nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒04‒12
eleven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A note on global identification in structural vector autoregressions By Emanuele Bacchiocchi; Toru Kitagawa
  2. Identifying structural shocks to volatility through a proxy-MGARCH model By Fengler, Matthias; Polivka, Jeannine
  3. Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations By Ricardo P. Masini; Marcelo C. Medeiros; Eduardo F. Mendes
  4. Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs By Jonas E. Arias; Jesús Fernández-Villaverde; Juan F. Rubio-Ramírez; Minchul Shin
  5. Bootstrap Inference for Hawkes and General Point Processes By Giuseppe Cavaliere; Ye Lu; Anders Rahbek; Jacob St{\ae}rk-{\O}stergaard
  6. Structural and Predictive Analyses with a Mixed Copula-Based Vector Autoregression Model By Woraphon Yamaka; Rangan Gupta; Sukrit Thongkairat; Paravee Maneejuk
  7. A Stochastic Time Series Model for Predicting Financial Trends using NLP By Pratyush Muthukumar; Jie Zhong
  8. The Science and Art of Communicating Fan Chart Uncertainty: The case of Inflation Outcome in Sierra Leone By Jackson, Emerson Abraham; Tamuke, Edmund
  9. Singular conditional autoregressive Wishart model for realized covariance matrices By Alfelt, Gustav; Bodnar, Taras; Javed, Farrukh; Tyrcha, Joanna
  10. A reality check on the GARCH-MIDAS volatility models By Virk, Nader; Javed, Farrukh; Awartani, Basel
  11. Domain Specific Concept Drift Detectors for Predicting Financial Time Series By Filippo Neri

  1. By: Emanuele Bacchiocchi; Toru Kitagawa
    Abstract: In a landmark contribution to the structural vector autoregression (SVARs) literature, Rubio-Ramirez, Waggoner, and Zha (2010, `Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,' Review of Economic Studies) shows a necessary and sufficient condition for equality restrictions to globally identify the structural parameters of a SVAR. The simplest form of the necessary and sufficient condition shown in Theorem 7 of Rubio-Ramirez et al (2010) checks the number of zero restrictions and the ranks of particular matrices without requiring knowledge of the true value of the structural or reduced-form parameters. However, this note shows by counterexample that this condition is not sufficient for global identification. Analytical investigation of the counterexample clarifies why their sufficiency claim breaks down. The problem with the rank condition is that it allows for the possibility that restrictions are redundant, in the sense that one or more restrictions may be implied by other restrictions, in which case the implied restriction contains no identifying information. We derive a modified necessary and sufficient condition for SVAR global identification and clarify how it can be assessed in practice.
    Date: 2021–02
  2. By: Fengler, Matthias; Polivka, Jeannine
    Abstract: We extend the classical MGARCH specification for volatility modeling by developing a structural MGARCH model targeting identification of shocks and volatility spillovers in a speculative return system. Similarly to the proxy-sVAR framework, we work with auxiliary proxy variables constructed from news-related measures to identify the underlying shock system. We achieve full identification with multiple proxies by chaining Givens rotations. In an empirical application, we identify an equity, bond and currency shock. We study the volatility spillovers implied by these labelled structural shocks. Our analysis shows that symmetric spillover regimes are rejected.
    Keywords: Givens rotations, identification, news-based measures, proxy-MGARCH, shock labelling, structural innovations, volatility spillovers
    JEL: C32 C51 C58 G12
    Date: 2021–04
  3. By: Ricardo P. Masini; Marcelo C. Medeiros; Eduardo F. Mendes
    Abstract: There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive models with heavy tailed, weakly dependent innovations with virtually no assumption on the conditional heteroskedasticity. In contrast to current literature, our innovation process satisfy an $L^1$ mixingale type condition on the centered conditional covariance matrices. This condition covers $L^1$-NED sequences and strong ($\alpha$-) mixing sequences as particular examples. From a modeling perspective, it covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications that were ruled out by assumption in previous studies.
