nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒07‒27
twelve papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Copula-Based Time Series With Filtered Nonstationarity By Xiaohong Chen; Zhijie Xiao; Bo Wang
  2. Locally trimmed least squares: conventional inference in possibly nonstationary models By Zhishui Hu; Ioannis Kasparis; Qiying Wang
  3. Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs By Deborah Gefang; Gary Koop; Aubrey Poon
  4. Inference in Bayesian Additive Vector Autoregressive Tree Models By Florian Huber; Luca Rossini
  5. Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic By Frank Schorfheide; Dongho Song
  6. Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency By Donggyu Kim; Xinyu Song; Yazhen Wang
  7. Large Time-Varying Volatility Models for Electricity Prices By Angelica Gianfreda; Francesco Ravazzolo; Luca Rossini
  8. Joint Bayesian Inference about Impulse Responses in VAR Models By Atsushi Inoue; Lutz Kilian
  9. Predicting the VIX and the Volatility Risk Premium: The Role of Short-run Funding Spreads Volatility Factors By Elena Andreou; Eric Ghysels
  10. Measuring Output Gap: Is It Worth Your Time? By Jiaqian Chen; Lucyna Gornicka
  11. Networks in risk spillovers: A multivariate GARCH perspective By Monica Billio; Massimiliano Caporin; Lorenzo Frattarolo; Loriana Pelizzon
  12. Cyber Attacks, Spillovers and Contagion in the Cryptocurrency Markets By Guglielmo Maria Caporale; Woo-Young Kang; Fabio Spagnolo; Nicola Spagnolo

  1. By: Xiaohong Chen (Cowles Foundation, Yale University); Zhijie Xiao (Dept. of Economics, Boston College); Bo Wang (Dept. of Economics, Boston College)
    Abstract: Economic and ï¬ nancial time series data can exhibit nonstationary and nonlinear patterns simultaneously. This paper studies copula-based time series models that capture both patterns. We propose a procedure where nonstationarity is removed via a ï¬ ltration, and then the nonlinear temporal dependence in the ï¬ ltered data is captured via a flexible Markov copula. We study the asymptotic properties of two estimators of the parametric copula dependence parameters: the parametric (two-step) copula estimator where the marginal distribution of the ï¬ ltered series is estimated parametrically; and the semiparametric (two-step) copula estimator where the marginal distribution is estimated via a rescaled empirical distribution of the ï¬ ltered series. We show that the limiting distribution of the parametric copula estimator depends on the nonstationary ï¬ ltration and the parametric marginal distribution estimation, and may be non-normal. Surprisingly, the limiting distribution of the semiparametric copula estimator using the ï¬ ltered data is shown to be the same as that without nonstationary ï¬ ltration, which is normal and free of marginal distribution speciï¬ cation. The simple and robust properties of the semiparametric copula estimators extend to models with misspeciï¬ ed copulas, and facilitate statistical inferences, such as hypothesis testing and model selection tests, on semiparametric copula-based dynamic models in the presence of nonstationarity. Monte Carlo studies and real data applications are presented.
    Keywords: Residual copula, Cointegration, Unit Root, Nonstationarity, Nonlinearity, Tail Dependence, Semiparametric
    JEL: C14 C22
    Date: 2020–07
  2. By: Zhishui Hu; Ioannis Kasparis; Qiying Wang
    Abstract: A novel IV estimation method, that we term Locally Trimmed LS (LTLS), is developed which yields estimators with (mixed) Gaussian limit distributions in situations where the data may be weakly or strongly persistent. In particular, we allow for nonlinear predictive type of regressions where the regressor can be stationary short/long memory as well as nonstationary long memory process or a nearly integrated array. The resultant t-tests have conventional limit distributions (i.e. N(0; 1)) free of (near to unity and long memory) nuisance parameters. In the case where the regressor is a fractional process, no preliminary estimator for the memory parameter is required. Therefore, the practitioner can conduct inference while being agnostic about the exact dependence structure in the data. The LTLS estimator is obtained by applying certain chronological trimming to the OLS instrument via the utilisation of appropriate kernel functions of time trend variables. The finite sample performance of LTLS based t-tests is investigated with the aid of a simulation experiment. An empirical application to the predictability of stock returns is also provided.
    Date: 2020–06
  3. By: Deborah Gefang; Gary Koop; Aubrey Poon
    Abstract: Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency estimates or nowcasts of variables for which data is available at a low frequency. Bayesian methods are commonly used with MF-VARs to overcome overparameterization concerns. But Bayesian methods typically rely on computationally demanding Markov Chain Monte Carlo (MCMC) methods. In this paper, we develop Variational Bayes (VB) methods for use with MF-VARs using Dirichlet-Laplace global-local shrinkage priors. We show that these methods are accurate and computationally much more efficient than MCMC in two empirical applications involving large MF-VARs.
