nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒04‒17
six papers chosen by
Jaqueson K. Galimberti
Asian Development Bank

  1. Statistical Analysis of Time Series and Forecasting * By Pergamenshchikov Serguei; Pchelintsev Evgeny
  2. Estimation of Asymmetric Stochastic Volatility in Mean Models By Antonis Demos
  3. Network log-ARCH models for forecasting stock market volatility By Raffaele Mattera; Philipp Otto
  4. High-Frequency Volatility Estimation with Fast Multiple Change Points Detection By Greeshma Balabhadra; El Mehdi Ainasse; Pawel Polak
  5. Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models By Matteo Barigozzi
  6. Trend Breaks and the Persistence of Closed-End Mutual Fund Discounts By Nazif Durmaz; Hyeongwoo Kim; Hyejin Lee; Yanfei Sun

  1. By: Pergamenshchikov Serguei (LMRS - Laboratoire de Mathématiques Raphaël Salem - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - CNRS - Centre National de la Recherche Scientifique); Pchelintsev Evgeny (SSP&QF - International Laboratory of Statistics of Stochastic Processes and Quantitative Finance - Tomsk State University [Tomsk])
    Abstract: In this course, we present the principal parts of the time series analysis. First, stationary processes and trends in times series are introduced. Then we consider the linear regression models for which we study the main problems such that point estimation, the construction of confidence intervals, hypothesis testing, and forecasting. In addition, big data models and the main methods for their analysis are presented. Finally, we introduce the autoregressive and moving average autoregressive processes (ARMA) and study their basic properties, including the problem of forecasting.
    Date: 2023–02–02
  2. By: Antonis Demos (
    Abstract: Here we investigate the estimation of asymmetric Autoregressive Stochastic Volatility models with possibly time varying risk premia. We employ the Indirect Inference estimation developed in Gallant and Tauchen (1996), with a first step estimator either the Generalized Quadratic ARCH or the Exponential GARCH. We employ Monte-Carlo simulations to compare the two first step models in terms of bias and root Mean Squared Error. We apply the developed methods for the estimation of an asymmetric autoregressive SV-M model to international stock markets excess returns.
    Keywords: Stochastic Volatility estimation asymmetry leverage indirect inference
    Date: 2023–03–21
  3. By: Raffaele Mattera; Philipp Otto
    Abstract: This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock market. To improve the forecasting accuracy, the model integrates temporally lagged volatility information and information from adjacent nodes, which may instantaneously spill across the entire network. The model is also suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH model for processes to networks. This new approach is compared with independent univariate log-ARCH models. We could quantify the improvements due to the instantaneous network ARCH effects, which are studied for the first time in this paper. The edges are determined based on various distance and correlation measures between the time series. The performances of the alternative networks' definitions are compared in terms of out-of-sample accuracy. Furthermore, we consider ensemble forecasts based on different network definitions.
    Date: 2023–03
  4. By: Greeshma Balabhadra; El Mehdi Ainasse; Pawel Polak
    Abstract: We propose high-frequency volatility estimators with multiple change points that are $\ell_1$-regularized versions of two classical estimators: quadratic variation and bipower variation. We establish consistency of these estimators for the true unobserved volatility and the change points locations under general sub-Weibull distribution assumptions on the jump process. The proposed estimators employ the computationally efficient least angle regression algorithm for estimation purposes, followed by a reduced dynamic programming step to refine the final number of change points. In terms of numerical performance, the proposed estimators are computationally fast and accurately identify breakpoints near the end of the sample, which is highly desirable in today's electronic trading environment. In terms of out-of-sample volatility prediction, our new estimators provide more realistic and smoother volatility forecasts, and they outperform a wide range of classical and recent volatility estimators across various frequencies and forecasting horizons.
    Date: 2023–03
  5. By: Matteo Barigozzi
    Abstract: We review Quasi Maximum Likelihood estimation of factor models for high-dimensional panels of time series. We consider two cases: (1) estimation when no dynamic model for the factors is specified (Bai and Li, 2016); (2) estimation based on the Kalman smoother and the Expectation Maximization algorithm thus allowing to model explicitly the factor dynamics (Doz et al., 2012). Our interest is in approximate factor models, i.e., when we allow for the idiosyncratic components to be mildly cross-sectionally, as well as serially, correlated. Although such setting apparently makes estimation harder, we show, in fact, that factor models do not suffer of the curse of dimensionality problem, but instead they enjoy a blessing of dimensionality property. In particular, we show that if the cross-sectional dimension of the data, $N$, grows to infinity, then: (i) identification of the model is still possible, (ii) the mis-specification error due to the use of an exact factor model log-likelihood vanishes. Moreover, if we let also the sample size, $T$, grow to infinity, we can also consistently estimate all parameters of the model and make inference. The same is true for estimation of the latent factors which can be carried out by weighted least-squares, linear projection, or Kalman filtering/smoothing. We also compare the approaches presented with: Principal Component analysis and the classical, fixed $N$, exact Maximum Likelihood approach. We conclude with a discussion on efficiency of the considered estimators.
    Date: 2023–03
  6. By: Nazif Durmaz; Hyeongwoo Kim; Hyejin Lee; Yanfei Sun
    Abstract: Closed-end fund (CEF) prices often exhibit large and persistent deviations from their associated net asset values (NAVs), which is puzzling since CEFs are repackaged financial assets and NAVs are publicly observable. We point out that such high persistence is mainly observed when linear models are employed, calling for nonlinear models to understand this so-called CEF discount puzzle. Applying the RALS-LM framework that allows for multiple endogenously identified trend-breaks for 31 CEF discount data, we show that CEF prices tend to fluctuate around time-varying time trends, which can be consistent with a regime switching model. We also demonstrate that utilizing non-normal errors through moment conditions enhances the efficiency at the margin. Nonlinearity with level shifts only fails to explain the observed persistence of CEF discounts.
    Keywords: Closed-End Fund; CEF Discount Puzzle; Residual Augmented Least Squares; Non-Normal Error; Trend Breaks
    JEL: C22 G12 G15
    Date: 2023–03

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