nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒01‒03
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Asymptotics for Time-Varying Vector MA(∞) Processes By Yayi Yan; Jiti Gao; Bin Peng
  2. Realized GARCH, CBOE VIX, and the Volatility Risk Premium By Peter Reinhard Hansen; Zhuo Huang; Chen Tong; Tianyi Wang
  3. The Fixed-b Limiting Distribution and the ERP of HAR Tests Under Nonstationarity By Alessandro Casini
  4. Long and short memory in dynamic term structure models By Salman Huseynov
  5. Option Pricing with State-dependent Pricing Kernel By Chen Tong; Peter Reinhard Hansen; Zhuo Huang
  6. Approximating Bayes in the 21st Century By Gael M. Martin; David T. Frazier; Christian P. Robert

  1. By: Yayi Yan; Jiti Gao; Bin Peng
    Abstract: Moving average infinity (MA(∞)) processes play an important role in modeling time series data. While a strand of literature on time series analysis emphasizes the importance of modeling smooth changes over time and therefore is shifting its focus from parametric models to nonparametric ones, MA(∞) processes with constant parameters are often part of the fundamental data generating mechanism. Along this line of research, an intuitive question is how to allow the underlying data generating mechanism evolves over time. To better capture the dynamics, this paper considers a new class of time-varying vector moving average infinity (VMA(∞)) processes. Accordingly, we establish some new asymptotic properties, including the law of large numbers, the uniform convergence, the central limit theory, the bootstrap consistency, and the long-run covariance matrix estimation for the class of time-varying VMA(∞) processes. Finally, we demonstrate the empirical relevance and usefulness of the newly proposed model and estimation theory through extensive simulated and real data studies.
    Keywords: multivariate time series, nonparametric kernel estimation, time-varying Beveridge–Nelson decomposition
    JEL: C14 C32 E52
    Date: 2021
  2. By: Peter Reinhard Hansen; Zhuo Huang; Chen Tong; Tianyi Wang
    Abstract: We show that the Realized GARCH model yields close-form expression for both the Volatility Index (VIX) and the volatility risk premium (VRP). The Realized GARCH model is driven by two shocks, a return shock and a volatility shock, and these are natural state variables in the stochastic discount factor (SDF). The volatility shock endows the exponentially affine SDF with a compensation for volatility risk. This leads to dissimilar dynamic properties under the physical and risk-neutral measures that can explain time-variation in the VRP. In an empirical application with the S&P 500 returns, the VIX, and the VRP, we find that the Realized GARCH model significantly outperforms conventional GARCH models.
    Date: 2021–12
  3. By: Alessandro Casini
    Abstract: We show that the nonstandard limiting distribution of HAR test statistics under fixed-b asymptotics is not pivotal (even after studentization) when the data are nonstationarity. It takes the form of a complicated function of Gaussian processes and depends on the integrated local long-run variance and on on the second moments of the relevant series (e.g., of the regressors and errors for the case of the linear regression model). Hence, existing fixed-b inference methods based on stationarity are not theoretically valid in general. The nuisance parameters entering the fixed-b limiting distribution can be consistently estimated under small-b asymptotics but only with nonparametric rate of convergence. Hence, We show that the error in rejection probability (ERP) is an order of magnitude larger than that under stationarity and is also larger than that of HAR tests based on HAC estimators under conventional asymptotics. These theoretical results reconcile with recent finite-sample evidence in Casini (2021) and Casini, Deng and Perron (2021) who showing that fixed-b HAR tests can perform poorly when the data are nonstationary. They can be conservative under the null hypothesis and have non-monotonic power under the alternative hypothesis irrespective of how large the sample size is.
    Date: 2021–11
  4. By: Salman Huseynov (Aarhus University, Department of Economics and Business Economics and CREATES)
    Abstract: I provide a unified theoretical framework for long memory term structure models and show that the recent state-space approach suffers from a parameter identification problem. I propose a different framework to estimate long memory models in a state-space setup, which addresses the shortcomings of the existing approach. The proposed framework allows asymmetrically treating the physical and risk-neutral dynamics, which simplifies estimation considerably and helps to conduct an extensive comparison with standard term structure models. Relying on a battery of tests, I find that standard term structure models perform just as well as the more complicated long memory models and produce plausible term premium estimates.
    Keywords: Dynamic term structure models, Long memory, Affine model, Shadow rate model
    JEL: C32 E43 G12
    Date: 2021–12–20
  5. By: Chen Tong; Peter Reinhard Hansen; Zhuo Huang
    Abstract: We introduce a new volatility model for option pricing that combines Markov switching with the Realized GARCH framework and leads to a novel pricing kernel with a regime-specific variance risk premium. An analytical approximation method based on an Edgeworth expansion of cumulative returns enables us to derive the pricing formula for European options in this setting. The Markov switching Realized GARCH model is easy to estimate because inferences about regimes can be deduced with realized volatility measures. In an empirical application with S&P 500 index options from 1990 to 2019, we find that investors' aversion to volatility-specific risk is time varying. The proposed framework outperforms competing methods and reduces option pricing errors by 15% or more both in-sample as well as out-of-sample.
    Date: 2021–12
  6. By: Gael M. Martin; David T. Frazier; Christian P. Robert
    Abstract: The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain `intractable' statistical problems that challenge exact methods like Markov chain Monte Carlo: for instance, models with unavailable likelihoods, high-dimensional models, and models featuring large data sets. These approximate methods are the subject of this review. The aim is to help new researchers in particular -- and more generally those interested in adopting a Bayesian approach to empirical work -- distinguish between different approximate techniques; understand the sense in which they are approximate; appreciate when and why particular methods are useful; and see the ways in which they can can be combined.
    Keywords: Approximate Bayesian inference, intractable Bayesian problems, approximate Bayesian computation, Bayesian synthetic likelihood, variational Bayes, integrated nested Laplace approximation
    Date: 2021

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