nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒03‒08
eleven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Inference of breakpoints in high-dimensional time series By Chen, Likai; Wang, Weining; Wu, Wei Biao
  2. Time-varying state correlations in state space models and their estimation via indirect inference By Caterina Schiavoni; Siem Jan Koopman; Franz Palm; Stephan Smeekes; Jan van den Brakel
  3. Moment tests of independent components By Dante Amengual; Gabriele Fiorentini; Enrique Sentana
  4. Robust Estimation of Integrated Volatility By Li, M. Z.; Linton, O.
  5. Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection By Fryzlewicz, Piotr
  6. Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas By Stefano Grassi; Francesco Violante
  7. State Heterogeneity Analysis of Financial Volatility Using High-Frequency Financial Data By Dohyun Chun; Donggyu Kim
  8. Overnight GARCH-It\^o Volatility Models By Donggyu Kim; Yazhen Wang
  9. Approximate Bayes factors for unit root testing By Magris Martin; Iosifidis Alexandros
  10. Cointegrated Solutions of Unit-Root VARs: An Extended Representation Theorem By Mario Faliva; Maria Grazia Zoia
  11. Tail Event Driven Factor Augmented Dynamic Model By Wang, Weining; Yu, Lining; Wang, Bingling

  1. By: Chen, Likai; Wang, Weining; Wu, Wei Biao
    Abstract: For multiple change-points detection of high-dimensional time series, we provide asymptotic theory concerning the consistency and the asymptotic distribution of the breakpoint statistics and estimated break sizes. The theory backs up a simple two- step procedure for detecting and estimating multiple change-points. The proposed two-step procedure involves the maximum of a MOSUM (moving sum) type statistics in the rst step and a CUSUM (cumulative sum) re nement step on an aggregated time series in the second step. Thus, for a xed time-point, we can capture both the biggest break across di erent coordinates and aggregating simultaneous breaks over multiple coordinates. Extending the existing high-dimensional Gaussian approximation theorem to dependent data with jumps, the theory allows us to characterize the size and power of our multiple change-point test asymptotically. Moreover, we can make inferences on the breakpoints estimates when the break sizes are small. Our theoretical setup incorporates both weak temporal and strong or weak cross-sectional dependence and is suitable for heavy-tailed innovations. A robust long-run covariance matrix estimation is proposed, which can be of independent interest. An application on detecting structural changes of the U.S. unemployment rate is considered to illustrate the usefulness of our method.
    Keywords: multiple change points detection,temporal and cross-sectional dependence,Gaussian approximation,inference of break locations
    JEL: C00
    Date: 2020
  2. By: Caterina Schiavoni (Maastricht University); Siem Jan Koopman (Vrije Universiteit Amsterdam); Franz Palm (Maastricht University); Stephan Smeekes (Maastricht University); Jan van den Brakel (Maastricht University)
    Abstract: Statistics Netherlands uses a state space model to estimate the Dutch unemployment by using monthly series about the labour force surveys (LFS). More accurate estimates of this variable can be obtained by including auxiliary information in the model, such as the univariate administrative series of claimant counts. Legislative changes and economic crises may affect the relation between survey-based and auxiliary series. This time-changing relationship is captured by a time-varying correlation parameter in the covariance matrix of the transition equation’s error terms. We treat the latter parameter as a state variable, which makes the state space model become nonlinear and therefore its estimation by Kalman filtering and maximum likelihood infeasible. We therefore propose an indirect inference approach to estimate the static parameters of the model, which employs cubic splines for the auxiliary model, and a bootstrap filter method to estimate the time-varying correlation together with the other state variables of the model. We conduct a Monte Carlo simulation study that shows that our proposed methodology is able to correctly estimate both the time-constant parameters and the state vector of the model. Empirically we find that the financial crisis of 2008 triggered a deeper and more prolonged deviation between the survey-based and the claimant counts series, than a legislative change in 2015. Promptly tackling such changes, which our proposed method does, results in more realistic real-time unemployment estimates.
