nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒11‒02
eleven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning By Xing Yan; Weizhong Zhang; Lin Ma; Wei Liu; Qi Wu
  2. Roughness in spot variance? A GMM approach for estimation of fractional log-normal stochastic volatility models using realized measures By Anine E. Bolko; Kim Christensen; Mikko S. Pakkanen; Bezirgen Veliyev
  3. Reconciled Estimates of Monthly GDP in the US By Koop, Gary; McIntyre, Stuart; Mitchell, James; Poon, Aubrey
  4. A New Class of Robust Observation-Driven Models By Francisco Blasques; Christian Francq; Sébastien Laurent
  5. Movements of oil prices and exchange rates in China and India: New evidence from wavelet-based, non-linear, autoregressive distributed lag estimations By Khraief, Naceur; Shahbaz, Muhammad; Kumar Mahalik, Mantu; Bhattacharya, Mita
  6. Time series models for epidemics: leading indicators, control groups and policy assessment By Andrew C. Harvey
  7. An Analysis of International Shock Transmission: A Multi-level Factor Augmented TVP GVAR Approach By Bahar Sungurtekin Hallam
  8. Hybrid Modelling Approaches for Forecasting Energy Spot Prices in EPEC market By Tahir Miriyev; Alessandro Contu; Kevin Schafers; Ion Gabriel Ion
  9. Forecasting With Factor-Augmented Quantile Autoregressions: A Model Averaging Approach By Anthoulla Phella
  10. Portfolio diversification opportunities for U.S. Islamic investors with its trading partners when the world catches a cold: A Multivariate-GARCH and wavelet approach By Lim, Siok Jin
  11. Proxy SVAR identification of monetary policy shocks: MonteCarlo evidence and insights for the US By Herwartz, Helmut; Rohloff, Hannes; Wang, Shu

  1. By: Xing Yan; Weizhong Zhang; Lin Ma; Wei Liu; Qi Wu
    Abstract: We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic and the asymmetrical heavy-tail property of financial time series. It combines the merits of a popular sequential neural network model, i.e., LSTM, with a novel parametric quantile function that we construct to represent the conditional distribution of asset returns. Our model also captures individually the serial dependences of higher moments, rather than just the volatility. Across a wide range of asset classes, the out-of-sample forecasts of conditional quantiles or VaR of our model outperform the GARCH family. Further, the proposed approach does not suffer from the issue of quantile crossing, nor does it expose to the ill-posedness comparing to the parametric probability density function approach.
    Date: 2020–10
  2. By: Anine E. Bolko (Aarhus University and CREATES); Kim Christensen (Aarhus University and CREATES); Mikko S. Pakkanen (Imperial College London and CREATES); Bezirgen Veliyev (Aarhus University and CREATES)
    Abstract: In this paper, we develop a generalized method of moments approach for joint estimation of the parameters of a fractional log-normal stochastic volatility model. We show that with an arbitrary Hurst exponent an estimator based on integrated variance is consistent. Moreover, under stronger conditions we also derive a central limit theorem. These results stand even when integrated variance is replaced with a realized measure of volatility calculated from discrete high-frequency data. However, in practice a realized estimator contains sampling error, the effect of which is to skew the fractal coefficient toward "roughness". We construct an analytical approach to control this error. In a simulation study, we demonstrate convincing small sample properties of our approach based both on integrated and realized variance over the entire memory spectrum. We show that the bias correction attenuates any systematic deviance in the estimated parameters. Our procedure is applied to empirical high-frequency data from numerous leading equity indexes. With our robust approach the Hurst index is estimated around 0.05, confirming roughness in integrated variance.
    Keywords: GMM estimation, realized variance, rough volatility, stochastic volatility
    JEL: C10 C50
    Date: 2020–10–19
  3. By: Koop, Gary (University of Strathclyde); McIntyre, Stuart (University of Strathclyde); Mitchell, James (University of Warwick); Poon, Aubrey (University of Strathclyde)
    Abstract: In the US, income and expenditure side estimates of GDP (GDPI and GDPE) measure “true” GDP with error and are available at the quarterly frequency. Methods exist for producing reconciled quarterly estimates of GDP based on GDPI and GDPE. In this paper, we extend these methods to provide reconciled historical GDP estimates at the monthly frequency from 1960. We do this using a Bayesian Mixed Frequency Vector Autoregression involving GDPE, GDPI, unobserved true GDP and monthly indicators of short-term economic activity. We illustrate how the new monthly data contribute to our historical understanding of business cycles.
