nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒07‒11
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Financial-market volatility prediction with multiplicative Markov-switching MIDAS components By Bjoern Schulte-Tillman; Mawuli Segnon; Bernd Wilfling
  2. The Forecasting performance of the Factor model with Martingale Difference errors By Alessandro Giovannelli; Luca Mattia Rolla
  3. Subgeometrically ergodic autoregressions with autoregressive conditional heteroskedasticity By Mika Meitz; Pentti Saikkonen
  4. Testing Overidentifying Restrictions with High-Dimensional Data and Heteroskedasticity By Qingliang Fan; Zijian Guo; Ziwei Mei
  5. Dealing with dimension reduction in financial panel data By Alessandro Bitetto; Paola Cerchiello; Charilaos Mertzanis
  6. Maximum entropy approach to multivariate time series randomization By Marcaccioli, Riccardo; Livan, Giacomo

  1. By: Bjoern Schulte-Tillman; Mawuli Segnon; Bernd Wilfling
    Abstract: We propose four multiplicative-component volatility MIDAS models to disentangle short- and long-term volatility sources. Three of our models specify short-term volatility as Markov-switching processes. We establish statistical properties, covariance-stationarity conditions, and an estimation framework using regime-switching filter techniques. A simulation study shows the robustness of the estimates against several mis-specifications. An out-of-sample forecasting analysis with daily S&P500 returns and quarterly-sampled (macro)economic variables yields two major results. (i) Specific long-term variables in the MIDAS models significantly improve forecast accuracy (over the non-MIDAS benchmarks). (ii) We robustly find superior performance of one Markov-switching MIDAS specification (among a set of competitor models) when using the 'Term structure' as the long-term variable.
    Keywords: MIDAS volatility modeling, Hierarchical hidden Markov models, Markov-switching, Forecasting, Model conï¬ dence sets
    JEL: C51 C53 C58 E44
    Date: 2022–06
  2. By: Alessandro Giovannelli; Luca Mattia Rolla
    Abstract: We compare the pseudo-real-time forecasting performance of two factor models for a large set of macroeconomic and financial time series: (i) The standard principal-component model used by Stock and Watson (2002) (ii) The factor model with martingale difference errors introduced by Lee and Shao (2018). The factor model with martingale difference error (FMMDE) makes it possible to retrieve a transformation of the original series so that the resulting variables can be partitioned into distinct groups according to whether they are conditionally mean independent upon past information or not. In terms of prediction, this feature of the model allows us to achieve optimal results (in the mean squared error sense) as dimension reduction is performed. Conducting a pseudo-real-time forecasting exercise based on a large dataset of macroeconomic and financial monthly time series for the U.S. economy, the results obtained from the empirical study suggest that FMMDE performs comparably to SW for short-term forecasting horizons. In contrast, for long-term forecasting horizons, FMMDE displays superior performance. These results are particularly evident for Output & Income, Labor Market, and Consumption sectors.
    Date: 2022–05
  3. By: Mika Meitz; Pentti Saikkonen
    Abstract: In this paper, we consider subgeometric ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The notion of subgeometric ergodicity was introduced in the Markov chain literature in 1980s and it means that the transition probability measures converge to the stationary measure at a rate slower than geometric; this rate is also closely related to the convergence rate of $\beta$-mixing coefficients. While the existing literature on subgeometrically ergodic autoregressions assumes a homoskedastic error term, this paper provides an extension to the case of conditionally heteroskedastic ARCH-type errors, considerably widening the scope of potential applications. Specifically, we consider suitably defined higher-order nonlinear autoregressions with possibly nonlinear ARCH errors and show that they are, under appropriate conditions, subgeometrically ergodic at a polynomial rate. An empirical example using energy sector volatility index data illustrates the use of subgeometrically ergodic AR-ARCH models.
    Date: 2022–05
  4. By: Qingliang Fan; Zijian Guo; Ziwei Mei
    Abstract: This paper proposes a new test of overidentifying restrictions (called the Q test) with high-dimensional data. This test is based on estimation and inference for a quadratic form of high-dimensional parameters. It is shown to have the desired asymptotic size and power properties under heteroskedasticity, even if the number of instruments and covariates is larger than the sample size. Simulation results show that the new test performs favorably compared to existing alternative tests (Chao et al., 2014; Kolesar, 2018; Carrasco and Doukali, 2021) under the scenarios when those tests are feasible or not. An empirical example of the trade and economic growth nexus manifests the usefulness of the proposed test.
    Date: 2022–04
  5. By: Alessandro Bitetto (University of Pavia); Paola Cerchiello (University of Pavia); Charilaos Mertzanis (Abu Dhabi University)
    Abstract: In this paper, we present a fully data-driven statistical approach to building a synthetic index based on intrinsic information of the considered ecosystem, namely the financial one. Among the several methods made available in the literature, we propose the employment of a Dynamic Factor Model approach which allows us to fully and correctly compare observations at hand in space and time. We contribute to the research field by offering a statistically sound methodology which goes beyond state of the art techniques on dimension reduction, mainly based on Principal Component Analysis. We adopt a country by country fitting strategy to elicit the inner country specific characteristics and then we combine results together by means of a Vector Autoregressive and Kalman filter approach. To this aim, we analyze a set of 17 Financial Soundness Indicators provided by the International Monetary Fund ranging from 2006 to 2017 for 140 countries that span the globe, including both strong and developing economies.
    Keywords: Financial stability, Financing constraints, Data-driven, Dynamic Factor Model, State-space model, dimension reduction
    Date: 2022–06
  6. By: Marcaccioli, Riccardo; Livan, Giacomo
    Abstract: Natural and social multivariate systems are commonly studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical approach—analogous to the configuration model for networked systems—for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications with a case study on financial portfolio selection.
    Keywords: EPSRC Early Career Fellowship in Digital Economy (Grant No. EP/N006062/1).
    JEL: J1 C1
    Date: 2020–06–30

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