scholarly journals Calculation of Value-at-Risk Variance-Covariance with the Approach of Simple Cash Portfolio, Factor Models and Cash Flow

2020 ◽  
Vol 1 (1) ◽  
pp. 19-24
Author(s):  
Puspa Liza Ghazali ◽  
Riaman Riaman ◽  
Ristifani Ulfatmi
Keyword(s):  
At Risk ◽  
Covariance Matrix ◽  
Cash Flow ◽  
Value At Risk ◽  
Factor Model ◽  
Factor Models ◽  
Portfolio Approach ◽  
Normally Distributed

One way to calculate Value-at-Risk (VaR) is the variation-covariance method. The calculation of VaR covariance assumes stock data is normally distributed. The data needed to calculate VaR by the variance-covariance method is the covariance matrix of Bank Danamon and Bank Mandiri stock data. The main topics discussed in this paper are calculating VaR covariance with a simple cash portfolio approach, factor models and cash flow. For comparison of the use of the three approaches Backtesting, the backtest results indicate that the factor model is the best method.  

scholarly journals Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

2021 ◽  
Vol 14 (11) ◽  
pp. 540
Author(s):  
Eyden Samunderu ◽  
Yvonne T. Murahwa
Keyword(s):  
At Risk ◽  
Normal Distribution ◽  
Hedge Funds ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Extensive Literature ◽  
Data Set ◽  
Normally Distributed

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

scholarly journals Evaluating covariance matrix forecasts in a value-at-risk framework

2001 ◽  
Vol 3 (3) ◽  
pp. 69-97 ◽  
Author(s):  
Jose Lopez ◽  
Christian Walter
Keyword(s):  
At Risk ◽  
Covariance Matrix ◽  
Value At Risk ◽  
A Value ◽  
Risk Framework

Factor Models

2017 ◽  
Author(s):  
Kerry E. Back
Keyword(s):  
Performance Evaluation ◽  
Covariance Matrix ◽  
Factor Model ◽  
Factor Models ◽  
Arbitrage Pricing Theory ◽  
Excess Returns ◽  
Pricing Factors ◽  
Pricing Theory ◽  
The Mean ◽  
Mean Variance

The CAPM and factor models in general are explained. Factors can be replaced by the returns or excess returns that are maximally correlated (the projections of the factors). A factor model is equivalent to an affine representation of an SDF and to spanning a return on the mean‐variance frontier. The use of alphas for performance evaluation is explained. Statistical factor models are defined as models in which factors explain the covariance matrix of returns. A proof is given of the Arbitrage Pricing Theory, which states that statistical factors are approximate pricing factors. The CAPM and the Fama‐French‐Carhart model are evaluated relative to portfolios based on sorts on size, book‐to‐market, and momentum.

scholarly journals VALUE-AT-RISK AND EXPECTED SHORTFALL FOR LINEAR PORTFOLIOS WITH ELLIPTICALLY DISTRIBUTED RISK FACTORS

2005 ◽  
Vol 08 (05) ◽  
pp. 537-551 ◽  
Author(s):  
JULES SADEFO KAMDEM
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Value At Risk ◽  
Expected Shortfall ◽  
Multivariate T Distribution ◽  
T Distribution ◽  
Multivariate T ◽  
Normally Distributed

In this paper, we generalize the parametric Δ-VaR method from portfolios with normally distributed risk factors to portfolios with elliptically distributed ones. We treat both the expected shortfall and the Value-at-Risk of such portfolios. Special attention is given to the particular case of a multivariate t-distribution.

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