nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒11‒11
eleven papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Comprehensive Testing of Linearity against the Smooth Transition Autoregressive Model By Dakyung Seong; Jin Seo Cho; Timo Teräsvirta
  2. Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach By Andrea Bucci; Giulio Palomba; Eduardo Rossi
  3. Comparing long monthly Chinese and selected European temperature series using the Vector Seasonal Shifting Mean and Covariance Autoregressive model By Changli He; Jian Kang; Timo Teräsvirta; Shuhua Zhang
  4. Long monthly temperature series and the Vector Seasonal Shifting Mean and Covariance Autoregressive model By Changli He; Jian Kang; Timo Teräsvirta; Shuhua Zhang
  5. The Fourier Transform Method for Volatility Functional Inference by Asynchronous Observations By Richard Y. Chen
  6. Discriminating between GARCH models for option pricing by their ability to compute accurate VIX measures By Christophe Chorro; Fanirisoa Rahantamialisoa Hasinavonizaka Zazaravaka
  7. Residual Augmented Fourier ADF Unit Root Test By Yilanci, Veli; Aydin, Mücahit; Aydin, Mehmet
  8. Binary Conditional Forecasts By McCracken, Michael W.; McGillicuddy, Joseph; Owyang, Michael T.
  9. Forecasting Dollar Real Exchange Rates and the Role of Real Activity Factors By Sarthak Behera; Hyeongwoo Kim
  10. The population question in Zimbabwe: reliable projections from the Box – Jenkins ARIMA approach By Nyoni, Thabani
  11. Fundamental Factors Affecting The Moex Russia Index: Structural Break Detection In A Long-Term Time Series By Agata Lozinskaia; Anastasiia Saltykova

  1. By: Dakyung Seong (University of California); Jin Seo Cho (Yonsei University); Timo Teräsvirta (Aarhus University and CREATES)
    Abstract: This paper examines the null limit distribution of the quasi-likelihood ratio (QLR) statistic that tests linearity condition using the smooth transition autoregressive (STAR) model. We explicitly show that the QLR test statistic weakly converges to a functional of a Gaussian stochastic process under the null of linearity by resolving the issue of twofold identification meaning that Davies’s (1977, 1987) identification problem arises in two different ways under the null. We illustrate our theory using the exponential STAR and logistic STAR models and also conduct Monte Carlo simulations. Finally, we test for neglected nonlinearity in the German money demand, growth rates of US unemployment, and German industrial production. These empirical examples also demonstrate that the QLR test statistic complements the linearity test of the Lagrange multiplier test statistic in Teräsvirta (1994).
    Keywords: QLR test statistic, STAR model, linearity test, Gaussian process, null limit distribution, nonstandard testing problem
    JEL: C12 C18 C46 C52
    Date: 2019–11–01
  2. By: Andrea Bucci (Dipartimento di Scienze Economiche e Sociali, Universita' Politecnica delle Marche); Giulio Palomba (Dipartimento di Scienze Economiche e Sociali, Universita' Politecnica delle Marche); Eduardo Rossi (Dipartimento di Scienze Economiche ed Aziendali, University of Pavia)
    Abstract: This paper addresses the question of the relevance of macroeconomic determinants in forecasting the evolution of stock markets volatilities and co-volatilities. Our approach combines the Cholesky decomposition of the covariance matrix with the use of the Vector Logistic Smooth Transition Autoregressive Model. The model includes predetermined variables and takes into account the asymmetries in volatility process. Structural breaks and nonlinearity tests are also implemented to determine the number of regimes and to identify the transition variables. The model is applied to realized volatility of stock indices of several countries in order to evaluate the role of economic variables in predicting the future evolution of conditional covariances. Our results show that the forecast accuracy of our model is significantly de m the accuracy of the forecasts obtained via other standard approaches.