    Date: 2019–12
  4. By: Jonas E. Arias; Jesús Fernández-Villaverde; Juan F. Rubio-Ramírez; Minchul Shin
    Abstract: We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely on a Markov chain Monte Carlo to sample from the posterior distribution. We show how to use the posterior simulation outputs as inputs for exercises in causality assessment. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. Our estimated time-varying-parameters SIRD model captures the data dynamics very well, including the three waves of infections. We use the estimated (true) number of new cases and the time-varying effective reproduction number from the epidemiological model as information for structural vector autoregressions and local projections. We document how additional government-mandated mobility curtailments would have reduced deaths at zero cost or a very small cost in terms of output.
    Date: 2021–03
  5. By: Giuseppe Cavaliere; Ye Lu; Anders Rahbek; Jacob St{\ae}rk-{\O}stergaard
    Abstract: Inference and testing in general point process models such as the Hawkes model is predominantly based on asymptotic approximations for likelihood-based estimators and tests, as originally developed in Ogata (1978). As an alternative, and to improve finite sample performance, this paper considers bootstrap-based inference for interval estimation and testing. Specifically, for a wide class of point process models we consider a novel bootstrap scheme labeled 'fixed intensity bootstrap' (FIB), where the conditional intensity is kept fixed across bootstrap repetitions. The FIB, which is very simple to implement and fast in practice, naturally extends previous ideas from the bootstrap literature on time series in discrete time, where the so-called 'fixed design' and 'fixed volatility' bootstrap schemes have shown to be particularly useful and effective. We compare the FIB with the classic recursive bootstrap, which is here labeled 'recursive intensity bootstrap' (RIB). In RIB algorithms, the intensity is stochastic in the bootstrap world and implementation of the bootstrap is more involved, due to its sequential structure. For both bootstrap schemes, no asymptotic theory is available; we therefore provide a new bootstrap (asymptotic) theory, which allows to assess bootstrap validity. We also introduce novel 'non-parametric' FIB and RIB schemes, which are based on resampling time-changed transformations of the original waiting times. We show effectiveness of the different bootstrap schemes in finite samples through a set of detailed Monte Carlo experiments. As far as we are aware, this is the first detailed Monte Carlo study of bootstrap implementations for Hawkes-type processes. Finally, in order to illustrate, we provide applications of the bootstrap to both financial data and social media data.
    Date: 2021–04
  6. By: Woraphon Yamaka (Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University; Chiang Mai 50200, Thailand); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria 0002, South Africa); Sukrit Thongkairat (Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University; Chiang Mai 50200, Thailand); Paravee Maneejuk (Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University; Chiang Mai 50200, Thailand)
    Abstract: In this study, we introduce a mixed copula-based vector autoregressive (VAR) model for investigating the relationship between random variables. The one-step maximum likelihood estimation is used to obtain point estimates of the autoregressive parameters and mixed copula parameters. More specifically, we combine the likelihoods of the marginal and mixed Copula to construct the full likelihood function. The simulation study is used to confirm the accuracy of the estimation as well as the reliability of the proposed model. Various mixed copula forms from a combination of Gaussian, Student-t, Clayton, Frank, Gumbel, and Joe copulas are introduced. The proposed model is compared to the traditional VAR model and single copula-based VAR models to assess its performance. Furthermore, the real data study is also conducted to validate our proposed method. As a result, it is found that the one-step maximum likelihood provides accurate and reliable results. Also, we show that if we ignore the complex and nonlinear correlation between the errors, it causes significant efficiency loss in the parameter estimation, in terms of Bias and MSE. In the application study, the mixed copula-based VAR is the best fitting Copula for our application study.
    Keywords: Forecasting; Mixed copula; One step maximum likelihood estimation; Vector autoregressive
    Date: 2021–01
  7. By: Pratyush Muthukumar; Jie Zhong
    Abstract: Stock price forecasting is a highly complex and vitally important field of research. Recent advancements in deep neural network technology allow researchers to develop highly accurate models to predict financial trends. We propose a novel deep learning model called ST-GAN, or Stochastic Time-series Generative Adversarial Network, that analyzes both financial news texts and financial numerical data to predict stock trends. We utilize cutting-edge technology like the Generative Adversarial Network (GAN) to learn the correlations among textual and numerical data over time. We develop a new method of training a time-series GAN directly using the learned representations of Naive Bayes' sentiment analysis on financial text data alongside technical indicators from numerical data. Our experimental results show significant improvement over various existing models and prior research on deep neural networks for stock price forecasting.
