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Asset Return Volatility and Correlation Measurement, Modeling and Forecasting General Issues Diebold, F.X. and Yilmaz, K. (2004, revised 2008), "Macroeconomic Volatility and Stock Market Volatility, Worldwide," Manuscript, University of Pennsylvania and Koc University. Prepared for Robert F. Engle Festschrift. We study a broad international cross section of stock markets, and we find a clear link between macroeconomic fundamentals and stock market volatilities, with volatile fundamentals translating into volatile stock markets. Diebold, F.X. and Yilmaz, K. (2007, revised 2008), "Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets," NBER Working Paper No. 13811, forthcoming in The Economic Journal. We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of return spillovers and volatility spillovers. Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers, and both turn out to be empirically important. In particular, in an analysis of nineteen global equity markets from the early 1990s to the present, we find striking evidence of divergent behavior in the dynamics of return spillovers vs. volatility spillovers: Return spillovers display a gently increasing trend but no bursts, whereas volatility spillovers display no trend but clear bursts. Click here for weekly updates of the Diebold-Yilmaz Spillover Index, reported weekly by the Turkish Economic Research Forum. Andersen, T.G., Bollerslev, T. and Diebold, F.X. (Forthcoming), "Parametric and Nonparametric Volatility Measurement," in L.P. Hansen and Y. Ait-Sahalia (eds.), Handbook of Financial Econometrics. Amsterdam: North-Holland. All the technical rigor you (or at least I) could ever want... Andersen, T.G., Bollerslev, T., Diebold, F.X. and Wu, J. (2005), “A Framework for Exploring the Macroeconomic Determinants of Systematic Risk,” American Economic Review, 95, 398-404. We selectively survey, unify and extend the literature on realized volatility of financial asset returns. Rather than focusing exclusively on characterizing the properties of realized volatility, we progress by examining economically interesting functions of realized volatility, namely realized betas for equity portfolios, relating them both to their underlying realized variance and covariance parts and to underlying macroeconomic fundamentals. Andersen, T.G., Bollerslev, T., Christoffersen, P.F. and Diebold, F.X. (2006), "Practical Volatility and Correlation Modeling for Financial Market Risk Management," in M. Carey and R. Stulz (eds.), Risks of Financial Institutions, University of Chicago Press for NBER, 513-548. What academics have to offer market financial institution risk management practitioners. Improvements to current industry practice that are nevertheless parsimonious and easily estimated. Practical approaches to high-dimensional covariance matrix modeling, and pitfalls to avoid... Diebold, F.X. Hickman, A., Inoue, A. and Schuermann, T. (1998), "Converting 1-Day Volatility to h-Day Volatility: Scaling by Root-h is Worse than You Think," Wharton Financial Institutions Center, Working Paper 97-34. Published in condensed form as "Scale Models," Risk, 11, 104-107, 1998.
Realized Volatility Diebold, F.X. (2006), "On Market Microstructure Noise and Realized Volatility," Journal of Business and Economic Statistics, 24, 181-183. Incorporating jumps, time-varying expected returns, and intrinsic market microstructure nonlinearities when correcting realized volatility for microstructure noise. The wide-ranging predictions of economic theory for the correlation between microstructure noise and latent price. Andersen, T.G., Bollerslev, T. and Diebold, F.X. (Forthcoming), "Parametric and Nonparametric Volatility Measurement," in L.P. Hansen and Y. Ait-Sahalia (eds.), Handbook of Financial Econometrics. Amsterdam: North-Holland. All the technical rigor you (or at least I) could ever want... Andersen, T.G., Bollerslev, T., Diebold, F.X. and Wu, J. (2006), "Realized Beta: Persistence and Predictability," in T. Fomby and D. Terrell (eds.) Advances in Econometrics: Econometric Analysis of Economic and Financial Time Series in Honor of R.F. Engle and C.W.J. Granger , Volume B, 1-40. (Appendix here.) We move beyond analysis of more statistical objects like realized variances and covariances to a key financial economic object: systematic risk as captured by realized beta. Realized betas turn out to be noticeably more stable than the underlying realized variance and covariances, due to nonlinear fractional cointegration between the realized variance and covariances. Andersen, T.G., Bollerslev, T. and Diebold, F.X. (2007), "Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," Review of Economics and Statistics, 89, 701-720. Separating jump from non-jump movements in asset return volatility fluctuations. This is potentially of great value for volatility forecasting, because jump movements are likely very quickly mean reverting, whereas non-jump movements are not. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2003), "Modeling and Forecasting Realized Volatility," Econometrica, 71, 529-626. Andersen, T., Bollerslev, T., Diebold, F.X. and Ebens, H. (2001), "The Distribution of Realized Stock Return Volatility," Journal of Financial Economics, 61, 43-76. Andersen, T. Bollerslev, T., Diebold, F.X. and Labys, P. (2001), "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, 96, 42-55. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (2000), "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," Multinational Finance Journal, 4, 159-179. Andersen, T., Bollerslev, T., Diebold, F.X. and Labys, P. (1999), "(Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation," Manuscript, Northwestern University, Duke University and University of Pennsylvania. Published in revised form as "Great Realizations," Risk, March 2000, 105-108.
Range-Based Volatility Brandt, M.W. and Diebold, F.X. (2006),"A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," Journal of Business, 79, 61-74. We generalize the Alizadeh-Brandt-Diebold (2002) range-based approach to volatility to the multivariate case by exploiting no-arb conditions. Absence of triangular arbitrage in foreign exchange, for example, implies that dollar rate covariances may be expressed in terms of dollar rate volatilities and cross rate volatility, all of which may be estimated by the range, and then plugged in. Alizadeh, S., Brandt, M. and Diebold, F.X. (2002), "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, 57, 1047-1092.
GARCH-Based Volatility Diebold, F.X. (2003), "The ET Interview: Professor Robert F. Engle," Econometric Theory, 19, 1159-1193. Diebold, F.X. (1986), "Modeling the Persistence of Conditional Variances: A comment," Econometric Reviews, 5, 51-56. |