nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒06‒18
fourteen papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Mildly explosive autoregression under stationary conditional heteroskedasticity By Stelios Arvanitis; Tassos Magdalinos
  2. Modelling asymmetric conditional heteroskedasticity in financial asset returns: an extension of Nelson’s EGARCH model By Cassim, Lucius
  3. Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement By Elena Goldman; Xiangjin Shen
  4. Time series with interdependent level and second moment: statistical testing and applications with Greek external trade and simulated data By Alexandros E. Milionis; Nikolaos G. Galanopoulos
  5. Log periodogram regression of two-dimensional intrinsic stationary random fields By Yoshihiro Yajima; Yasumasa Matsuda
  6. Specification tests for non-Gaussian maximum likelihood estimators By Fiorentini, Gabriele; Sentana, Enrique
  7. Least Squares and IVX Limit Theory in Systems of Predictive Regressions with GARCH innovations By Tassos Magdalinos
  8. Bayesian vector autoregressions By Silvia Miranda Agrippino; Giovanni Ricco
  9. Robust analysis of convergence in per capita GDP in BRICS economies By Phiri, Andrew
  10. A new procedure for pre-testing the distribution properties of Stock returns By Afees A. Salisu; Ibrahim D. Raheem
  11. Latent Volatility Granger Causality and Spillovers in Renewable Energy and Crude Oil ETFs By Chia-Lin Chang; Michael McAleer; Yu-Ann Wang
  12. Investigating credit transmission mechanism in the Republic of Macedonia: evidence from Vector Error Correction Model By Milan Eliskovski
  13. Partially Adaptive Econometric Methods and the Modern Obesity Epidemic By Scott A. Carson; James B. McDonald
  14. Economic Policy Uncertainty Spillovers in Booms and Busts By Giovanni Caggiano; Efrem Castelnuovo; Juan Manuel Figueres

  1. By: Stelios Arvanitis (Athens University of Economics and Business, Greece); Tassos Magdalinos (University of Southampton, UK; Rimini Centre for Economic Analysis)
    Abstract: A limit theory is developed for mildly explosive autoregressions under stationary (weakly or strongly dependent) conditionally heteroskedastic errors. The conditional variance process is allowed to be stationary, integrable and mixingale, thus encompassing general classes of GARCH type or stochastic volatility models. No mixing conditions nor moments of higher order than 2 are assumed for the innovation process. As in Magdalinos (2012), we find that the asymptotic behaviour of the sample moments is affected by the memory of the innovation process both in the form of the limiting distribution and, in the case of long range dependence, the rate of convergence, while conditional heteroskedasticity affects only the asymptotic variance. These effects are cancelled out in least squares regression theory and thereby the Cauchy limit theory of Phillips and Magdalinos (2007a) remains invariant to a wide class of stationary conditionally heteroskedastic innovations processes.
    Keywords: Central limit theory, Explosive autoregression, Long Memory, Conditional heteroskedasticity, GARCH, mixingale, Cauchy distribution
    Date: 2018–06
  2. By: Cassim, Lucius
    Abstract: Recently, volatility modeling has been a very active and extensive research area in empirical finance and time series econometrics for both academics and practitioners. GARCH models have been the most widely used in this regard. However, GARCH models have been found to have serious limitations empirically among which includes, but not limited to; failure to take into account leverage effect in financial asset returns. As such so many models have been proposed in trying to solve the limitations of the leverage effect in GARCH models two of which are the EGARCH and the TARCH models. The EGARCH model is the most highly used model. It however has its limitations which include, but not limited to; stability conditions in general and existence of unconditional moments in particular depend on the conditional density, failure to capture leverage effect when the parameters are of the same signs, assuming independence of the innovations, lack of asymptotic theory for its estimators et cetera. This paper therefore is geared at extending/improving on the EGARCH model by taking into account the said empirical limitations. The main objective of this paper therefore is to develop a volatility model that solves the problems faced by the exponential GARCH model. Using the Quasi-maximum likelihood estimation technique coupled with martingale techniques, while relaxing the independence assumption of the innovations; the paper has shown that the proposed asymmetric volatility model not only provides strongly consistent estimators but also provides asymptotically efficient estimators
    Keywords: GARCH, TARCH, EGARCH, Quasi Maximum Likelihood Estimation, Martingale
    JEL: C58
    Date: 2018–05–05
  3. By: Elena Goldman; Xiangjin Shen
    Abstract: We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms. Based on maximum likelihood estimation of S&P 500 returns, S&P/TSX returns and Monte Carlo numerical example, we find that the proposed more general asymmetric volatility model has better fit, higher persistence of negative news, higher degree of risk aversion and significant effects of macroeconomic variables on the lowfrequency volatility component. We then apply a variety of volatility models in setting initial margin requirements for a central clearing counterparty (CCP). Finally, we show how to mitigate procyclicality of initial margins using a three-regime threshold autoregressive model.