    Keywords: state-space model ; vector autoregressions ; Bayesian methods ; turning points ;
    JEL: E01 E32
    Date: 2020
  4. By: Francisco Blasques (Vrije Universiteit Amsterdam); Christian Francq (University of Lille); Sébastien Laurent (Aix-Marseille University)
    Abstract: This paper introduces a new class of observation-driven models, including score models as a special case. This new class inherits and extends the basic ideas behind the development of score models and addresses a number of unsolved issues in the score literature. In particular, the new class of models (i) allows QML estimation of static parameters, (ii) allows the production of leverage effects in the presence of negative outliers, (iii) allows update asymmetry and asymmetric forecast loss functions in the presence of symmetric or skewed innovations, and (iii) achieves out-of-sample outlier robustness in the presence of sub-exponential tails. We establish the asymptotic properties of the QLE, QMLE, and MLE as well as likelihood ratio and Lagrange multiplier test statistics. The finite sample properties are studied by means of an extensive Monte Carlo study. Finally, we show the empirical relevance of this new class of models on real data.
    Date: 2020–10–21
  5. By: Khraief, Naceur; Shahbaz, Muhammad; Kumar Mahalik, Mantu; Bhattacharya, Mita
    Abstract: This paper contributes to the existing literature by investigating the impact of oil prices on real exchange rates in China and India. We employ the non-linear, autoregressive-distributed lag model advanced by Shin et al. (2014), which allows both short-run and long-run asymmetry pass-through to a variable of interest. Oil prices and exchange rates are frequently found to be noisy. In order to detect the accurate relationship between oil prices and exchange rates, the maximum overlap, discrete-wavelet transformation is used to remove noise from the original series. The dynamic relationship between the original and de-noised series is compared. Our empirical findings suggest only long-run asymmetric effects of oil prices on exchange rates for both countries; however, after time-series noise removal, the asymmetric long-run effect becomes symmetric for India. Policy implications also are included.
    Keywords: Oil price shocks, asymmetric effects, exchange rates, India, China, NARDL
    JEL: F0
    Date: 2020–10–02
  6. By: Andrew C. Harvey
    Abstract: This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. A class of univariate time series models was developed by Harvey and Kattuman (2020). Here the framework is extended to modelling the relationship between two or more series. The role of common trends is discussed, and it is shown that when there is balanced growth in the logarithms of the growth rates of the cumulated series, simple regression models can be used to forecast using leading indicators. Data on daily deaths from Covid-19 in Italy and the UK provides an example. When growth is not balanced, the model can be extended by including a stochastic trend: the viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden's soft lockdown coronavirus policy.
    Keywords: Balanced growth, Co-integration, Covid-19, Gompertz curve, Kalman filter, Stochastic trend
    JEL: C22 C32
    Date: 2020–10
  7. By: Bahar Sungurtekin Hallam
    Abstract: We develop and apply a new methodology to study the transmission mechanisms of international macroeconomic and financial shocks in the context of emerging markets. Our approach combines aspects of factor analysis and GVAR models by replacing the cross-unit averages that serve as foreign variables in the GVAR model with macroeconomic and financial factors extracted from potentially unbalanced panels of country-level data. Factors are extracted at the country, region and global levels, with a natural hierarchical structure. Furthermore, we allow for time variation in both the model parameters and shock volatility. Our key empirical findings are as follows. First, there is substantial time-variation in the responses of our chosen emerging economies to foreign financial, interest rate and macroeconomic shocks. Second, in response to tighter global financial conditions, policy rates increase in most of our chosen emerging economies, particularly after the crisis. They appear more concerned with financial stability and capital inflows, given that they increase their short term rates more at the expense of large drops in equity prices and output. Third, financial tightening in other emerging market country groups has a loosening effect on domestic financial conditions. Fourth, as we include a global financial risk factor along with the US monetary policy rate, our results suggest that the contractionary effects of US interest rate shocks are taken over by the global financial risk shock. Lastly, we find some evidence that macroeconomic interdependencies among emerging economies have been increasing while their dependencies on advanced economies have been decreasing over time.
