SVI estimation of the implied volatility by Kalman filter.

SVI estimation of the implied volatility by Kalman filter.

To understand and model the dynamics of the implied volatility smile is essential for trading, pricing and risk management portfolio. We suggest a  linear Kalman filter for updating of the Stochastic Volatility Inspired (SVI) model of the volatility. From a risk management perspective we generate th...

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Bibliographic Details
Main Authors: Burnos, Sergey, Ngow, ChaSing
Format: Others
Language:English
Published: Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) 2010
Subjects:
Kalman filter
SVI model
implied volatility
Mathematical statistics
Matematisk statistik
Applied mathematics
Tillämpad matematik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-13949
Description
Summary:To understand and model the dynamics of the implied volatility smile is essential for trading, pricing and risk management portfolio. We suggest a  linear Kalman filter for updating of the Stochastic Volatility Inspired (SVI) model of the volatility. From a risk management perspective we generate the 1-day ahead forecast of profit and loss (P\&L) of option portfolios. We compare the estimation of the implied volatility using the SVI model with the cubic polynomial model. We find that the SVI Kalman filter has outperformed the  others.

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