Disentangling systematic and idiosyncratic dynamics in panels of volatility measures

Abstract

Realized volatilities measured on several assets exhibit a common secular trend and some idiosyncratic pattern. We accommodate such an empirical regularity extending the class of Multiplicative Error Models (MEMs) to a model where the common trend is estimated nonparametrically while the idiosyncratic dynamics are assumed to follow univariate MEMs. Estimation theory based on seminonparametric methods is developed for this class of models for large cross-sections and large time dimensions. The methodology is illustrated using two panels of realized volatility measures between 2001 and 2008: the SPDR Sectoral Indices of the S&P500 and the constituents of the S&P100. Results show that the shape of the common volatility trend captures the overall level of risk in the market and that the idiosyncratic dynamics have an heterogeneous degree of persistence around the trend. An out-of-sample forecasting exercise shows that the proposed methodology improves volatility prediction over a number of benchmark specifications.

Generalized dynamic factor models and volatilities: estimation and forecasting

“…(i) When the panel under study itself is a large panel of volatility proxies (as realized volatilities or adjusted log-ranges), a factor analysis on such panels is the common way to cope with highdimensionality issues-see Engle and Marcucci (2006), Barigozzi et al (2014), Luciani and Veredas (2015), or Ghysels (2014), for recent contributions in that context. But the question then naturally arises of how those volatility proxies have been obtained (presumably, from some unreported primitive large panel of returns).…”

NETS: Network estimation for time series

“…Indeed, an important case in which the assumption of sparsity is violated, is when the components of the panel are a function of a set of common factors (Bai, 2003;Forni, Hallin, Lippi, & Reichlin, 2000;Stock & Watson, 2002). The presence of common factors in panels of volatilities, similar to those analyzed in this paper, is documented for example in Barigozzi, Brownlees, Gallo, and Veredas (2014) and Barigozzi and Hallin (2016). 1 Note also that the dependencies we are interested in, being conditional, cannot in general be captured by adding a small number of additional sectoral factors, as proposed for example by Foerster, Sarte, and Watson (2011).…”

“…Thus with the notation used in the previous sections we have z it = y it . In a way, the volatility residuals can be thought of as the short-run idiosyncratic volatility component of a volatility component model (Barigozzi et al, 2014;Wang & Ghysels, 2015). 5 The residuals are obtained after estimating the model by least squares.…”

Examining Monetary Policy Transmission in the People's Republic of China–Structural Change Models with a Monetary Policy Index

“…In a more recent paper, Tenreyro and Thwaites (2013) also suggest that monetary policy transmission has asymmetric effects, with the authors finding a greater effect on output (and inflation) in an expansion. Dolado, Maria-Dolores, and Ruge-Murcia (2005); Peersman and Smets (2001); Aragón and Portugal (2009); and, more recently, Barigozzi et al (2014) have also investigated the topic of asymmetric monetary policy in eurozone economies. Despite these studies, across advanced economies, no real consensus has been reached in this area of research.…”