    Keywords: Econometric and statistical models; Payment clearing and settlement systems
    JEL: E41 C31 C36
    Date: 2018
  4. By: Alexandros E. Milionis (Bank of Greece and University of the Aegean); Nikolaos G. Galanopoulos (University of Athens)
    Abstract: This work aims to fill an existing gap in the literature regarding the statistical testing for the existence and the identification of the character of time-varying second moment in its dependence on a non-constant mean level in time series. To this end a new statistical testing procedure is introduced with some considerable advantages over the existing ones. Amongst others it is argued that the existing statistical tests are insufficient and sometimes lead to biased results. Further the effect of the application of this methodology on some crucial elements of time series modelling such as outlier detection and seasonal adjustment is examined, through case studies conducted on a comparative basis using both the new methodology and an established one. The severe consequences of the improper treatment of the type of time-varying second moment dealt with in this work are evidenced and emphasized. The data set comprises time series on monthly external trade statistics for Greece. Overall, the resulting empirical evidence favours the new approach. Further supporting evidence is provided by the application of the new methodology to simulated data.
    Keywords: time series transformations; applied time series analysis; seasonal adjustment; detection of outliers; Greek external trade time series
    JEL: C15 C22 C51 F14
    Date: 2018–05
  5. By: Yoshihiro Yajima; Yasumasa Matsuda
    Abstract: We propose a new estimator for a semiparametric two-dimensional intrinsic stationary random model observed on a regular grid and derive its asymptotic properties. This random field is nonstationary and includes a fractional Brownian field, which is a Gaussian random field and is used to model many physical processes in space. First we calculate tapered bivariate discrete Fourier transforms and periodograms of data observed on a grid and next apply a log-periodogram regression, which is originally proposed to estimate a long-memory parameter of semiparametric models for time series data. We prove that for a nonstationary two-dimensional random field, the estimator is still consistent and has the limiting normal distribution as the sample size goes to infinity. We conduct a computational simulation to compare the performance of it with those of different estimators proposed by other authors.
    Date: 2018–05
  6. By: Fiorentini, Gabriele; Sentana, Enrique
    Abstract: We propose generalised DWH specification tests which simultaneously compare three or more likelihood-based estimators of conditional mean and variance parameters in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for GARCH models and in many empirically relevant macro and finance applications involving VARs and multivariate regressions. To design powerful and reliable tests, we determine the rank deficiencies of the differences between the estimators' asymptotic covariance matrices under the null of correct specification, and take into account that some parameters remain consistently estimated under the alternative of distributional misspecification. Finally, we provide finite sample results through Monte Carlo simulations.
