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on Econometric Time Series |
| By: | Sibbertsen, Philipp; Wenger, Kai; Wingert, Simon |
| Abstract: | This paper considers estimation and testing of multiple breaks that occur at unknown dates in multivariate long-memory time series. We propose a likelihood ratio based approach for estimating breaks in the mean and the covariance of a system of long-memory time series. The limiting distribution of these estimates as well as consistency of the estimators is derived. A testing procedure to determine the unknown number of break points is given based on iterative testing on the regression residuals. A Monte Carlo exercise shows the finite sample performance of our method. An empirical application to inflation series illustrates the usefulness of our procedures. |
| Keywords: | Multivariate Long Memory ; Multiple Structural Breaks ; Hypothesis Testing |
| JEL: | C12 C22 C58 G15 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-676&r=all |
| By: | T. -N. Nguyen; M. -N. Tran; R. Kohn |
| Abstract: | We propose a new class of financial volatility models, which we call the REcurrent Conditional Heteroskedastic (RECH) models, to improve both the in-sample analysis and out-of-sample forecast performance of the traditional conditional heteroskedastic models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into the conditional variance of the traditional conditional heteroskedastic models, e.g. the GARCH-type models, to flexibly capture the dynamics of the underlying volatility. The RECH models can detect interesting effects in financial volatility overlooked by the existing conditional heteroskedastic models such as the GARCH (Bollerslev, 1986), GJR (Glosten et al., 1993) and EGARCH (Nelson, 1991). The new models often have good out-of-sample forecasts while still explain well the stylized facts of financial volatility by retaining the well-established structures of the econometric GARCH-type models. These properties are illustrated through simulation studies and applications to four real stock index datasets. An user-friendly software package together with the examples reported in the paper are available at https://github.com/vbayeslab. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.13061&r=all |
| By: | Perone, G. |
| Abstract: | Coronavirus disease (COVID-19) is a severe ongoing novel pandemic that has emerged in Wuhan, China, in December 2019. As of October 13, the outbreak has spread rapidly across the world, affecting over 38 million people, and causing over 1 million deaths. In this article, I analysed several time series forecasting methods to predict the spread of COVID-19 second wave in Italy, over the period after October 13, 2020. I used an autoregressive model (ARIMA), an exponential smoothing state space model (ETS), a neural network autoregression model (NNAR), and the following hybrid combinations of them: ARIMA-ETS, ARIMA-NNAR, ETS-NNAR, and ARIMA-ETS-NNAR. About the data, I forecasted the number of patients hospitalized with mild symptoms, and in intensive care units (ICU). The data refer to the period February 21, 2020– October 13, 2020 and are extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results show that i) the hybrid models, except for ARIMA-ETS, are better at capturing the linear and non-linear epidemic patterns, by outperforming the respective single models; and ii) the number of COVID-19-related hospitalized with mild symptoms and in ICU will rapidly increase in the next weeks, by reaching the peak in about 50-60 days, i.e. in mid-December 2020, at least. To tackle the upcoming COVID-19 second wave it is necessary to enhance social distancing, hire healthcare workers and implement sufficient hospital facilities, protective equipment, and ordinary and intensive care beds. |
| Keywords: | COVID-19; outbreak; second wave; Italy; hybrid forecasting models; ARIMA; ETS; NNAR. |
| JEL: | C22 C53 I18 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:yor:hectdg:20/18&r=all |
| By: | Alexander Wehrli (ETH Zürich); Didier Sornette (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology) |
| Abstract: | We introduce a novel modelling framework - the Hawkes(p,q) process - which allows us to parsimoniously disentangle and quantify the time-varying share of high frequency financial price changes that are due to endogenous feedback processes and not exogenous impulses. We show how both flexible exogenous arrival intensities, as well as a time-dependent feedback parameter can be estimated in a structural manner using an Expectation Maximization algorithm. We use this approach to investigate potential characteristic signatures of anomalous market regimes in the vicinity of "flash crashes" - events where prices exhibit highly irregular and cascading dynamics. Our study covers some of the most liquid electronic financial markets, in particular equity and bond futures, foreign exchange and cryptocurrencies. Systematically balancing the degrees of freedom of both exogenously driving processes and endogenous feedback variation using information criteria, we show that the dynamics around such events are not universal, highlighting the usefulness of our approach (i) post-mortem for developing remedies and better future processes - e.g. improving circuit breakers or latency floor designs - and potentially (ii) ex-ante for short-term forecasts in the case of endogenously driven events. Finally, we test our proposed model against a process with refined treatment of exogenous clustering dynamics in the spirit of the recently proposed autoregressive moving-average (ARMA) point process. |
