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Robust nonlinear filtering of stochastic volatility in finance

Authors: Aihara, ShinIchi; Bagchi, Arunabha;

Robust nonlinear filtering of stochastic volatility in finance

Abstract

Volatility of the stock price is the key to the pricing problem of stock related derivatives in finance. Volatility appears in the diffusion term of the usual modeling of stock prices. One popular approach is to take volatility to be stochastic, and assumes that it satisfies a stochastic differential equation. Taking the stock price to be the observation, we may then pose the filtering problem of estimating the volatility on line based on the stock price data. This is an unconventional filtering problem which we solve in this paper. But even more interesting is the fact that this filtering algorithm is inherently not robust. In the rest of the paper we derive the robust form of this filter.

Country
Netherlands
Keywords

METIS-200985

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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