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Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics
Author(s) -
BarndorffNielsen Ole E.,
Shephard Neil
Publication year - 2001
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00282
Subject(s) - ornstein–uhlenbeck process , stochastic volatility , econometrics , volatility (finance) , gaussian , context (archaeology) , mathematical finance , construct (python library) , economics , stochastic process , financial econometrics , statistical physics , computer science , mathematics , financial market , finance , physics , statistics , geography , quantum mechanics , indirect finance , archaeology , programming language
Non‐Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory.