Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

2009 ◽  
Vol 16 (5) ◽  
pp. 777-792 ◽  
Author(s):  
Georges Dionne ◽  
Pierre Duchesne ◽  
Maria Pacurar
2010 ◽  
Vol 2010 ◽  
pp. 1-26 ◽  
Author(s):  
Christian Gourieroux ◽  
Joann Jasiak

This paper presents a new nonparametric method for computing the conditional Value-at-Risk, based on a local approximation of the conditional density function in a neighborhood of a predetermined extreme value for univariate and multivariate series of portfolio returns. For illustration, the method is applied to intraday VaR estimation on portfolios of two stocks traded on the Toronto Stock Exchange. The performance of the new VaR computation method is compared to the historical simulation, variance-covariance, and J. P. Morgan methods.


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

Author(s):  
Tomáš Konderla ◽  
Václav Klepáč

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.


2016 ◽  
Vol 03 (04) ◽  
pp. 1650031 ◽  
Author(s):  
Tarek Ibrahim Eldomiaty ◽  
Mohamed Hashem Rashwan ◽  
Mohamed Bahaa El Din ◽  
Waleed Tayel

Purpose: The objective of this study is to examine the relative contribution of firm-level, industry-level and country level variables to working capital at risk. Working capital at risk is treated as the value at risk for a portfolio of firm’s current assets. As far as short-term liquidity is concerned, working capital at risk, being the maximum amount that a firm may lose at a certain confidence interval, must be the most important part that a firm’s management must focus on. Design/methodology/approach: This study empirically examines the possible associations between wide range of variables and working capital at risk. The sample firms include 143 non-financial firms listed in Egypt stock exchange. The data cover the years 2000–2014. The statistical tests include the fixed and random effects, testing for linearity versus nonlinearity. The least squares dummy variables and discriminant analysis are utilized. The working capital at risk is classified into three levels: low, medium and high. Findings: The general findings of the study show that cash conversion cycle and the leverage are the most significant determinants of working capital at risk. Both determinants have significant influence on the level of volatility of working capital throughout the three categories of working capital at risk. Originality/value: This study offers a new approach that deals with working capital as a portfolio, rather than single ratios, that firm’s management must decrease its volatility (value at risk), therefore, short-term liquidity can be improved significantly. This approach can be considered a financial engineering in terms of monitoring and managing short-term liquidity exposure.


2011 ◽  
Vol 3 (2) ◽  
pp. 93-108
Author(s):  
Rangga Handika

This paper offers a new measurement of risk, Value-at-Risk (VaR) for LQ-45 index in Indonesian Stock Exchange (ISX). Basic finance uses standard deviation in measuring and quantifying the risks. This paper uses VaR as a risk measure by using historical and analytical methods. This study uses the data containing all LQ-45 weekly data from January 1st, 2005 to December, 31st 2010. Moreover, this paper also calculates VaR of three indices (IHSG, Dow Jones, and S&P 500) for benchmarking purpose. This study finds that LQ-45 companies have VaR ranging from -5.30 to -41.05 percent with 95 percent level of confidence. It means that we can expect to suffer a minimum weekly loss between 5.30 to 41.05 percent in 5 percent probability when we invest in the LQ-45 companies stocks individually. Furthermore, this study finds that individual LQ-45 stock is riskier than indices based on VaR measure. This paper also concludes that individual LQ-45 stock tends not to follow normal distribution while index tends to follow by comparing their historical and analytical VaR calculation.


2009 ◽  
Vol 54 (183) ◽  
pp. 119-138 ◽  
Author(s):  
Milica Obadovic ◽  
Mirjana Obadovic

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.


2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Emilija Nikolić-Đorić ◽  
Dragan Đorić

This paper uses RiskMetrics, GARCH and IGARCH models to calculate daily VaR for Belgrade Stock Exchange index BELEX15 returns based on the normal and Student t innovation distribution. In the case of GARCH and IGARCH models VaR values are obtained applying Extreme Value Theory on the standardized residuals. The Kupiec's LR statistics was used to test the accuracy of risk measurement models. The main conclusions are: (1) when modelling value-at-risk it is very important to have a good model for volatility of stock returns; (2) both stationary and integrated GARCH models outperform RiskMetrics in estimating VaR; (3) although long memory volatility is present in the BELEX15 index, IGARCH models cannot outperform GARCH type models in VaR evaluations for this index.


This study offers a daily dividend computation model and extends the two conventional arithmetic and logarithmic return equations to include daily dividend. The author examines the effect of daily dividend inclusion on the daily return volatility and Value-at-Risk (VaR) of the five stocks listed in the Dhaka Stock Exchange (DSE) Limited. The research shows that in most cases the inclusion of daily dividends significantly reduces the daily volatility of returns. Also, with a few exceptions, the VaR of the remaining stocks’ return declines substantially, decreasing the maximum expected loss of return. Finally, after inclusion of a daily dividend, the author finds that a more extended holding period offers a proportionately lower VaR of the daily return.


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