    Keywords: bootstrap filter, cubic splines, indirect inference, nonlinear state space, time-varying parameter, unemployment
    JEL: J64 C22 C32
    Date: 2021–02–24
  3. By: Dante Amengual (CEMFI, Centro de Estudios Monetarios y Financieros); Gabriele Fiorentini (Università di Firenze and RCEA); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: We propose simple specification tests for independent component analysis and structural vector autoregressions with non-Gaussian shocks that check the normality of a single shock and the potential cross-sectional dependence among several of them. Our tests compare the integer (product) moments of the shocks in the sample with their population counterparts. Importantly, we explicitly consider the sampling variability resulting from using shocks computed with consistent parameter estimators. We study the finite sample size of our tests in extensive simulation exercises and discuss some bootstrap procedures. We also show that our tests have non-negligible power against a variety of empirically plausible alternatives.
    Keywords: Covariance, co-skewness, co-kurtosis, finite normal mixtures, normality tests, pseudo maximum likelihood estimators, structural vector autoregressions.
    JEL: C32 C46 C52
    Date: 2021–02
  4. By: Li, M. Z.; Linton, O.
    Abstract: We introduce a new method to estimate the integrated volatility (IV) based on noisy high-frequency data. Our method employs the ReMeDI approach introduced by Li and Linton (2021a) to estimate the moments of the microstructure noise and thereby eliminate their influence, and the pre-averaging method to target the volatility parameter. The method is robust: it can be applied when the efficient price exhibits stochastic volatility and jumps, the observation times are random and endogenous, and the noise process is nonstationary, autocorrelated and dependent on the efficient price. We derive the limit distribution for the proposed estimators under infill asymptotics in a general setting. Our simulation and empirical studies demonstrate the robustness, accuracy and computational efficiency of our estimators compared to several alternatives recently proposed in the literature.
    Date: 2021–02–24
  5. By: Fryzlewicz, Piotr
    Abstract: Many existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients: one is “Wild Binary Segmentation 2” (WBS2), a recursive algorithm for producing what we call a ‘complete’ solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing 0 , … , T- 1 change-points, where T is the data length. The other ingredient is a new model selection procedure, referred to as “Steepest Drop to Low Levels” (SDLL). The SDLL criterion acts on the WBS2 solution path, and, unlike many existing model selection procedures for change-point problems, it is not penalty-based, and only uses thresholding as a certain discrete secondary check. The resulting WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and to significantly outperform the competition in the frequent change-point scenarios tested. WBS2.SDLL is fast, easy to code and does not require the choice of a window or span parameter.
    Keywords: segmentation; break detection; jump detection; randomized algorithms; adaptive algorithms; multiscale methods; EP/ L014246/1
    JEL: C1
    Date: 2020–12–01
  6. By: Stefano Grassi (University of Rome Tor Vergata and CREATES - Aarhus University); Francesco Violante (CREST, GENES, ENSAE Paris, Institut Polytechnique de Paris and CREATES - Aarhus University)
    Abstract: Starting from the Cholesky-GARCH model, recently proposed by Darolles, Francq, and Laurent (2018), the paper introduces the Block-Cholesky GARCH (BC-GARCH). This new model adapts in a natural way to the asset pricing framework. After deriving conditions for stationarity, uniform invertibility and beta tracking, we investigate the finite sample properties of a variety of maximum likelihood estimators suited for the BC-GARCH by means of an extensive Monte Carlo experiment. We illustrate the usefulness of the BC-GARCH in two empirical applications. The first tests for the presence of beta spillovers in a bivariate system in the context of the Fama and French (1993) three factor framework. The second empirical application consists of a large scale exercise exploring the cross-sectional variation of expected returns for 40 industry portfolios.
    Keywords: Cholesky decomposition, Multivariate GARCH, Asset Pricing, Time Varying Beta, Two Pass Regression.
