EconPapers    
Economics at your fingertips  
 

Bootstrapped nonlinear impulse-response analysis: the FTSE100 (UK) and the NDX100 (US) indices 2012-2021

Per Bjarte Solibakke

International Journal of Computational Economics and Econometrics, 2022, vol. 12, issue 1/2, 197-221

Abstract: This paper presents bootstrapped nonlinear impulse response function analyses for general step ahead mean and volatility densities. From strictly (ergodic and) stationary series and BIC optimal nonlinear model coefficients, the paper establishes step-ahead densities for both the conditional mean and volatility. For sampling variances using one thousand samples and conditioning all paths on the daily impulses -5, -3, ..., 5% all mean and volatility responses show mean reversion. For the volatility, all increases seem to arise from negative index movements suggesting strong asymmetry. Furthermore, the model coefficients for the volatility exhibit data dependence suggesting ability to predict volatility. The indices report some striking step-ahead differences for both the mean and the volatility. For the mean, only the NDX100 seems to show overreactions. For the volatility, for both positive and negative impulses the NDX100 reports higher volatility responses then FTSE100. However, asymmetry manifested for both indices suggesting that trading volatility as an asset may insure against market crashes and be an excellent diversification instrument. Finally, using a stochastic volatility model to obtain calibrated functions that give step-ahead predicted values for static predictions, enriches participants' derivative trading strategies (i.e., volatility swaps).

Keywords: bootstrapping; conditional heteroscedasticity; equity markets; impulse-response functions; nonlinearity; volatility predictions. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=120531 (text/html)
Open Access

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:ids:ijcome:v:12:y:2022:i:1/2:p:197-221

Access Statistics for this article

More articles in International Journal of Computational Economics and Econometrics from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().

 
Page updated 2025-03-19
Handle: RePEc:ids:ijcome:v:12:y:2022:i:1/2:p:197-221