    Keywords: Multivariate realized volatility, Non-linear models, Smooth transition, Forecast evaluation, Portfolio optimization
    JEL: C32 C58 G11 G17
    Date: 2019–10
  3. By: Changli He (Coordinated Innovation Center for Computable Modeling in Management Science, Tianjin University of Finance and Economics); Jian Kang (Coordinated Innovation Center for Computable Modeling in Management Science, Tianjin University of Finance and Economics, School of Accounting and Finance, The Hong Kong Polytechnic University); Timo Teräsvirta (Aarhus University and CREATES); Shuhua Zhang (Coordinated Innovation Center for Computable Modeling in Management Science, Tianjin University of Finance and Economics)
    Abstract: The purpose of this paper is to study differences in long monthly Asian and European temperature series. The longest available Asian series are those of Beijing and Shanghai, and they are compared with the ones for St Petersburg, Dublin and Uccle that have a rather different climate. The comparison is carried out in the Vector Shifting Mean and Covariance Autoregressive model that the authors have previously used to analysed 20 long European temperature series. This model gives information about mean shifts in these five temperature series as well as (error) correlations between them. The results suggest, among other things, that warming has begun later in China than in Europe, but that the change in the summer months in both Beijing and Shanghai has been quite rapid.
    Keywords: Climate change, changing seasonality, long monthly Chinese temperature series, nonlinear model, nonlinear time series, time-varying correlation, time-varying variance, time-varying vector smooth transition autoregression
    JEL: C32 C52 Q54
    Date: 2019–12–01
  4. By: Changli He (Tianjin University of Finance and Economics); Jian Kang (Tianjin University of Finance and Economics); Timo Teräsvirta (Aarhus University and CREATES); Shuhua Zhang (Tianjin University of Finance and Economics)
    Abstract: We consider a vector version of the Shifting Seasonal Mean Autoregressive model. The model is used for describing dynamic behaviour of and contemporaneous dependence between a number of long monthly temperature series for 20 cities in Europe, extending from the second half of the 18th century until mid-2010s. The results indicate strong warming in the winter months, February excluded, and cooling followed by warming during the summer months. Error variances are mostly constant over time, but for many series there is systematic decrease between 1820 and 1850 in April. Error correlations are considered by selecting two small sets of series and modelling correlations within these sets. Some correlations do change over time, but a large majority remains constant. Not surprisingly, the correlations generally decrease with the distance between cities, but geography also plays a role.
    Keywords: Changing seasonality, nonlinear model, vector smooth transition, autoregression
    JEL: C32 C52 Q54
    Date: 2019–11–01
  5. By: Richard Y. Chen
    Abstract: We study the volatility functional inference by Fourier transforms. This spectral framework is advantageous in that it harnesses the power of harmonic analysis to handle missing data and asynchronous observations without any artificial time alignment nor data imputation. Under conditions, this spectral approach is consistent and we provide limit distributions using irregular and asynchronous observations. When observations are synchronous, the Fourier transform method for volatility functionals attains both the optimal convergence rate and the efficient bound in the sense of Le Cam and H\'ajek. Another finding is asynchronicity or missing data as a form of noise produces "interference" in the spectrum estimation and impacts on the convergence rate of volatility functional estimators. This new methodology extends previous applications of volatility functionals, including principal component analysis, generalized method of moments, continuous-time linear regression models et cetera, to high-frequency datasets of which asynchronicity is a prevailing feature.
    Date: 2019–11
  6. By: Christophe Chorro (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Fanirisoa Rahantamialisoa Hasinavonizaka Zazaravaka (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, University of Ca’ Foscari [Venice, Italy])
    Abstract: In this paper, we discuss the pricing performances of a large collection of GARCH models by questioning the global synergy between the choice of the affine/non-affine GARCH specification, the use of competing alternatives to the Gaussian distribution, the selection of an appropriate pricing kernel and the choice of different estimation strategies based on several sets of financial information. Furthermore, the study answers an important question in relation to the correlation between the performance of a pricing scheme and its ability to forecast VIX dynamics. VIX analysis clearly appears as a parsimonious first-stage filter to discard the worst GARCH option pricing models.
