Intraday Value at Risk Estimation Based on an Asymmetric Autoregressive Conditional Duration Approach

Document Type : applied

Authors

1 Assistant Prof., Finance Department, Khatam University, Tehran, Iran

2 MSc. in Finance, Khatam University, Tehran, Iran

Abstract

The most important parameter in risk evaluation by intraday value at risk (IVaR) simulation is the irregular spaced high frequency data. There are several methods for model high frequency data and in this paper we propose a method to compute IVaR using real time high frequency transaction data for 10 stock of Tehran Stock Exchange. Transactions durations are modeled by asymmetric autoregressive conditional duration (AACD) and autoregressive conditional duration (ACD) mehhods and IVaR has been calculated by Mont Carlo simulation. Research results show that IVaR calculated using AACD method outperforms. And also results of IVaR calculation shows a daily pattern in IVaR variation.
JEL: G23, G32
How to cite this paper: Pouyanfar, A., & Damerloo Abhari, A. (2017). Intraday Value at Risk Estimation Based on an Asymmetric Autoregressive Conditional Duration Approach. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 278–296. (In Persian)

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References

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Journal of Risk modeling and Financial Engineering
Volume 2, Issue 3 - Serial Number 5
September 2017
Pages 278-296

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