Analyzing the Spectrum of Asset Returns: Jump and Volatility Components in High Frequency Data
AbstractThis paper reports some of the recent developments in the econometric analysis of semimartingales estimated using high frequency financial returns. It describes a simple yet powerful methodology to decompose asset returns sampled at high frequency into their base components (continuous, small jumps, large jumps), determine the relative magnitude of the components, and analyze the finer characteristics of these components such as the degree of activity of the jumps. We incorporate to effect of market microstructure noise on the test statistics, apply the methodology to high frequency individual stock returns, transactions and quotes, stock index returns and compare the qualitative features of the estimated process for these different data and discuss the economic implications of the results.( JEL C58, G12, G13)
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Bibliographic InfoArticle provided by American Economic Association in its journal Journal of Economic Literature.
Volume (Year): 50 (2012)
Issue (Month): 4 (December)
Find related papers by JEL classification:
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
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- Deniz Erdemlioglu & Sébastien Laurent & Christopher J. Neely, 2013. "Which continuous-time model is most appropriate for exchange rates?," Working Papers 2013-024, Federal Reserve Bank of St. Louis.
- Lars Winkelmann, 2013. "Quantitative forward guidance and the predictability of monetary policy - A wavelet based jump detection approach -," SFB 649 Discussion Papers SFB649DP2013-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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