nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒02‒13
three papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. High-frequency realized stochastic volatility model By Watanabe, Toshiaki; Nakajima, Jouchi
  2. Penalized Model Averaging for High Dimensional Quantile Regressions By Haowen Bao; Zongwu Cai; Yuying Sun
  3. Model Averaging for Asymptotically Optimal Combined Forecasts By Yi-Ting Chen; Chu-An Liu

  1. By: Watanabe, Toshiaki; Nakajima, Jouchi
    Abstract: A new high-frequency realized stochastic volatility model is proposed. Apart from the standard daily-frequency stochastic volatility model, the high-frequency stochastic volatility model is fit to intraday returns by extensively incorporating intraday volatility patterns. The daily realized volatility calculated using intraday returns is incorporated into the high-frequency stochastic volatility model by considering the bias in the daily realized volatility caused by microstructure noise. The volatility of intraday returns is assumed to consist of the autoregressive process, the seasonal component of the intraday volatility pattern, and the announcement component responding to macroeconomic announcements. A Bayesian method via Markov chain Monte Carlo is developed for the analysis of the proposed model. The empirical analysis using the 5-minute returns of E-mini S&P 500 futures provides evidence that our high-frequency realized stochastic volatility model improves in-sample model fit and volatility forecasting over the existing models.
    Keywords: Bayesian analysis, High-frequency data, Markov chain Monte Carlo, Realized volatility, Stochastic volatility model, Volatility forecasting
    JEL: C22 C53 C58 G17
    Date: 2023–01
  2. By: Haowen Bao (School of Economics and Management, University of Chinese Academy of Sciences and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Yuying Sun (School of Economics and Management, University of Chinese Academy of Sciences and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China)
    Abstract: This paper proposes a new penalized model averaging method for high dimensional quantile regressions based on quasi-maximum likelihood estimation, which determines optimal combination weights and yields sparseness from various potential covariates simultaneously. The proposed weight choice criterion is based on the Kullback-Leibler loss with penalties, which could reduce to Mallows-type criterion for asymmetric Laplace density. Both the dimension of covariates and the number of possibly misspecified candidate models are allowed to be diverging with the sample size. The asymptotic optimality and convergence rate of the selected weights are derived, even when all candidate models are misspecified. We further extend our concern to the ultra-high dimensional scenarios and establish the corresponding asymptotic optimality. Simulation studies and empirical application to stock returns forecasting illustrate that the proposed method outperforms existing methods.
    JEL: C51 C52 C53
    Date: 2023–01
  3. By: Yi-Ting Chen (National Taiwan University); Chu-An Liu (Institute of Economics, Academia Sinica, Taipei, Taiwan)
    Abstract: We propose a model-averaging (MA) method for constructing asymptotically optimal combined forecasts. The asymptotic optimality is defined in terms of approximating an unknown conditional-mean sequence based on the local-to-zero asymptotics. Unlike existing methods, our method is designed for combining a set of forecast sequences, which is more general than combining a set of single forecasts, generated from a set of predictive regressions. This design generates essential features that are not shared by related existing methods, and the resulting asymptotically optimal weights may be consistently estimated under suitable conditions. We also assess the forecasting performance of our method using simulation data and real data.
    Keywords: : Asymptotic optimality, forecast combination, model averaging
    JEL: C18 C41 C54
    Date: 2021–06

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