    Keywords: Mixed Frequency, Variational inference, Vector Autoregression, Stochastic Volatility, Hierarchical Prior, Forecasting
    JEL: C11 C32 C53
    Date: 2020–05
  4. By: Florian Huber; Luca Rossini
    Abstract: Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This linearity assumption might be overly restrictive and could have a deleterious impact on forecasting accuracy. As a solution, we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting Bayesian additive vector autoregressive tree (BAVART) model is capable of capturing arbitrary non-linear relations between the endogenous variables and the covariates without much input from the researcher. Since controlling for heteroscedasticity is key for producing precise density forecasts, our model allows for stochastic volatility in the errors. Using synthetic and real data, we demonstrate the advantages of our methods. For Eurozone data, we show that our nonparametric approach improves upon commonly used forecasting models and that it produces impulse responses to an uncertainty shock that are consistent with established findings in the literature.
    Date: 2020–06
  5. By: Frank Schorfheide; Dongho Song
    Abstract: In this paper we resuscitate the mixed-frequency vector autoregression (MF-VAR) developed in Schorfheide and Song (2015) to generate real-time macroeconomic forecasts for the U.S. during the COVID-19 pandemic. The model combines eleven time series observed at two frequencies: quarterly and monthly. We deliberately do not modify the model specification in view of the recession induced by the COVID-19 outbreak. We find that forecasts based on a pre-crisis estimate of the VAR using data up until the end of 2019 appear to be more stable and reasonable than forecasts based on a sequence of recursive estimates that include the most recent observations. Overall, the MF-VAR outlook is quite pessimistic. The estimated MF-VAR implies that level variables are highly persistent, which means that the COVID-19 shock generates a long-lasting reduction in real activity. Regularly updated forecasts are available at
    Keywords: Bayesian inference; COVID-19; Macroeconomic Forecasting; Minnesota Prior
    JEL: C11 C32 C53
    Date: 2020–07–08
  6. By: Donggyu Kim; Xinyu Song; Yazhen Wang
    Abstract: This paper introduces unified models for high-dimensional factor-based Ito process, which can accommodate both continuous-time Ito diffusion and discrete-time stochastic volatility (SV) models by embedding the discrete SV model in the continuous instantaneous factor volatility process. We call it the SV-Ito model. Based on the series of daily integrated factor volatility matrix estimators, we propose quasi-maximum likelihood and least squares estimation methods. Their asymptotic properties are established. We apply the proposed method to predict future vast volatility matrix whose asymptotic behaviors are studied. A simulation study is conducted to check the finite sample performance of the proposed estimation and prediction method. An empirical analysis is carried out to demonstrate the advantage of the SV-Ito model in volatility prediction and portfolio allocation problems.
    Date: 2020–06
  7. By: Angelica Gianfreda; Francesco Ravazzolo; Luca Rossini
    Abstract: We study the importance of time-varying volatility in modelling hourly electricity prices when fundamental drivers are included in the estimation. This allows us to contribute to the literature of large Bayesian VARs by using well-known time series models in a huge dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exploit the importance of both Gaussian and non-Gaussian error terms in stochastic volatility. We find that by using regressors as fuels prices, forecasted demand and forecasted renewable energy is essential in order to properly capture the volatility of these prices. Moreover, we show that the time-varying volatility models outperform the constant volatility models in both the in-sample model- fit and the out-of-sample forecasting performance.
    Keywords: Electricity, Hourly Prices, Renewable Energy Sources, Non-Gaussian, Stochastic-Volatility, Forecasting
    Date: 2020–07
  8. By: Atsushi Inoue; Lutz Kilian
    Abstract: Structural VAR models are routinely estimated by Bayesian methods. Several recent studies have voiced concerns about the common use of posterior median (or mean) response functions in applied VAR analysis. In this paper, we show that these response functions can be misleading because in empirically relevant settings there need not exist a posterior draw for the impulse response function that matches the posterior median or mean response function, even as the number of posterior draws approaches infinity. As a result, the use of these summary statistics may distort the shape of the impulse response function which is of foremost interest in applied work. The same concern applies to error bands based on the upper and lower quantiles of the marginal posterior distributions of the impulse responses. In addition, these error bands fail to capture the full uncertainty about the estimates of the structural impulse responses. In response to these concerns, we propose new estimators of impulse response functions under quadratic loss, under absolute loss and under Dirac delta loss that are consistent with Bayesian statistical decision theory, that are optimal in the relevant sense, that respect the dynamics of the impulse response functions and that are easy to implement. We also propose joint credible sets for these estimators derived under the same loss function. Our analysis covers a much wider range of structural VAR models than previous proposals in the literature including models that combine short-run and long-run exclusion restrictions and models that combine zero restrictions, sign restrictions and narrative restrictions.
