Setting the margins of Hang Seng Index Futures on different positions using an APARCH-GPD Model based on extreme value theory

Yan Chen and Wenqiang Yu

Physica A: Statistical Mechanics and its Applications, 2020, vol. 544, issue C

Abstract: An asymmetric power autoregressive conditional heteroscedasticity with generalized Pareto distribution (APARCH-GPD) model is proposed in this study to determine the optimal margin level for the Hang Seng Index futures contracts on the Hong Kong Futures Exchange. This method requires two steps. First, the APARCH model is used to measure the time-varying volatility of futures contract returns. Then, the tail distribution of the residuals from APARCH model is estimated by the GPD on the basis of the extreme value theory. Value-at-risk is finally estimated and predicted by the APARCH-GPD model, and this is compared with the APARCH-t and EWMA models by backtesting historical return series. The experimental results show that the long trading position of the Hang Seng Index futures contract undertakes more risk than the short trading position. Moreover, the APARCH-GPD model offers better one-day forecasting on both positions than the other models. The findings of this study provide important implications for making decisions on financial risk management.

Keywords: Futures margin setting; Asymmetric power ARCH; Generalized Pareto distribution; Position; Value-at-risk (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119318023
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:eee:phsmap:v:544:y:2020:i:c:s0378437119318023

DOI: 10.1016/j.physa.2019.123207

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().