Volatility co-movements: A time-scale decomposition analysis

Abstract

In this paper, we are interested in detecting contagion from US to European stock market volatilities in the period immediately after the Lehman Brothers collapse. The analysis is based on a factor decomposition of the covariance matrix, in the time and frequency domain, using wavelets. The analysis aims to disentangle two components of volatility contagion (anticipated and unanticipated by the market). Once we focus on standardized factor loadings, the results show no evidence of contagion (from the US) in market expectations (coming from implied volatility) and evidence of unanticipated contagion (coming from the volatility risk premium) for almost any European country. Finally, the estimation of a three-factor model specification shows that a European common shock plays an important role in determining volatility co-movements mainly in the tranquil period, while in the period of financial turmoil, the US common shock is the main driver of volatility co-movements

Moment risk premia and the cross-section of stock returns in the European stock market

“…4.1. The model-free method: moment swap contracts Carr and Wu (2009) demonstrate that the payoff of a variance swap can be used to quantify the variance risk premium, 6 i.e., the amount that investors are willing to pay to hedge against peaks of variance (see Buraschi et al, 2014 andCipollini et al, 2015 for the volatility risk 6 Note that the variance risk premium is defined as the difference between the risk-neutral and the physical expectation of variance over the next month. In particular, Bekaert and Hoerova (2014) define the variance risk premium as:…”

Financial contagion in real economy: The key role of policy uncertainty

“…However, Jin and An (2016) showed significant contagion effects from the U.S. to the BRICSs' stock although the degree of stock market reactions to such shocks differs from one market to another, depending on the level of integration with the international economy. Informational spillovers are also present in Cipollini, Cascio, and Muzzioli (2015), who argued that contagion occurs because trading activity in one market creates an informational cascade in another. Most prior studies employ classical time-domain analyses, and they usually use methods that test changes in correlation coefficients: Dungey and Gajurel (2015), Fry-McKibbin, Martin, andTang (2014), Aït-Sahalia et al (2015), Flavin and Sheenan (2015) and Finta, Frijns, and Tourani-Rad (2018).…”

An Empirical Study of Volatility Spillover Worldwide

“…Important empirical contributions across various markets include Konstantinidi et al (2008), Kenourgios (2014) Gupta and Kamilla (2015), Peng and Ng (2012), Dania and Malhotra (2014), and Chen (2014). Cipollini et al (2015) study volatility co-movements using "factor decomposition of the wavelet covariance matrix. " This paper employs both lower and higher order techniques to study co-movement of volatilities across asset classes worldwide due to contagion from localized stress events.…”