nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒10‒19
ten papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A Simple Instrument for Proxy Vector Autoregressive Analysis By Lukas Boer; Helmut Lütkepohl
  2. A Class of Time-Varying Vector Moving Average Models: Nonparametric Kernel Estimation and Application By Yayi Yan; Jiti Gao; Bin Peng
  3. Conditional asymmetry in ARCH(8) models By Julien Royer
  4. Discovering general and sectorial trends in a large set of time series By Espasa Terrades, Antoni; Carlomagno Real, Guillermo
  5. Forecasting Short-term load using Econometrics time series model with T-student Distribution By Kasun Chandrarathna; Arman Edalati; AhmadReza Fourozan tabar
  6. Forecasting Hourly Prices in Indian Spot Electricity Market By Mukherjee, Paramita; Coondoo, Dipankor; Lahiri, Poulomi
  7. Asymmetric Volatility Effects in Risk Management: An Empirical Analysis using a Stock Index Futures By Benavides Guillermo
  8. Fractional differentiation and its use in machine learning By Janusz Gajda; Rafał Walasek
  9. Volatility transmission and volatility impulse response functions in the main and the satellite Renminbi exchange rate markets By Funke, Michael; Loermann, Julius; Tsang, Andrew
  10. Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland By Nissilä, Wilma

  1. By: Lukas Boer; Helmut Lütkepohl
    Abstract: A major challenge for proxy vector autoregressive analysis is the construction of a suitable instrument variable for identifying a shock of interest. We propose a simple proxy that can be constructed whenever the dating and sign of particular shocks are known. It is shown that the proxy can lead to impulse response estimates of the impact effects of the shock of interest that are nearly as efficient as or even more efficient than estimators based on a conventional, more sophisticated proxy.
    Keywords: GMM, heteroskedastic VAR, instrumental variable estimation, proxy VAR, structural vector autoregression
    JEL: C32
    Date: 2020
  2. By: Yayi Yan; Jiti Gao; Bin Peng
    Abstract: Multivariate dynamic time series models are widely encountered in practical studies, e.g., modelling policy transmission mechanism and measuring connectedness between economic agents. To better capture the dynamics, this paper proposes a wide class of multivariate dynamic models with time-varying coefficients, which have a general time-varying vector moving average (VMA) representation, and nest, for instance, time-varying vector autoregression (VAR), time-varying vector autoregression moving-average (VARMA), and so forth as special cases. The paper then develops a unified estimation method for the unknown quantities before an asymptotic theory for the proposed estimators is established. In the empirical study, we investigate the transmission mechanism of monetary policy using U.S. data, and uncover a fall in the volatilities of exogenous shocks. In addition, we find that (i) monetary policy shocks have less influence on inflation before and during the so-called Great Moderation, (ii) inflation is more anchored recently, and (iii) the long-run level of inflation is below, but quite close to the Federal Reserve's target of two percent after the beginning of the Great Moderation period.
    Date: 2020–10
  3. By: Julien Royer (CREST, ENSAE, Institut Polytechnique de Paris)
    Abstract: We consider an extension of ARCH(8) models to account for conditional asymmetry in the presence of high persistence. After stating existence and stationarity conditions, this paper develops the statistical inference of such models and proves the consistency and asymptotic distribution of a Quasi Maximum Likelihood estimator. Some particular specifications are studied and tests for asymmetry and GARCH validity are derived. Finally we present an application on a set of equity indice store examine the preeminence of GARCH (1,1) specifications. We find strong evidences that the short memory feature of such models is not suitable for lightly traded assets.
    Keywords: ARCH(8) models, conditional asymmetry, Quasi Maximum Likelihood Estimation
    JEL: C22 C51 C58
    Date: 2020–07–07
  4. By: Espasa Terrades, Antoni; Carlomagno Real, Guillermo
    Abstract: The objective of this research note is to extend the pairwise procedure studied by Car- lomagno and Espasa (ming) to the case of general and sectorial trends. The extension allows to discover subsets of series that share general and/or sectorial stochastic trends between a (possible large) set of time series. This could be useful to model and forecast all of the series under analysis. Our approach does not need to assume pervasiveness of the trends, nor impose special restrictions on the serial or cross-sectional idiosyncratic correlation of the series. Additionally, the asymptotic theory works both, with finite N and T ! 1, and with [T;N] ! 1. In a Monte Carlo experiment we show that the extended procedure can produce reliable results in finite samples.
