pith. sign in

arxiv: 1707.00610 · v2 · pith:F2G6Q2PHnew · submitted 2017-06-29 · 💱 q-fin.PR · math.PR

Option Pricing under Fast-varying and Rough Stochastic Volatility

classification 💱 q-fin.PR math.PR
keywords volatilitymeanrevertingroughfastmodelsmarkovstochastic
0
0 comments X p. Extension
pith:F2G6Q2PH Add to your LaTeX paper What is a Pith Number?
\usepackage{pith}
\pithnumber{F2G6Q2PH}

Prints a linked pith:F2G6Q2PH badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more

read the original abstract

Recent empirical studies suggest that the volatilities associated with financial time series exhibit short-range correlations. This entails that the volatility process is very rough and its autocorrelation exhibits sharp decay at the origin. Another classic stylistic feature often assumed for the volatility is that it is mean reverting. In this paper it is shown that the price impact of a rapidly mean reverting rough volatility model coincides with that associated with fast mean reverting Markov stochastic volatility models. This reconciles the empirical observation of rough volatility paths with the good fit of the implied volatility surface to models of fast mean reverting Markov volatilities. Moreover, the result conforms with recent numerical results regarding rough stochastic volatility models. It extends the scope of models for which the asymptotic results of fast mean reverting Markov volatilities are valid. The paper concludes with a general discussion of fractional volatility asymptotics and their interrelation. The regimes discussed there include fast and slow volatility factors with strong or small volatility fluctuations and with the limits not commuting in general. The notion of a characteristic term structure exponent is introduced, this exponent governs the implied volatility term structure in the various asymptotic regimes.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.