Abstract
Article history: Received October 15, 2012 Received in revised format 4 December 2012 Accepted 8 December 2012 Available online December 9 2012 Measuring liquidity risk plays an important role on any business unit especially financial organizations. Social security systems in most countries around the world are responsible to provide necessary requirements in many countries such as health care, pension plans, etc. Therefore, it is necessary to reduce any risk associated with these systems as much as possible. In this paper, we study liquidity risk in Iranian social security using VaR technique. The proposed model of this paper uses historical information for a fiscal year of 2008-2011. We first divide the information of each year into two groups of first and second half and using VaR technique analyzed whether there was any trend change in these two groups. The results of our survey indicate that the mean of VaR in the second half of the year is greater than the first half of the year. Therefore, we can confirm that VaR maintains an increasing trend over the time horizon. We also study the trend in liquidity using regression analysis for each year, separately and the results of our survey confirm that there was an increasing trend in liquidity over time. © 2013 Growing Science Ltd. All rights reserved.
Highlights
Introductionvalue at risk (VaR) estimates under the assumption of dependency across a significant reduction of the estimation error, with AR (1)-GARCH (1,1)-GPD model
Measuring liquidity risk plays an important role on any business unit especially financial organizations
They compared value at risk (VaR) estimated under independency relatively to the VaR when dependence was considered. The efficiency of those methods was examined and compared using the backtesting tests. They reported the adequacy of the recent extensions of liquidity risk in the VaR estimation and proved a performance improvement of Corresponding author
Summary
VaR estimates under the assumption of dependency across a significant reduction of the estimation error, with AR (1)-GARCH (1,1)-GPD model. Brana et al (2012) studied the impact of global excess liquidity on commodities and asset prices for a set of emerging market countries by investigating a panel VAR framework. They defined first global liquidity and reported that excess liquidity at global level had spillover impacts on output and price levels in emerging countries. According to their investigation, the effect on real estate, commodity and share prices in emerging countries was less clear. The shape of the curves was captured by a factor structure, which is estimated nonparametrically. Chadha et al (2010) decomposed broad money into primitive demand and supply shocks and reported that supply shocks had played an important role in the time series in each of the USA, UK and Eurozone in the short to medium term
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