Time-Changed GARCH versus GARJI Model for Extreme Events: An Empirical Study
Lie-Jane Kao,
Po-Cheng Wu and
Cheng Few Lee
Chapter 40 in Handbook of Investment Analysis, Portfolio Management, and Financial Derivatives:In 4 Volumes, 2024, pp 1339-1356 from World Scientific Publishing Co. Pte. Ltd.
Abstract:
In literature, a GARCH-jump mixture model, namely, the GARCH-jump model with autoregressive conditional jump intensity (GARJI) model, in which two conditional independent processes, i.e., a diffusion and a compounded Poisson process are used to account for stock price movements caused by normal and extreme event news arrivals, individually, is developed by Chan and Maheu (2002, 2004) to describe the volatility clustering and leverage effect phenomenon. The resulting model is less efficient and provides only ex post filter for the probability of the occurrences of large price movements. A more informative and parsimonious model, however, the VG NGARCH model, is proposed and calibrated in this study. Being an extension of the variance-gamma model developed by Madan et al. (1998), the proposed VG NGARCH model incorporates an autoregressive structure on the conditional shape parameters, which describes the news arrival rates of different impact sizes on the price movements, and an ex ante prediction for the occurrences of large price movements is provided. The performance of the proposed VG NGARCH model is compared to the GARJI model based on daily stock prices of five component financial companies in S&P 500, namely, Bank of America, Wells Fargo, J.P. Morgan Chase, CitiGroup, and AIG, respectively, from January 3, 2006 to December 31, 2009. The goodness of fit of the VG NGARCH model and its ability to predict the probabilities of large price movements are demonstrated by comparing with the benchmark GARJI model.
Keywords: Financial Accounting; Financial Auditing; Mutual Funds; Hedge Funds; Asset Pricing; Options; Portfolio Analysis; Risk Management; Investment Analysis; Momentum Analysis; Behavior Analysis; Futures; Index Futures; CDCs; Financial Econometrics; Statistics; Financial Derivatives; Financial Accounting (search for similar items in EconPapers)
JEL-codes: G1 G11 G12 G3 M41 M42 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.worldscientific.com/doi/pdf/10.1142/9789811269943_0040 (application/pdf)
https://www.worldscientific.com/doi/abs/10.1142/9789811269943_0040 (text/html)
Ebook Access is available upon purchase.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9789811269943_0040
Ordering information: This item can be ordered from
Access Statistics for this chapter
More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().