| Keywords: | Flash crash; Hawkes process; ARMA point process; High frequency financial data; Market microstructure; EM algorithm; Time-varying parameters |
| JEL: | C01 C40 C52 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2092&r=all |
| By: | Kiss, Tamás (Örebro University School of Business); Nguyen, Hoang (Örebro University School of Business); Österholm, Pär (Örebro University School of Business) |
| Abstract: | In this paper, we analyse the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigate the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data ranging from January 1954 to September 2019, the properties of the models are assessed both within- and out-of-sample. We find strong evidence in favour of modelling both GARCH effects and non-Gaussianity. Accounting for these properties improves within-sample performance as well as point and density forecasts. |
| Keywords: | Non-Gaussianity; GARCH; Density forecasts; Probability integral transform |
| JEL: | C22 C52 E44 E47 G17 |
| Date: | 2020–10–29 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:oruesi:2020_013&r=all |
| By: | Licht, Adrian; Escribano Saez, Alvaro; Blazsek, Szabolcs Istvan |
| Abstract: | In this paper, we introduce Beta-t-QVAR (quasi-vector autoregression) for the joint modelling of score-driven location and scale. Asymptotic theory of the maximum likelihood (ML) estimatoris presented, and sufficient conditions of consistency and asymptotic normality of ML are proven. Forthe joint score-driven modelling of risk premium and volatility, Dow Jones Industrial Average (DJIA)data are used in an empirical illustration. Prediction accuracy of Beta-t-QVAR is superior to theprediction accuracies of Beta-t-EGARCH (exponential generalized AR conditional heteroscedasticity),A-PARCH (asymmetric power ARCH), and GARCH (generalized ARCH). The empirical results motivate the use of Beta-t-QVAR for the valuation of DJIA options. |
| Keywords: | Generalized Autoregressive Score; Dynamic Conditional Score; Risk Premium; Volatility |
| JEL: | C58 C22 |
| Date: | 2020–11–05 |
| URL: | http://d.repec.org/n?u=RePEc:cte:werepe:31339&r=all |
| By: | Tae-Hwy Lee (Department of Economics, University of California Riverside); Ekaterina Seregina (University of California Riverside) |
| Abstract: | This paper studies forecast combination (as an expert system) using the precision matrix estimation of forecast errors when the latter admit the approximate factor model. This approach incorporates the facts that experts often use common sets of information and hence they tend to make common mistakes. This premise is evidenced in many empirical results. For example, the European Central Bank's Survey of Professional Forecasters on Euro-area real GDP growth demonstrates that the professional forecasters tend to jointly understate or overstate GDP growth. Motivated by this stylized fact, we develop a novel framework which exploits the factor structure of forecast errors and the sparsity in the precision matrix of the idiosyncratic components of the forecast errors. The proposed algorithm is called Factor Graphical Model (FGM). Our approach overcomes the challenge of obtaining the forecasts that contain unique information, which was shown to be necessary to achieve a "winning" forecast combination. In simulation, we demonstrate the merits of the FGM in comparison with the equal-weighted forecasts and the standard graphical methods in the literature. An empirical application to forecasting macroeconomic time series in big data environment highlights the advantage of the FGM approach in comparison with the existing methods of forecast combination. |
| Keywords: | High-dimensionality, Graphical Lasso, Approximate Factor Model, Nodewise Regression, Precision Matrix |
| JEL: | C13 C38 C55 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:202024&r=all |
| By: | Elizabeth Fons; Paula Dawson; Xiao-jun Zeng; John Keane; Alexandros Iosifidis |
| Abstract: | Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage. This is even more so in the field of financial prediction, where data tends to be small, noisy and non-stationary. In this paper we evaluate several augmentation methods applied to stocks datasets using two state-of-the-art deep learning models. The results show that several augmentation methods significantly improve financial performance when used in combination with a trading strategy. For a relatively small dataset ($\approx30K$ samples), augmentation methods achieve up to $400\%$ improvement in risk adjusted return performance; for a larger stock dataset ($\approx300K$ samples), results show up to $40\%$ improvement. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.15111&r=all |
| By: | Kleyton Vieira Sales da Costa; Felipe Leite Coelho da Silva; Josiane da Silva Cordeiro Coelho |
| Abstract: | Gross domestic product (GDP) is an important economic indicator that aggregates useful information to assist economic agents and policymakers in their decision-making process. In this context, GDP forecasting becomes a powerful decision optimization tool in several areas. In order to contribute in this direction, we investigated the efficiency of classical time series models and the class of state-space models, applied to Brazilian gross domestic product. The models used were: a Seasonal Autoregressive Integrated Moving Average (SARIMA) and a Holt-Winters method, which are classical time series models; and the dynamic linear model, a state-space model. Based on statistical metrics of model comparison, the dynamic linear model presented the best forecasting model and fit performance for the analyzed period, also incorporating the growth rate structure significantly. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.13259&r=all |