    Keywords: Time-varying parameter GVAR model, Factor analysis, Dual Kalman filter, Transmission channels of external shocks, Monetary policy
    JEL: C30 C32 C38 E44 F41
    Date: 2020
  8. By: Tahir Miriyev; Alessandro Contu; Kevin Schafers; Ion Gabriel Ion
    Abstract: In this work we considered several hybrid modelling approaches for forecasting energy spot prices in EPEC market. Hybridization is performed through combining a Naive model, Fourier analysis, ARMA and GARCH models, a mean-reversion and jump-diffusion model, and Recurrent Neural Networks (RNN). Training data was given in terms of electricity prices for 2013-2014 years, and test data as a year of 2015.
    Date: 2020–10
  9. By: Anthoulla Phella
    Abstract: This paper considers forecasts of the growth and inflation distributions of the United Kingdom with factor-augmented quantile autoregressions under a model averaging framework. We investigate model combinations across models using weights that minimise the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Quantile Regression Information Criterion (QRIC) as well as the leave-one-out cross validation criterion. The unobserved factors are estimated by principal components of a large panel with N predictors over T periods under a recursive estimation scheme. We apply the aforementioned methods to the UK GDP growth and CPI inflation rate. We find that, on average, for GDP growth, in terms of coverage and final prediction error, the equal weights or the weights obtained by the AIC and BIC perform equally well but are outperformed by the QRIC and the Jackknife approach on the majority of the quantiles of interest. In contrast, the naive QAR(1) model of inflation outperforms all model averaging methodologies.
    Date: 2020–10
  10. By: Lim, Siok Jin
    Abstract: The goal of this study is to analyse the co-movements and the portfolio diversification between the Islamic index of U.S. and its top trading partners, namely Canada, China, Mexico, Japan and Germany, using Morgan Stanley Capital International (MSCI) daily returns data from January 2013 to August 2020. We employed three main techniques: multivariate-GARCH-DCC, CWT and MODWT to analyse whether these markets have any diversification opportunities. Our findings reveal that, first, we observed that the U.S. Islamic index and its trading partners showed increased integration after U.S. implemented its first China-specific tariffs in 2018 and were closely integrated during the Covid-19 pandemic in 2020. Second, CWT results show that investors would gain diversification benefits in China and Mexico under specific investment horizons. Third, the results of MODWT shows Japan Islamic index provide short term diversification opportunity and Mexico Islamic index for longer term investments.
    Keywords: Islamic stocks; trade war; Covid-19; portfolio diversification; MGARCH-DCC; Wavelets
    JEL: C58 E44 G15
    Date: 2020–10–01
  11. By: Herwartz, Helmut; Rohloff, Hannes; Wang, Shu
    Abstract: In empirical macroeconomics, proxy structural vector autoregressive models (SVARs) have become a prominent path towards detecting monetary policy (MP) shocks. However, in practice, the merits of proxy SVARs depend on the relevance and exogeneity of the instrumental information employed. Our Monte Carlo analysis sheds light on the performance of proxy SVARs under realistic scenarios of low relative signal strength attached to MP shocks and alternative assumptions on instrument accuracy. In an empirical application with US data we argue in favor of the specific informational content of instruments based on the dynamic stochastic general equilibrium model of Smets andWouters (2007). A joint assessment of the benchmark proxy SVAR and the outcomes of a structural covariance change model imply that from 1973 until 1979 monetary policy contributed on average between 2.2 and 2.4 units of inflation in the GDP deflator. For the so-called Volcker disinflation starting in 1979Q4, the benchmark structural model shows that the Fed's policy measures effectively reduced the GDP deflator within three years (i.e. by -3.06 units until 1982Q3). While the empirical analysis largely conditions ona small-dimensional trinity SVAR, the benchmark proxy SVAR shocks remain remarkably robust within a six-dimensional factor-augmented model comprising rich information from Michael McCracken's database (FRED-QD).
    Keywords: structural vector autoregression,external instruments,proxy SVAR,heteroskedasticity,monetary policy shocks
    JEL: C15 C32 C36 E47
    Date: 2020

This nep-ets issue is ©2020 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.