Realized covariance tick-by-tick in presence of rounded time stamps and general microstructure effects

This paper presents two classes of tick-by-tick covariance estimators adapted to the case of rounding in the price time stamps to a frequency lower than the typical arrival rate of tick prices. Through Monte Carlo simulations, we investigate the behavior of such estimators under realistic market microstructure conditions analogous to those of the financial data examined in this paper's empirical section, that is, nonsynchronous trading, general ARMA structure for microstructure noise, and true lead–lag cross-covariance. Simulation results show the robustness of the proposed tick-by-tick covariance estimators to time stamp rounding, and their overall performance is superior to competing covariance estimators under empirically realistic microstructure conditions. These results are confirmed in the empirical application where the economic benefits of the proposed estimators are evaluated with volatility timing strategies applied to a bivariate portfolio of S&P 500 futures and 30-year U.S. treasury bond futures

Testing the Lag Structure of Assets' Realized Volatility Dynamics

A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. 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 efficient parameter estimation, variable selection, and valid inference for time series processes. 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 difficulty of outperforming out-of-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

Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent

We propose a multivariate nonparametric technique for generating reliable short-term historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest rate series. It is computationally feasible in large dimensions and it can account for nonlinearities in the dependence of interest rates at all available maturities. Based on FGD we apply filtered historical simulation to compute reliable out-of-sample yield curve scenarios and confidence intervals. We back-test our methodology on daily USD bond data for forecasting horizons from 1 to 10 days. Based on several statistical performance measures we find significant evidence of a higher predictive power of our method when compared to scenarios generating techniques based on (i) factor analysis, (ii) a multivariate CCC-GARCH model, or (iii) an exponential smoothing covariances estimator as in the RiskMetrics-super-TM approach. Copyright , Oxford University Press.