    Date: 2021–02
  8. By: Jackson, Emerson Abraham; Tamuke, Edmund
    Abstract: The use of macro-econometric modelling technique has become a norm for policy decisions in central banks and in particular, the Bank of Sierra Leone. This study has leveraged on the technicalities of scientific and artistic approaches of assessing risks around point / baseline forecast; this in general makes it more convincing for probability confidence bands to be used in explaining uncertainty that surround point forecast in particular. In the case of this study, the use of the Box-Jenkins ARIMAX model has made it possible to highlight the relevance of Composite Leading Indicator (CLI) like Exchange Rate in alerting signals about early turning point of inflation outcome, both in terms of the uncertainty and risks surrounding its projections. With the derived (scientific) probability distribution of risks (30%, 60% and 90%), it was possible for the study outcome to unearth vast amount of information from the Inflation Fan Chart, particularly with respect to the art of providing balanced assessment of policy framework needed to communicate the BSL’s price stability objective. While the use of Fan Chart is hailed as a very relevant tool, the paper also recommend the use of other model approaches like Scenario and Sensitivity analysis, also considered relevant in providing leading evidence of balancing risks surrounding macroeconomic outlook.
    Keywords: Fan Chart; Normal Distribution; Forecast uncertainty; Balanced Risks
    JEL: C15 C52 C81 E37 E59
    Date: 2021–01–02
  9. By: Alfelt, Gustav (Department of Mathematics, Stockholm University); Bodnar, Taras (Department of Mathematics, Stockholm University); Javed, Farrukh (Örebro University School of Business); Tyrcha, Joanna (Department of Mathematics, Stockholm University)
    Abstract: Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in non-singular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by construction. Motivated by this situation, we introduce the Singular Conditional Autoregressive Wishart (SCAW) model to capture the temporal dynamics of time series of singular realized covariance matrices, extending the rich literature on econometric Wishart time series models to the singular case. This model is furthermore developed by covariance targeting adapted to matrices and a sectorwise BEKK-specification, allowing excellent scalability to large and extremely large portfolio sizes. Finally, the model is estimated to a 20 year long time series containing 50 stocks, and evaluated using out-ofsample forecast accuracy. It outperforms the benchmark Multivariate GARCH model with high statistical significance, and the sectorwise specification outperforms the baseline model, while using much fewer parameters.
    Keywords: Covariance targeting; High-dimensional data; Realized covariance matrix; Stock co-volatility; Time series matrix-variate model
    JEL: C32 C55 C58 G17
    Date: 2020–10–02
  10. By: Virk, Nader (Plymouth Business School); Javed, Farrukh (Örebro University School of Business); Awartani, Basel (Westminster Business School)
    Abstract: We employ a battery of model evaluation tests for a broad-set of GARCH-MIDAS models and account for data snooping bias. We document that inferences based on standard tests for GM variance components can be misleading. Our data mining free results show that the gains of macro-variables in forecasting total (long run) variance by GM models are overstated (understated). Estimation of different components of volatility is crucial for designing differentiated investing strategies, risk management plans and pricing of derivative securities. Therefore, researchers and practitioners should be wary of data mining bias, which may contaminate a forecast that may appear statistically validated using robust evaluation tests.
    Keywords: GARCH-MIDAS models; component variance forecasts; macro-variables; data snooping
    JEL: C32 C52 G11 G17
    Date: 2021–03–30
  11. By: Filippo Neri
    Abstract: Concept drift detectors allow learning systems to maintain good accuracy on non-stationary data streams. Financial time series are an instance of non-stationary data streams whose concept drifts (market phases) are so important to affect investment decisions worldwide. This paper studies how concept drift detectors behave when applied to financial time series. General results are: a) concept drift detectors usually improve the runtime over continuous learning, b) their computational cost is usually a fraction of the learning and prediction steps of even basic learners, c) it is important to study concept drift detectors in combination with the learning systems they will operate with, and d) concept drift detectors can be directly applied to the time series of raw financial data and not only to the model's accuracy one. Moreover, the study introduces three simple concept drift detectors, tailored to financial time series, and shows that two of them can be at least as effective as the most sophisticated ones from the state of the art when applied to financial time series.
    Date: 2021–03

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