    Keywords: Durbin-Wu-Hausman Tests; Partial Adaptivity; Semiparametric Estimators; Singular Covariance Matrices
    JEL: C12 C14 C22 C32 C52
    Date: 2018–05
  7. By: Tassos Magdalinos (University of Southampton, UK; Rimini Centre for Economic Analysis)
    Abstract: The paper examines the effect of conditional heteroskedasticity to least squares inference in stochastic regression models. We show that a regressor signal of exact order O^e_p(n^{1+\alpha}) for arbitrary \alpha > 0 is sufficient to eliminate stationary GARCH effects from the limit distributions of least squares based estimators and self-normalised test statistics. The above order dominates the O e p (n) signal of stationary regressors but is dominated by the O e p (n 2 ) signal of I(1) regressors, thereby showing that least squares invariance to GARCH effects is not an exclusively I(1) phenomenon but extends to processes with persistence degree arbitrarily close to stationarity. The theory validates standard inference for self normalised test statistics based on: (i) the OLS estimator when \alpha \in (0,1); (ii) the IVX estimator (Phillips and Magdalinos, 2009; Kostakis, Magdalinos and Stamatogiannis 2015a) when \alpha > 0, when the innovation sequence of the system is a stationary vec-GARCH process. An adjusted version of the IVX testing procedure is shown to also accommodate stationary regressors and produce standard chi-squared inference under conditional heteroskedasticity in the innovations across the full range \alpha \qeq 0.
    Keywords: Central limit theory, Conditional Heteroskedasticity, Mixed Normality, Wald test
    JEL: C22
    Date: 2018–06
  8. By: Silvia Miranda Agrippino (Bank of England); Giovanni Ricco (Observatoire français des conjonctures économiques)
    Abstract: This article reviews Bayesian inference methods for Vector Autoregression models, commonly used priors for economic and financial variables, and applications to structural analysis and forecasting.
    Keywords: Bayesian Inference; Vector autoregression models; BVAR; SVAR; Forecasting
    JEL: C30 C32 E00
    Date: 2018–05
  9. By: Phiri, Andrew
    Abstract: Whilst the issue of whether or not per capita GDP adheres to the convergence theory continues to draw increasing attention within the academic paradigm, with very little consensus having been reached in the literature thus far. Our study contributes to the literature by examining the stationarity of per capita GDP for BRICS countries using annual data collected between 1971 and 2015. Considering that our sample covers a period underlying a number of crisis and structural breaks within and amongst the BRICS countries, we rely on a robust nonlinear unit root testing procedure which captures a series of unobserved structural breaks. Our results confirm on Brazil and China being the only two BRICS economies who present the most convincing evidence of per capita GDP converging back to it’s natural equilibrium after an economic shock, whilst Russia and South Africa provide less convincing evidence of convergence dynamics in the time series and India having the weakest convergence properties.
    Keywords: Per capita GDP; Convergence; unit root tests; nonlinearities; structural breaks; BRICS Emerging economies
    JEL: C12 C13 C21 C22 C51 C52 O47
    Date: 2018–05–22
  10. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Ibrahim D. Raheem (School of Economics, University of Kent, Canterbury, UK)
    Abstract: The study offers a new procedure that helps determine the best distribution prior to modeling stock returns with GARCH-type models. Specifically, it demonstrates that pre-testing the residuals of stock returns for the best distribution can help to identify the appropriate GARCH error distribution regardless of the choice of GARCH-type model. This approach is robust to alternative data frequencies and different stock markets such as those of G7 countries
    Keywords: Stock returns; GARCH-type models; Error distributions
    JEL: C52 C53 G11 G14 G17
    Date: 2018–06
  11. By: Chia-Lin Chang (National Chung Hsing University); Michael McAleer (Asia University, University of Sydney Business School, EUR); Yu-Ann Wang (National Chung Hsing University)
    Abstract: The purpose of the paper is to examine latent volatility Granger causality for four renewable energy Exchange Traded Funds (ETFs) and crude oil ETF (USO), namely solar (TAN), wind (FAN), water (PIO), and nuclear (NLR). Data on the renewable energy and crude oil ETFs are from 18 June 2008 to 20 March 2017. From the underlying stochastic process of a vector random coefficient autoregressive (VRCAR) process for the shocks of returns, we derive Latent Volatility Granger causality from the Diagonal BEKK multivariate conditional volatility model. We follow Chang et al. (2015)’s definition of the co-volatility spillovers of shocks, which calculate the delayed effect of a returns shock in one asset on the subsequent volatility or co-volatility in another asset, and extend the effects of the co-volatility spillovers of shocks to the effects of the co-volatility spillovers of squared shocks. The empirical results show there are significant positive latent volatility Granger causality relationships between solar (TAN), wind (FAN), nuclear (NLR), and crude oil (USO) ETFs, specifically significant volatility spillovers of shocks from solar ETF on the subsequent wind ETF co-volatility with solar ETF, and wind ETF on the subsequent solar ETF co-volatility with wind ETF. Interestingly, there are significant volatility spillovers of squared shocks for the renewable energy ETFs, but not with crude oil ETFs.
