pp. 188-195 ◽  
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
At Risk ◽  
Risk Factor ◽  
Value At Risk ◽  
Likelihood Method ◽  
Normal Distributions ◽  
Confidence Levels ◽  
Portfolio Returns ◽  
Distribution Parameters ◽  
Financial Portfolios ◽  
Daily Changes

The article presents a comparative study of parametric linear value-at-risk (VaR) models used for estimating the risk of financial portfolios. We illustrate how to adjust VaR for auto-correlation in portfolio returns. The article presents static and dynamic methodology to compute VaR, based on the assumption that daily changes are independent and identically distributed (normal or non-normal) or auto-correlated in terms of the risk factor dynamics. We estimate the parametric linear VaR over a risk horizon of 1 day and 10 days at 99% and 95% confidence levels for the same data. We compare the parametric VaR and a VaR obtained using Monte Carlo simulations with historical simulations and use the maximum likelihood method to calibrate the distribution parameters of our risk factors. The study investigated whether the parametric linear VaR applies to contemporary risk factor analysis and pertained to selected foreign rates.

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