    Keywords: Heteroskedasticity; Pairwise Tests; Disaggregation; Factor Models; Cointegration
    JEL: C53 C32 C22 C01
    Date: 2020–09–29
  5. By: Kasun Chandrarathna; Arman Edalati; AhmadReza Fourozan tabar
    Abstract: By significant improvements in modern electrical systems, planning for unit commitment and power dispatching of them are two big concerns between the researchers. Short-term load forecasting plays a significant role in planning and dispatching them. In recent years, numerous works have been done on Short-term load forecasting. Having an accurate model for predicting the load can be beneficial for optimizing the electrical sources and protecting energy. Several models such as Artificial Intelligence and Statistics model have been used to improve the accuracy of load forecasting. Among the statistics models, time series models show a great performance. In this paper, an Autoregressive integrated moving average (SARIMA) - generalized autoregressive conditional heteroskedasticity (GARCH) model as a powerful tool for modeling the conditional mean and volatility of time series with the T-student Distribution is used to forecast electric load in short period of time. The attained model is compared with the ARIMA model with Normal Distribution. Finally, the effectiveness of the proposed approach is validated by applying real electric load data from the Electric Reliability Council of Texas (ERCOT). KEYWORDS: Electricity load, Forecasting, Econometrics Time Series Forecasting, SARIMA
    Date: 2020–09
  6. By: Mukherjee, Paramita; Coondoo, Dipankor; Lahiri, Poulomi
    Abstract: In this paper, an attempt has been made to forecast the hourly electricity spot prices in India as this is very important for the bidders in the energy exchange for participating in the day-ahead market. Forecasting high frequency data is a challenging task. In forecasting, different variants of ARMA, ARMA-GARCH models are applied in different contexts, but no unequivocal dominance of a particular model exists. In this paper, based on hourly data for several years for all the regions in India, several variants of ARMAX models are estimated, by combining static and dynamic forecasts. Along with ARMA, intra-day, inter-day and hourly variations in prices as well as seasonalities on weekdays, holidays and festive days are incorporated. ARMAX models in this context performed quite well for forecasting horizons of hourly prices of upto 5 days. Interestingly, the ARMAX models provide reasonably good forecasts for day-ahead-market and the simple structure can be quite easily implemented. Such forecasts are not only essential for the players in the spot market, but also provides insights for policymakers as it reveals several aspects of Indian electricity market including the different dimensions of seasonality in demand.
    Keywords: Forecasting, electricity, hourly data, energy, spot price, ARMAX model, day-ahead market
    JEL: C53 Q47
    Date: 2019
  7. By: Benavides Guillermo
    Abstract: In this research paper ARCH-type models and option implied volatilities (IV) are applied in order to estimate the Value-at-Risk (VaR) of a stock index futures portfolio for several time horizons. The relevance of the asymmetries in the estimated volatility estimation is considered. The empirical analysis is performed on futures contracts of both the Standard and Poors 500 Index and the Mexican Stock Exchange. According to the results, the IV model is superior in terms of precision compared to the ARCH-type models. Under both methodologies there are relevant statistical gains when asymmetries are included. The referred gains range from 4 to around 150 basis points of minimum capital risk requirements. This research documents the importance of taking asymmetric effects (leverage effects) into account in volatility forecasts when it comes to risk management analysis.
    Keywords: Asymmetric volatility;Backtesting;GARCH;TARCH;Implied volatility;Stock index futures;Value at Risk;Mexico
    JEL: C15 C22 C53 E31 E37
    Date: 2020–09
  8. By: Janusz Gajda (Faculty of Economic Sciences, University of Warsaw); Rafał Walasek (Faculty of Economic Sciences, University of Warsaw)
    Abstract: This article covers the implementation of fractional (non-integer order) differentiation on four datasets based on stock prices of main international stock indexes: WIG 20, S&P 500, DAX, Nikkei 225. This concept has been proposed by Lopez de Prado to find the most appropriate balance between zero differentiation and fully differentiated time series. The aim is making time series stationary while keeping its memory and predictive power. This paper makes also the comparison between fractional and classical differentiation in terms of the effectiveness of artificial neural networks. This comparison is done in two viewpoints: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The conclusion of the study is that fractionally differentiated time series performed better in trained ANN.
    Keywords: fractional differentiation, financial time series, stock exchange, artificial neural networks
    JEL: C22 C32 G10
    Date: 2020
  9. By: Funke, Michael; Loermann, Julius; Tsang, Andrew
    Abstract: We analyse volatility spillovers between the on- and offshore (CNY and CNH) Renminbi exchange rates towards the US dollar (USD). The volatility impulse response (VIRF) methodology introduced by Hafner and Herwatz (2006) is applied to several shocks between January 2012 and December 2019. Furthermore, we propose a novel way of estimating VIRFs based on Bayesian estimation of the MV-GARCH BEKK model. A simple Independence Chain Metropolis-Hastings algorithm allows drawing VIRFs in an efficient manner, allowing to analyse the significance and persistence of volatility shocks and associated volatility spillovers. The VIRF results show that the CNH exchange rate promptly reflects the global market demand and supply, while the CNY exchange rate reacts with a time lag. The VIRF results also show the existence of spillovers between the two markets as the co-volatility increases in response to shocks.
    JEL: C32 E58 F31 F51
    Date: 2020–10–06
  10. By: Nissilä, Wilma
    Abstract: This article surveys both earlier and recent research on recession forecasting with probit based time series models. Most studies use either a static probit model or its extensions in order toestimate the recession probabilities, while others use models based on a latent variable ap-proach to account for nonlinearities. Many studies find that the term spread (i.e, the difference between long-term and short-term yields) is a useful predictor for recessions, but some recent studies also find that the ability of spread to predict recessions in the Euro Area has diminished over the years. Confidence indicators and financial variables such as stock returns seem to provide additional predictive power over the term spread. More sophisticated models outper-form the basic static probit model in various studies. An empirical analysis made for Finland strengthens the findings of earlier studies. Consumer confidence is especially useful predictor of Finnish business cycle and the accuracy of the static single-predictor model can be improved by using multiple predictors and by allowing the dynamic extension.
    Keywords: business cycles,recession forecasting,probit models
    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.