State transformation of information spillover in asset markets and effective dynamic hedging strategies

Yu-Min Wang, Che-Chun Lin and I-Chun Tsai

International Review of Financial Analysis, 2023, vol. 89, issue C

Abstract: This paper aims to develop a strategy to effectively and dynamically hedge risk by considering regime transitions of spillover effects between assets. We take six assets (stocks, bonds, real estate, currency exchange, crude oil, and gold) that are commonly used to construct investment portfolios as examples and analyze asset price data between September 2002 and January 2022. In doing so, we aim to examine the information spillover of different asset prices in both bear and bull market environments to determine whether state transformation affects dynamic hedging effectiveness. Using Markov-Switching Factor-Augmented Vector Autoregression (MS-FAVAR), we construct a regimen-switching model using variables from finance and economic conditions as endogenous variables to define the state transformations of the information spillover. Empirical results reveal that the MS-FAVAR model highlights changes in information spillover during a financial crisis/economic recession. Using dynamic-weighted hedging portfolios constructed with different indicators, we find that hedging effectiveness and volatility vary depending on the state of information spillover between different asset markets and that bear markets significantly impacted hedging effectiveness. Results also show that the panic sentiment (the fear index) explains the probability of a bear market. It is suggested that the state transformation of information spillovers should be monitored periodically, and hedging portfolios should be dynamically adjusted (bear or bull market) with shifting fear sentiment.

Keywords: Information spillovers; Principal components analysis; MS-FAVAR model; Liquidity factor; Fear sentiment (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:89:y:2023:i:c:s1057521923002880

DOI: 10.1016/j.irfa.2023.102772

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