![]() ![]() J R Stat Soc, Ser B 57: 289-300.īertram P, Kruse R, Sibbertsen P (2013) Fractional integration versus level shifts: the case of realized asset correlations. J Appl Econom 17: 457-477.īenjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Econom Revi 35: 1485-1521.īarndorff-Nielsen OE, Shephard N (2002) Estimating quadratic variation using realized variance. Gallen.Īudrino F, Knaus SD (2016) Lassoing the har model: A model selection perspective on realized volatility dynamics. SSRN working paper series, University of St. Econom 71: 579-625.Īng A, Bekaert G (2007) Stock return predictability: Is it there? Rev Financ Stud 20: 651-707.Īudrino F, Camponovo L (2015) Oracle properties, bias correction, and inference of the adaptive lasso for time series extremum estimators. (2003) Modeling and forecasting realized volatility. J Am Stat Associ 96: 42-55.Īndersen TG, Bollerslev T, Diebold FX, et al. (2001) The distribution of realized exchange rate volatility. J Financ Econo 61: 43-76.Īndersen TG, Bollerslev T, Diebold FX, et al. (2001) The distribution of realized stock return volatility. Pier working paper 03-025, Northwestern University -Kellogg School of Management.Īndersen TG, Bollerslev T, Diebold FX, et al. doi: 10.3934/QFE.2017.4.363Īndersen T, Bollerslev T, Diebold F (2003) Some like it smooth, and some like it rough: untangling continuous and jump components in measuring, modeling, and forecasting asset return volatility. Testing the Lag Structure of Assets' Realized Volatility Dynamics. In an application to several constituents of the S & P 500 index it is shown that (ⅰ) the optimal significant lag structure is time-varying and subject to drastic regime shifts that seem to happen across assets simultaneously (ⅱ) in many cases the relevant information for prediction is included in the first 22 lags, corroborating previous results concerning the accuracy and the diffculty of outperforming outof-sample the heterogeneous autoregressive (HAR) model and (ⅲ) some common features of the optimal lag structure can be identified across assets belonging to the same market segment or showing a similar beta with respect to the market index.Ĭitation: Francesco Audrino, Lorenzo Camponovo, Constantin Roth. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to combine e cient parameter estimation, variable selection, and valid inference for time series processes. A (conservative) test is applied to investigate the optimal lag structure for modeling realized volatility dynamics. ![]()
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