Which power variation predicts volatility well?

Andersen, 2001, The distribution of exchange rate volatility, Journal of American Statistical Association, 96, 42, 10.1198/016214501750332965 Andersen, 2006, Roughing it up: including jump components in the measurement, modeling and forecasting of return volatility, Review of Economics and Statistics, 89, 701, 10.1162/rest.89.4.701 Andersen, 2003, Modeling and forecasting realized volatility, Econometrica, 71, 579, 10.1111/1468-0262.00418 Andreou, 2002, Detecting multiple breaks in financial market volatility dynamics, Journal of Applied Econometrics, 17, 579, 10.1002/jae.684 Andreou, 2009, Structural breaks in financial time series Banerjee, 2005, Modelling structural breaks, long memory and stock market volatility: an overview, Journal of Econometrics, 129, 1, 10.1016/j.jeconom.2004.09.001 Barndorff-Nielsen, 2003, Realised power variation and stochastic volatility models, Bernoulli, 9, 243, 10.3150/bj/1068128977 Barndorff-Nielsen, 2004, Power and bipower variation with stochastic volatility and jumps, Journal of Financial Econometrics, 2, 1, 10.1093/jjfinec/nbh001 Barndorff-Nielsen, 2005, Limit theorems for bipower variation in financial econometrics, Econometric Theory, 22, 677 Bollerslev, 1994 Diebold, 2001, Long memory and regime switching, Journal of Econometrics, 105, 131, 10.1016/S0304-4076(01)00073-2 Diebold, 1996 Ding, 1996, Modeling volatility persistence of speculative returns: a new approach, Journal of Econometrics, 73, 185, 10.1016/0304-4076(95)01737-2 Ding, 1993, A long memory property of stock market returns and a new model, Journal of Empirical Finance, 1, 83, 10.1016/0927-5398(93)90006-D Engle, R., Ghysels, E., and Sohn, B. (2006), “On the Economic Sources of Stock Market Volatility,” Unpublished manuscript, NYU and UNC. Forsberg, 2007, Why do absolute returns predict volatility so well, Journal of Financial Econometrics, 5, 31, 10.1093/jjfinec/nbl010 Ghysels, 2002 Ghysels, 2006, Predicting volatility: getting the most out of return data sampled at different frequencies, Journal of Econometrics, 131, 59, 10.1016/j.jeconom.2005.01.004 Gourieroux, 2001, Memory and infrequency breaks, Economics Letters, 70, 29, 10.1016/S0165-1765(00)00346-3 Granger, 2004, Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns, Journal of Empirical Finance, 11, 399, 10.1016/j.jempfin.2003.03.001 Hansen, 2005, A test for superior predictive ability, Journal of Business and Economic Statistics, 23, 365, 10.1198/073500105000000063 Hansen, 2005, A forecast comparison of volatility models: does anything beat a GARCH(1,1), Journal of Applied Econometrics, 20, 873, 10.1002/jae.800 Lopez, 2001, Evaluation of predictive accuracy of volatility models, Journal of Forecasting, 20, 87, 10.1002/1099-131X(200103)20:2<87::AID-FOR782>3.0.CO;2-7 Patton, A. (2006), “Volatility Forecast Comparison using Imperfect Volatility Proxies,” Unpublished manuscript, Oxford University. Politis, 1994, The stationary bootstrap, Journal of the American Statistical Association, 89, 1303, 10.2307/2290993 Wood, 1985, An investigation of transactions data for NYSE stocks, Journal of Finance, 40, 723, 10.2307/2327796