    Keywords: GARCH option pricing models,GARCH implied VIX,estimation strategies,non-monotonic stochastic discount factors
    Date: 2019–10
  7. By: Yilanci, Veli; Aydin, Mücahit; Aydin, Mehmet
    Abstract: This paper proposes a residual-based unit root test in the presence of smooth structural changes approximated by a Fourier function. While Fourier Augmented Dickey Fuller test that introduced by Enders and Lee (2012a) allows smooth changes of the unknown form, the Residual Augmented Least Squares procedure use additional higher moment information found in non-normal errors. The test offers a simple way to accommodate an unknown number and form of structural breaks and have good size and power properties in the case of non-normal errors.
    Keywords: Non-normal errors, Fourier Function, Unit root.
    JEL: C22 F31
    Date: 2019–11–03
  8. By: McCracken, Michael W. (Federal Reserve Bank of St. Louis); McGillicuddy, Joseph (Federal Reserve Bank of St. Louis); Owyang, Michael T. (Federal Reserve Bank of St. Louis)
    Abstract: While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous variables. In this paper, we merge elements from the literature on the construction and implementation of conditional forecasts with the literature on forecasting binary variables. We use the Qual-VAR [Dueker (2005)], whose joint VAR-probit structure allows us to form conditional forecasts of the latent variable which can then be used to form probabilistic forecasts of the binary variable. We apply the model to forecasting recessions in real-time and investigate the role of monetary and oil shocks on the likelihood of two U.S. recessions.
    Keywords: Qual-VAR; recession; monetary policy; oil shocks
    JEL: C22 C52 C53
    Date: 2019–10–01
  9. By: Sarthak Behera; Hyeongwoo Kim
    Abstract: We propose factor-based out of sample forecasting models for US dollar real exchange rates. We estimate latent common factors employing an array of data dimensionality reduction approaches that include the Principal Component Analysis, Partial Least Squares, and the LASSO for a large panel of 125 monthly frequency US macroeconomic time series data. We augment two benchmark models, a stationary autoregressive model and the random walk model, with estimated common factors to formulate out-of-sample forecasts of the real exchange rate. Empirical findings demonstrate that our factor augmented models outperform the benchmark models at longer horizons when factors are extracted from real activity variables excluding financial sector variables. Factors obtained from financial market variables overall play a limited role in forecasting. Our data-driven models tend to perform better than models with international factors that are motivated by exchange rate determination theories.
    Keywords: US Dollar Real Exchange Rate; Principal Component Analysis; Partial Least Squares; LASSO; Out-of-Sample Forecast
    JEL: C38 C53 C55 F31 G17
    Date: 2019–10
  10. By: Nyoni, Thabani
    Abstract: Employing annual time series data on total population in Zimbabwe from 1960 to 2017, we model and forecast total population over the next 3 decades using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that Zimbabwe annual total population is neither I (1) nor I (2) but for the sake of simplicity,we assume it is I (2). Based on the AIC, the study presents the ARIMA (2, 2, 2) model as the best model. The diagnostic tests further imply that the presented model is stable andacceptable. The results of the study indicate that total population in Zimbabwe will continue to increase in the next three decades. In order to enjoy the benefits of the Ahlburg (1998) and Becker et al (1999) prophecy, 2 policy prescriptions have been put forward.
    Keywords: ARIMA; forecasting; population growth; population policy; total population; Zimbabwe
    JEL: C53 Q56
    Date: 2019–09–10
  11. By: Agata Lozinskaia (National Research University Higher School of Economics); Anastasiia Saltykova (National Research University Higher School of Economics)
    Abstract: This paper studies how the influence of the fundamental factors on the Russian stock market changes retrospectively. We empirically test the impact of daily values of fundamental factors (indexes of foreign stock markets, oil price, exchange rate and interest rates in Russia and the USA) on the MOEX Russia Index over long time interval from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals the changes in the power and direction of the influence of the fundamental factors which are probably caused by the structural instability revealed earlier in Russia and other stock markets. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of the structural breaks. We find multiple breaks probably associated with dramatic falls in the stock market index, for example with the significant falls of the then MICEX index in the spring of 2006 and the global financial crisis of 2008-2009. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time.
    Keywords: Russian stock market, the MOEX Russian index, fundamental factors, structural breaks, long-term time series, rolling regression, breakpoint tests
    JEL: C22 G14 G15
    Date: 2019

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