    JEL: C12 C22 C58 G12 G13
    Date: 2021–03–03
  7. By: Dohyun Chun; Donggyu Kim
    Abstract: Recently, to account for low-frequency market dynamics, several volatility models, employing high-frequency financial data, have been developed. However, in financial markets, we often observe that financial volatility processes depend on economic states, so they have a state heterogeneous structure. In this paper, to study state heterogeneous market dynamics based on high-frequency data, we introduce a novel volatility model based on a continuous Ito diffusion process whose intraday instantaneous volatility process evolves depending on the exogenous state variable, as well as its integrated volatility. We call it the state heterogeneous GARCH-Ito (SG-Ito) model. We suggest a quasi-likelihood estimation procedure with the realized volatility proxy and establish its asymptotic behaviors. Moreover, to test the low-frequency state heterogeneity, we develop a Wald test-type hypothesis testing procedure. The results of empirical studies suggest the existence of leverage, investor attention, market illiquidity, stock market comovement, and post-holiday effect in S&P 500 index volatility.
    Date: 2021–02
  8. By: Donggyu Kim; Yazhen Wang
    Abstract: Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics. However, because high-frequency trading data are not available during the close-to-open period, the volatility models often ignore volatility information over the close-to-open period and thus may suffer from loss of important information relevant to market dynamics. In this paper, to account for whole-day market dynamics, we propose an overnight volatility model based on It\^o diffusions to accommodate two different instantaneous volatility processes for the open-to-close and close-to-open periods. We develop a weighted least squares method to estimate model parameters for two different periods and investigate its asymptotic properties. We conduct a simulation study to check the finite sample performance of the proposed model and method. Finally, we apply the proposed approaches to real trading data.
    Date: 2021–02
  9. By: Magris Martin; Iosifidis Alexandros
    Abstract: This paper introduces a feasible and practical Bayesian method for unit root testing in financial time series. We propose a convenient approximation of the Bayes factor in terms of the Bayesian Information Criterion as a straightforward and effective strategy for testing the unit root hypothesis. Our approximate approach relies on few assumptions, is of general applicability, and preserves a satisfactory error rate. Among its advantages, it does not require the prior distribution on model's parameters to be specified. Our simulation study and empirical application on real exchange rates show great accordance between the suggested simple approach and both Bayesian and non-Bayesian alternatives.
    Date: 2021–02
  10. By: Mario Faliva; Maria Grazia Zoia
    Abstract: This paper establishes an extended representation theorem for unit-root VARs. A specific algebraic technique is devised to recover stationarity from the solution of the model in the form of a cointegrating transformation. Closed forms of the results of interest are derived for integrated processes up to the 4-th order. An extension to higher-order processes turns out to be within the reach on an induction argument.
    Date: 2021–02
  11. By: Wang, Weining; Yu, Lining; Wang, Bingling
    Abstract: A factor augmented dynamic model for analysing tail behaviour of high dimensional time series is proposed. As a first step, the tail event driven latent factors are extracted. In the second step, a VAR (Vectorautoregression model) is carried out to analyse the interaction between these factors and the macroeconomic variables. Furthermore, this methodology also provides the possibility for central banks to examine the sensitivity between macroeconomic variables and financial shocks via impulse response analysis. Then the predictability of our estimator is illustrated. Finally, forecast error variance decomposition is carried out to investigate the network effect of these variables. The interesting findings are: firstly, GDP and Unemployment rate are very much sensitive to the shock of financial tail event driven factors, while these factors are more affected by inflation and short term interest rate. Secondly, financial tail event driven factors play important roles in the network constructed by the extracted factors and the macroeconomic variables. Thirdly, there is more connectedness during financial crisis than in the stable periods. Compared with median case, the network is more dense in lower quantile level.
    Keywords: Quantile Regression,Expectile Regression,Dynamic Factor Model,Dynamic Network
    JEL: C21 C51 G01 G18 G32 G38
    Date: 2020

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