Detecting multiple breaks in financial market volatility dynamics

By E. Andreou, Eric Ghysels. 2002

368 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/56e21693c8953ac6869e020ea6c380bdaa6226d9

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Citations in the corpus

Are Correlations Constant? Empirical and Theoretical Results on Popular Correlation Models in Finance (2017, positive): “dependency restrictions, the test statistic T Q is asymptotically Kolmogorov distributed 7 An alternative correlation break point methodology is the test proposed by Andreou and Ghysels (2002, 2003). The results in our paper do not change significantly under this alternative test. To conserve space,”
Testing for Structural Breaks in Variance with Additive Outliers and Measurement Errors (2006, methodology): “Andreou and Ghysels (2002) show by Monte Carlo simulation that strongly-persistent volatility may lead to important distortions in the size of several CUSUM tests.”
Recent Advances in ARCH Modelling (2007, methodology): “We first discuss conditions on the coefficients bj and the random variables εt which guarantee the existence of a stationary solution to equations (2) with finite second or fourth moments.”
Intra- and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures (2016, background): “The existence of structural breaks is a classical statistical problem which affects volatility and long-range dependence in stock returns (Andreou & Ghysels, 2002).”
Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements (2009, background): “This suggests the possibility that multiple structural breaks in the level of RV have occurred, as documented by Andreou and Ghysels (2002). It is difficult to pin down when exactly these level shifts occurred though, and they appear to be most adequately characterized as gradual changes.”
VOLATILITY AND CORRELATION FORECASTING (2006, background): “Structural breaks in volatility is considered by Andreou and Ghysels (2002), Lamoureux and Lastrapes (1990), Pastor and Stambaugh (2001), and Schwert (1989). Hamilton and Lin (1996) and Perez-Quiros and Timmermann (2000) study volatility across business cycle regimes. The connections between long-me”
Extensions of some classical methods in change point analysis (2014, background): “Andreou and Ghysels (2002) were concerned with the dynamic evolution of financial market volatilities.”
Structural breaks in time series (2013, background): “Andreou and Ghysels (2002) were concerned with the dynamic evolution of financial market volatilities.”
Testing for changes in the unconditional variance of financial time series (2004, methodology): “Andreou and Ghysels (2002), when computing the Kokoszka and Leipus (2000) test, use the VARHAC estimator of den Hann and Levin (1997) for ω 4 .”
On the Economic Sources of Stock Market Volatility (2009, neutral): “Lamoureux and Lastrapes (1990), Andreou and Ghysels (2002), Horvath, Kokoszka, and Zhang (2006), (2) the shape of the option smile see e.g. Bates (2000) and (3) the equity premium see e.”
Generalized M‐fluctuation tests for parameter instability (2007, background): “, Andreou and Ghysels 2002), economics (see e.g., Stock and Watson 1996), political and social sciences (see e.g., Piehl, Cooper, Braga, and Kennedy 2003), or biostatistics and medical statistics (see e.g., Muggeo 2003). Usually, in structural change problems it is known with respect to which quanti”
Testing for causality in variance in the presence of breaks (2004, background): “Through a distinct strand of literature, there has recently been increasing awareness that many time series experience occasional structural breaks in (unconditional) volatility; see Andreou and Ghysels (2002), Lamoureux and Lastrapes (1990), McConnell and PerezQuiros (2000), Stock and Watson (2003)”