nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒02‒19
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Testing for Volatility Co-movement in Bivariate Stochastic Volatility Models By Jinghui Chen; Masahito Kobayashi; Michael McAleer
  2. Estimation of Structural Impulse Responses: Short-Run versus Long-Run Identifying Restrictions By Helmut Lütkepohl; Anna Staszewska-Bystrova; Peter Winker
  3. Some consequences of using "measurement error shocks" when estimating time series models By Adrian Pagan
  4. partialCI: An R package for the analysis of partially cointegrated time series By Clegg, Matthew; Krauß, Christopher; Rende, Jonas
  5. Uncertain Volatility Models with Stochastic Bounds By Jean-Pierre Fouque; Ning Ning
  6. Hawkes process model with a time-dependent background rate and its application to high-frequency financial data By Takahiro Omi; Yoshito Hirata; Kazuyuki Aihara

  1. By: Jinghui Chen (Graduate School of International Social Sciences, Yokohama National University, Japan); Masahito Kobayashi (Department of Economics, Yokohama National University, Japan); Michael McAleer (National Tsing Hua University, Taiwan; Erasmus School of Economics, Erasmus University Rotterdam, and Tinbergen Institute, The Netherlands;Complutense University of Madrid, Spain)
    Abstract: The paper considers the problem of volatility co-movement, namely as to whether two nancial returns have perfectly correlated common volatility process, in the framework of multivariate stochastic volatility models and proposes a test which checks the volatility co-movement. The proposed test is a stochastic volatility version of the co-movement test proposed by Engle and Susmel (1993), who investigated whether international equity markets have volatility co-movement using the framework of the ARCH model. In empirical analysis we found that volatility co-movement exists among closelylinked stock markets and that volatility co-movement of the exchange rate markets tends to be found when the overall volatility level is low, which is contrasting to the often-cited nding in the nancial contagion literature that nancial returns have co-movement in the level during the nancial crisis.
    Keywords: Lagrange multiplier test; Volatility co-movement; Stock markets; Exchange rate Markets; Financial crisis
    JEL: C12 C58 G01 G11
    Date: 2017–02–13
  2. By: Helmut Lütkepohl; Anna Staszewska-Bystrova; Peter Winker
    Abstract: There is evidence that estimates of long-run impulse responses of structural vector autoregressive (VAR) models based on long-run identifying restrictions may not be very accurate. This finding suggests that using short-run identifying restrictions may be preferable. We compare structural VAR impulse response estimates based on long-run and short-run identifying restrictions and find that long-run identifying restrictions can result in much more precise estimates for the structural impulse responses than restrictions on the impact effects of the shocks.
    Keywords: Impulse responses, structural vector autoregressive model, longrun multipliers, short-run multipliers
    JEL: C32
    Date: 2017
  3. By: Adrian Pagan
    Abstract: In a number of time times models there are I(1) variables that appear in data sets in differenced from. This note shows that an emerging practice of assuming that observed data relates to model variables through the use of “measurement error shocks” when estimating these models can imply that there is a lack of co-integration between model and data variables, and also between data variables themselves. An analysis is provided of what the nature of the measurement error would need to be if it was desired to reproduce the same co-integration information as seen in the data. Sometimes this adjustment can be complex. It is very unlikely that measurement error can be described properly with the white noise shocks that are commonly used for measurement error.
    Date: 2017–02
  4. By: Clegg, Matthew; Krauß, Christopher; Rende, Jonas
    Abstract: Partial cointegration is a weakening of cointegration, allowing for the residual series to contain a mean-reverting and a random walk component. Analytically, the residual series is described by a partially autoregressive process. The partialCI package provides estimation, testing, and simulation routines for PCI models in state space. We illustrate the functionality with two examples: A financial application in the context of pairs trading and a macroeconomic application, i.e., the relationship between GDP and consumption. For both examples, we show that the variables are not cointegated in the classic sense, but can be modeled with partial cointegration.
    Keywords: R software,cointegration,partial cointegration,pairs trading,permanent components,transient components
    Date: 2017
  5. By: Jean-Pierre Fouque; Ning Ning
    Abstract: In this paper, we propose the uncertain volatility models with stochastic bounds. Like the regular uncertain volatility models, we know only that the true model lies in a family of progressively measurable and bounded processes, but instead of using two deterministic bounds, the uncertain volatility fluctuates between two stochastic bounds generated by its inherent stochastic volatility process. This brings better accuracy and is consistent with the observed volatility path such as for the VIX as a proxy for instance. We apply the regular perturbation analysis upon the worst case scenario price, and derive the first order approximation in the regime of slowly varying stochastic bounds. The original problem which involves solving a fully nonlinear PDE in dimension two for the worst case scenario price, is reduced to solving a nonlinear PDE in dimension one and a linear PDE with source, which gives a tremendous computational advantage. Numerical experiments show that this approximation procedure performs very well, even in the regime of moderately slow varying stochastic bounds.
    Date: 2017–02
  6. By: Takahiro Omi; Yoshito Hirata; Kazuyuki Aihara
    Abstract: A Hawkes process model with a time-varying background rate is developed for analyzing the high-frequency financial data. In our model, the logarithm of the background rate is modeled by a linear model with variable-width basis functions, and the parameters are estimated by a Bayesian method. We find that the data are explained significantly better by our model as compared to the Hawkes model with a stationary background rate, which is commonly used in the field of quantitative finance. Our model can capture not only the slow time-variation, such as in the intraday seasonality, but also the rapid one, which follows a macroeconomic news announcement. We also demonstrate that the level of the endogeneity of markets, quantified by the branching ratio of the Hawkes process, is overestimated if the time-variation is not considered.
    Date: 2017–02

This nep-ets issue is ©2017 by Yong Yin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.