Interest-Rate Volatility in Emerging Markets

By S. Edwards, Raul Susmel. Review of Economics and Statistics, 2003

129 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/fd56a79b876eee74de1d01f7f7878e6bac7b2a82

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Citations in the corpus

Volatility transmission across markets: a Multichain Markov Switching model (2007, methodology): “These loss functions are: LSEτ = T−1 ∑T−τ t=1 [ ln(yt+τ )− ln(ŷt+τ |t) ]2 (6) LAEτ = T−1 ∑T−τ t=1 ∣ln(yt+τ )− ln(ŷt+τ |t) ∣∣ .”
Stock market cycles, financial liberalization and volatility (2003, unknown): “Edwards and Susmel (2003) investigated the time pattern of interest rate volatility in four Latin American countries, and compared them to that of Hong Kong.”
Volatility Spillovers in East Asian Financial Markets: A Mem-Based Approach (2012, methodology): “…an increase of correlations of returns across markets (Forbes & Rigobon, 2002); the multivariate GARCH literature analyzes the behavior of conditional variances and covariances, possibly inserting a Markov switching behavior to account for sudden surges in volatility (Edwards & Susmel, 2001, 2003).”
Volatility Transmission Models: A Survey (2006, unknown): “However, as suggested by Engle and González-Rivera (1991) and Bollerslev and Wooldridge (1992), the conditional normality assumption may be too restrictive, especially in financial time series.”
Regime Switching for Dynamic Correlations (2006, methodology): “The theoretical and empirical properties of this model are developed in Engle and Sheppard (2001). Regime switching has been often employed to model univariate heteroskedastic time series. The most straightforward approach is to have constant variance (and mean) in each regime, e.g. see Garcia and P”