nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒06‒21
three papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Change-Point Analysis of Time Series with Evolutionary Spectra By Alessandro Casini; Pierre Perron
  2. A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting By Chao Wang; Richard Gerlach
  3. Forecasting Sovereign Bond Realized Volatility Using Time-Varying Coefficients Model By Barbora Malinska

  1. By: Alessandro Casini; Pierre Perron
    Abstract: This paper develops change-point methods for the time-varying spectrum of a time series. We focus on time series with a bounded spectral density that change smoothly under the null hypothesis but exhibits change-points or becomes less smooth under the alternative. We provide a general theory for inference about the degree of smoothness of the spectral density over time. We address two local problems. The first is the detection of discontinuities (or breaks) in the spectrum at unknown dates and frequencies. The second involves abrupt yet continuous changes in the spectrum over a short time period at an unknown frequency without signifying a break. We consider estimation and minimax-optimal testing. We determine the optimal rate for the minimax distinguishable boundary, i.e., the minimum break magnitude such that we are still able to uniformly control type I and type II errors. We propose a novel procedure for the estimation of the change-points based on a wild sequential top-down algorithm and show its consistency under shrinking shifts and possibly growing number of change-points. Our method can be used across many fields and a companion program is made available in popular software packages.
    Date: 2021–06
  2. By: Chao Wang; Richard Gerlach
    Abstract: In this paper, an innovative threshold measurement equation is proposed to be employed in a Realized-GARCH framework. The proposed framework employs a nonlinear threshold regression specification to consider the leverage effect and model the contemporaneous dependence between the observed realized measures and hidden volatility. A Bayesian Markov Chain Monte Carlo method is adapted and employed for the model estimation and forecasting, with its validity assessed via a simulation study. The usefulness of the proposed measurement equation in a Realized-GARCH model has been evaluated via a comprehensive empirical study, by forecasting the 1% and 2.5% Value-at-Risk and Expected Shortfall on six market indices. The proposed framework is shown to be capable of producing competitive tail risk forecasting results, compared to the original Realized-GARCH. Especially, the proposed model is favoured during the high volatility 2008 Global Financial Crisis period.
    Date: 2021–06
  3. By: Barbora Malinska (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic)
    Abstract: This paper studies predictability of realized volatility of U.S. Treasury futures using high-frequency data for 2-year, 5-year, 10-year and 30-year tenors from 2006 to 2017. We extend heterogeneous autoregressive model by Corsi (2009) by higher-order realized moments and allow all model coefficients to be time-varying in order to explore dynamics in forecasting power of individual predictors across the term structure. We find realized kurtosis to be valuable predictor across the term structure with robust contribution also in out-of-sample analysis for the shorter tenors. Time-varying coefficient models are found to bring significant out-of-sample forecasting accuracy gain at the short end of the term structure. Further, we detect significant asymmetry in forecasting errors present for all the tenors as the constant-coefficient models were found to generate systemic under-predictions of future realized volatility.
    Keywords: Realized moments, Sovereign bonds, Volatility forecasting, High-frequency data, Time-varying coefficients
    JEL: C32 C53 G17
    Date: 2021–06

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