Stock market confidence and copula-based Markov models
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Date
2010-09-30
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Abstract
This paper presents a descriptive model of stock market confidence
conditional on stock market uncertainty in a first-order copula-based
Markov approach. By using monthly closing prices of the VIX as a
stock market uncertainty proxy for the United States and the copula
of Fang et al. (2000) a stable nonlinear relation between confidence
and uncertainty is derived. Based on the existence of a specific dependence
structure uncertainty-reducing policies by US institutions
which are intended to recover stock market confidence are evaluated
with respect to its efficiency. The model implication for high uncertainty
regimes is an aggressive uncertainty-reducing policy in order
to avoid sticking in an uncertainty trap. Furthermore, uncertainty
driving profit expectations force an uncertainty level which does not
correspond to high confidence and calls for regulatory actions. Additionally,
the methodological approach is appropriate for conditional
quantile forecasts and a potential tool in risk management.
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Keywords
confidence, temporal dependence, uncertainty