Neglecting Structural Breaks when Estimating and Valuing Dynamic Correlations for Asset Allocation

Andreea Halunga, Christos Savva

Research output: Contribution to journalArticle

Abstract

This paper assesses the econometric and economic value consequences of neglecting structural breaks in dynamic correlation models and in the context of asset allocation framework. It is shown that changes in the parameters of the conditional correlation process can lead to biased estimates of persistence. Monte Carlo simulations reveal that short-run persistence is downward biased while long-run persistence is severely upward biased, leading to spurious high persistence of shocks to conditional correlation. An application to stock returns supports these results and concludes that neglecting such structural shifts could lead to misleading decisions on portfolio diversification, hedging, and risk management.
LanguageEnglish
Pages660-678
Number of pages19
JournalEconometric Reviews
Volume38
Issue number6
Early online date8 Feb 2018
DOIs
StatusPublished - 2019

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Asset allocation
Dynamic correlation
Persistence
Structural breaks
Conditional correlation
Economic value
Short-run
Risk management
Structural shift
Monte Carlo simulation
Stock returns
Portfolio diversification
Econometrics
Hedging

Cite this

Neglecting Structural Breaks when Estimating and Valuing Dynamic Correlations for Asset Allocation. / Halunga, Andreea; Savva, Christos.

In: Econometric Reviews, Vol. 38, No. 6, 2019, p. 660-678.

Research output: Contribution to journalArticle

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