    Keywords: Renewable Energy; Latent Volatility; Granger Causality; Co-volatility Spillovers; Solar; Wind; Water; Nuclear Power.
    JEL: C32 C58 G12 G15 Q42
    Date: 2018–05–25
  12. By: Milan Eliskovski (National Bank of the Republic of Macedonia)
    Abstract: Research subject of this paper is the credit transmission mechanism in the Republic of Macedonia or in other words this paper investigates the effects of the monetary signals by the National Bank of the Republic of Macedonia on banks' lending. The credit transmission is analyzed through its narrow nature or so called bank lending channel. In order to explain how the bank lending channel operates in Macedonia, two theoretical models are considered and econometrically tested. The first one is the traditional bank lending channel explained by Bernanke and Blinder model and the second one is the credit rationing model by Stiglitz and Weiss. The econometric technique employed is the vector error correction model or known as Johansen cointegration technique which is appropriate for empirical testing based on time series. The empirical results suggest that the Stiglitz and Weiss model better explains the banks' behavior in the Republic of Macedonia, that is the banking sector is risk averse and rations loans with an aim not to deteriorate its' profitability. Therefore, monetary tightening signals clearly affect the banks to restrict lending. On the other hand, the monetary expansionary signals have to be supported by favorable balance sheet structure of the banks as well as by favorable macroeconomic conditions in order to encourage lending.
    Keywords: bank lending channel, monetary transmission, credit rationing, VECM analysis
    JEL: C22 E52 E58 G21
    Date: 2018
  13. By: Scott A. Carson; James B. McDonald
    Abstract: Assumptions about explanatory variables and errors are central in regression analysis. For example, the well-known method of ordinary least squares yields consistent and efficient estimators if the underlying error terms are independently, identically, and normally distributed. Additionally, the conditional distribution of the dependent variable is symmetric. The modern obesity epidemic is a well-known health dilemma where the BMI distribution was initially positively skewed but has become more symmetric, which may affect inferences about health and public resource allocation. This study applies partially adaptive estimation methods with flexible error distributions to account for possible skewness and leptokurtosis in the distribution of BMI.
    Keywords: obesity epidemic, partially adaptive estimation, skewed generalized T distribution
    JEL: C10 D13 J13
    Date: 2018
  14. By: Giovanni Caggiano (Monash University, Australia; University of Padova, Italy; Rimini Centre for Economic Analysis); Efrem Castelnuovo (University of Melbourne, Australia; University of Padova, Italy); Juan Manuel Figueres (University of Padova, Italy)
    Abstract: We estimate a nonlinear VAR to quantify the impact of economic policy uncertainty shocks originating in the US on the Canadian unemployment rate in booms and busts. We find strong evidence in favor of asymmetric spillover effects. Unemployment in Canada is shown to react to uncertainty shocks in economic busts only. Such shocks explain about 13% of the variance of the 2-year ahead forecast error of the Canadian unemployment rate in periods of slack vs. just 2% during economic booms. Counterfactual simulations lead to the identification of a novel "economic policy uncertainty spillovers channel". According to this channel, jumps in US uncertainty foster economic policy uncertainty in Canada in the first place and, because of the latter, lead to a temporary increase in the Canadian unemployment rate. Evidence of asymmetric spillover effects due to US EPU shocks are also found for the UK economy. This evidence, which refers to a large economy having a low trade intensity with the US, supports our view that a channel other than trade could be behind our empirical results.
    Keywords: Economic Policy Uncertainty Shocks, Spillover Effects, Unemployment Dynamics, Smooth Transition Vector AutoRegressions, Recessions
    JEL: C32 E32 E52
    Date: 2018–06

This nep-ets issue is ©2018 by Jaqueson K. Galimberti. 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.