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Multivariate stochastic volatility with large and moderate shocks
Author(s) -
Izzeldin Marwan,
Tsionas Mike G.,
Michaelides Panayotis G.
Publication year - 2019
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12443
Subject(s) - multivariate statistics , stochastic volatility , univariate , econometrics , volatility (finance) , forward volatility , sabr volatility model , implied volatility , multivariate analysis , prior probability , economics , mathematics , statistics , bayesian probability
Summary The paper proposes a multivariate stochastic volatility model where shifts in volatility are endogenously driven by large return shocks. The model proposed generalizes the univariate stochastic volatility model of Dendramis and colleagues to a multivariate context. Allowing for multivariate dependence permits the volatility of common return factors to affect individual stock returns volatility jointly. The model is further extended to allow for endogenous thresholds that depend on covariates. Model selection priors are introduced and the new techniques are applied by using data from the FTSE100‐index.