The Forecasting Performance of Corridor Implied Volatility in the Italian Market

Silvia Muzzioli ()

Computational Economics, 2013, vol. 41, issue 3, 359-386

Abstract: Corridor implied volatility introduced in Carr and Madan (Volatility: new estimation techniques for pricing derivatives, 1998 ) and recently implemented in Andersen and Bondarenko (Volatility as an asset class, 2007 ) is obtained from model-free implied volatility by truncating the integration domain between two barriers. Corridor implied volatility is implicitly linked with the concept that the tails of the risk-neutral distribution are estimated with less precision than central values, due to the lack of liquid options for very high and very low strikes. However, there is no golden choice for the barrier levels, which are likely to change depending on the underlying asset risk neutral distribution. The latter feature renders its forecasting performance mainly an empirical question. The aim of the paper is to investigate the forecasting performance of corridor implied volatility by choosing different corridors with symmetric and asymmetric cuts, and compare the results with the preliminary findings in Muzzioli (CEFIN working paper no 23, 2010b ). Moreover, we shed light on the information content of different parts of the risk neutral distribution of the stock price, by using a model-independent approach based on corridor measures. To this end we compute both realized and model-free variance measures accounting for both falls and increases in the underlying asset price. The forecasting performance of volatility measures is evaluated both in a statistical and an economic setting. The economic significance is assessed by employing trading strategies based on delta-neutral straddles. The comparison is pursued by using intra-day synchronous prices between the options and the underlying asset. Copyright Springer Science+Business Media New York 2013

Keywords: Corridor implied volatility; Model-free implied volatility; Variance swap; Corridor variance swap; G13; G14 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10614-012-9343-x (text/html)
Access to full text is restricted to subscribers.

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:kap:compec:v:41:y:2013:i:3:p:359-386

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-012-9343-x

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().