Estimating and forecasting instantaneous volatility through a duration model : An assessment based on VaR
AbstractIn order to forecast one-step ahead volatility, we calculated jump intensity by using estimated parameters of a duration model of price change. In this procedure, we do not assume any distribution on log-return. Although we do not make any distributional assumption, we may practically choose a suitable distribution e.g. Normal, student, etc, including empirical density, when we calculate a VaR (Value at Risk) with an instantaneous volatility to check the prediction performance. Furthermore, we compare the goodness of fit among assumed distributions of log-return. We find that fat tail distributions such as NIG, Laplace, are well fitted to the actual high frequency data listed on the Tokyo stock exchange 1st section from 4 Jan. 2001 to 28 June 2001
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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society 2004 Far Eastern Meetings with number 592.
Date of creation: 11 Aug 2004
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High frequency data; Duration model; Instantaneous volatility; VaR;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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- Pierre Giot, 2005. "Market risk models for intraday data," European Journal of Finance, Taylor and Francis Journals, vol. 11(4), pages 309-324.
- Luc BAUWENS & Pierre GIOT, 2000.
"The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks,"
Annales d'Economie et de Statistique,
ENSAE, issue 60, pages 117-149.
- BAUWENS, Luc & GIOT, Pierre, . "The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks," CORE Discussion Papers RP -1497, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
- GIOT, Pierre, 1999. "Time transformations, intraday data and volatility models," CORE Discussion Papers 1999044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
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