    Keywords: Loss function; joint inference; median response function; mean response function; modal model
    JEL: C22 C32 C52
    Date: 2020–07–17
  9. By: Elena Andreou; Eric Ghysels
    Abstract: This paper presents an innovative approach to extract Volatility Factors which predict the VIX, the S&P500 Realized Volatility (RV) and the Variance Risk Premium (VRP). The approach is innovative along two different dimensions, namely: (1) we extract Volatility Factors from panels of filtered volatilities - in particular large panels of univariate ARCH-type models and propose methods to estimate common Volatility Factors in the presence of estimation error and (2) we price equity volatility risk using factors which go beyond the equity class namely Volatility Factors extracted from panels of volatilities of short-run funding spreads. The role of these Volatility Factors is compared with the corresponding factors extracted from the panels of the above spreads as well as related factors proposed in the literature. Our monthly short-run funding spreads Volatility Factors provide both in- and out-of-sample predictive gains for forecasting the monthly VIX, RV as well as the equity premium, while the corresponding daily volatility factors via Mixed Data Sampling (MIDAS) models provide further improvements.
    Keywords: Factor asset pricing models; Volatility Factors; ARCH filters
    JEL: C2 C5 G1
    Date: 2020–03
  10. By: Jiaqian Chen; Lucyna Gornicka
    Abstract: We apply a range of models to the U.K. data to obtain estimates of the output gap. A structural VAR with an appropriate identification strategy provides improved estimates of output gap with better real time properties and lower sensitivity to temporary shocks than the usual filtering techniques. It also produces smaller out-of-sample forecast errors for inflation. At the same time, however, our results suggest caution in basing policy decisions on output gap estimates.
    Keywords: Potential output;Supply and demand;Economic theory;Real interest rates;Business cycles;output gaps,real time estimation,business cycles.,WP,output gap,supply shock,filter method,type of shock,GFC
    Date: 2020–02–07
  11. By: Monica Billio (Department of Economics, University Of Venice Cà Foscari); Massimiliano Caporin (Department of Statistical Sciences, University Of Padua); Lorenzo Frattarolo (Department of Economics, University Of Venice Cà Foscari); Loriana Pelizzon (SAFE-Goethe University Frankfurt (Germany); Department of Economics, University Of Venice Cà Foscari)
    Abstract: We propose a spatiotemporal approach for modeling risk spillovers using time-varying proximity matrices based on observable financial networks and introduce a new bilateral Multivariate GARCH speci_cation. We study covariance stationarity and identification of the model, develop the quasi-maximum-likelihood estimator and analyze its consistency and asymptotic normality. We show how to isolate risk channels and we discuss how to compute target exposure able to reduce system variance. An empirical analysis on Euroarea bond data shows that Italy and Ireland are key players in spreading risk, France and Portugal are major risk receivers, and we uncover Spain's non-trivial role as risk middleman.
    Keywords: Spatial GARCH, network, risk spillover, financial spillover
    JEL: C58 G10
    Date: 2020
  12. By: Guglielmo Maria Caporale; Woo-Young Kang; Fabio Spagnolo; Nicola Spagnolo
    Abstract: This paper examines mean and volatility spillovers between three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber attacks. Specifically, trivariate GARCH-BEKK models are estimated which include suitably defined dummies corresponding to different types, targets and number per day of cyber attacks. Significant dynamic linkages (interdependence) among the three cryptocurrencies under investigation are found in most cases when cyber attacks are taken into account, Bitcoin appearing to be the dominant one. Further, Wald tests for parameter shifts during episodes of turbulence resulting from cyber attacks provide evidence that the latter affect the transmission mechanism between cryptocurrency returns and volatilities (contagion). More precisely, cyber attacks appear to strengthen cross-market linkages, thereby reducing portfolio diversification opportunities for cryptocurrency investors. Finally, the conditional correlation analysis confirms the previous findings.
    Keywords: mean and volatility spillovers, contagion, cryptocurrencies, cyber attacks
    JEL: C32 F30 G15
    Date: 2020

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