nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒02‒28
eight papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Conditional Heteroskedasticity in the Volatility of Asset Returns By Ding, Y.
  2. The Time-Varying Multivariate Autoregressive Index Model By G. Cubadda; S. Grassi; B. Guardabascio
  3. Stock returns predictability with unstable predictors By Calonaci, Fabio; Kapetanios, George; Price, Simon
  4. Efficiently Detecting Multiple Structural Breaks in Systems of Linear Regression Equations with Integrated and Stationary Regressors By Karsten Schweikert
  5. Testing for cointegration with structural changes in very small sample By Jérôme Trinh
  6. A first order binomial mixed poisson integer-valued autoregressive model with serially dependent innovations By Chen, Zezhun Chen; Dassios, Angelos; Tzougas, George
  7. The Variability and Volatility of Sleep: An Archetypal Approach By Hamermesh, Daniel S.; Pfann, Gerard A.
  8. Fractional SDE-Net: Generation of Time Series Data with Long-term Memory By Kohei Hayashi; Kei Nakagawa

  1. By: Ding, Y.
    Abstract: We propose a new class of conditional heteroskedasticity in the volatility (CHV) models which allows for time-varying volatility of volatility in the volatility of asset returns. This class nests a variety of GARCH-type models and the SHARV model of Ding (2021). CH-V models can be seen as a special case of the stochastic volatility of volatility model. We then introduce two examples of CH-V in which we specify a GJR-GARCH and an E-GARCH processes for the volatility of volatility, respectively. We also show a novel way of introducing the leverage effect of negative returns on the volatility through the volatility of volatility process. Empirical study confirms that CH-V models have better goodness-of-fit and out-of-sample volatility and Value-at-Risk forecasts than common GARCH-type models.
    Keywords: forecasting, GARCH, SHARV, volatility, volatility of volatility
    JEL: C22 C32 C53 C58 G17
    Date: 2021–11–09
  2. By: G. Cubadda; S. Grassi; B. Guardabascio
    Abstract: Many economic variables feature changes in their conditional mean and volatility, and Time Varying Vector Autoregressive Models are often used to handle such complexity in the data. Unfortunately, when the number of series grows, they present increasing estimation and interpretation problems. This paper tries to address this issue proposing a new Multivariate Autoregressive Index model that features time varying means and volatility. Technically, we develop a new estimation methodology that mix switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows to select or weight, in real time, the number of common components and other features of the data using Dynamic Model Selection or Dynamic Model Averaging without further computational cost. Using USA macroeconomic data, we provide a structural analysis and a forecasting exercise that demonstrates the feasibility and usefulness of this new model. Keywords: Large datasets, Multivariate Autoregressive Index models, Stochastic volatility, Bayesian VARs.
    Date: 2022–01
  3. By: Calonaci, Fabio; Kapetanios, George; Price, Simon
    Abstract: We re-examine predictability of US stock returns. Theoretically well-founded models predict that stationary combinations of I(1) variables such as the dividend or earnings to price ratios or the consumption/asset/income relationship often known as CAY may predict returns. However, there is evidence that these relationships are unstable, and that allowing for discrete shifts in the unconditional mean (location shifts) can lead to greater predictability. It is unclear why there should be a small number of discrete shifts and we allow for more general instability in the predictors, characterised by smooth variation variation, using a method introduced by Giraitis, Kapetanios and Yates. This can remove persistent components from observed time series, that may otherwise account for the presence of near unit root type behaviour. Our methodology may therefore be seen as an alternative to the widely used IVX methods where there is strong persistence in the predictor. We apply this to the three predictors mentioned above in a sample from 1952 to 2019 (including the financial crisis but excluding the Covid pandemic) and find that modelling smooth instability improves predictability and forecasting performance and tends to outperform discrete location shifts, whether identified by in-sample Bai-Perron tests or Markov-switching models.
    Keywords: returns predictability; long horizons; instability
    Date: 2022–02–18
  4. By: Karsten Schweikert
    Abstract: In this paper, we propose a two-step procedure based on the group LASSO estimator in combination with a backward elimination algorithm to efficiently detect multiple structural breaks in linear regressions with multivariate responses. Applying the two-step estimator, we jointly detect the number and location of change points, and provide consistent estimates of the coefficients. Our framework is flexible enough to allow for a mix of integrated and stationary regressors, as well as deterministic terms. Using simulation experiments, we show that the proposed two-step estimator performs competitively against the likelihood-based approach (Qu and Perron, 2007; Li and Perron, 2017; Oka and Perron, 2018) when trying to detect common breaks in finite samples. However, the two-step estimator is computationally much more efficient. An economic application to the identification of structural breaks in the term structure of interest rates illustrates this methodology.
    Date: 2022–01
  5. By: Jérôme Trinh (Université de Cergy-Pontoise, THEMA)
    Abstract: This article proposes an adaptation of existing tests of cointegration with endogenous structural changes to very small sample size. Size-corrected critical values for both testing cointegration with endogenous structural breaks and testing structural breaks in the parameters in a cointegration model are computed in this context. We show that the power of such a testing procedure is satisfying in sample sizes smaller than fifty observations. This of interest for macroeconometric studies of emerging economies for which the data history is usually not long enough to apply conventional methods. When the serial correlation is low, we find the tests to be powerful for even less than thirty observations. A combined procedure of testing for cointegration and structural change allows us to improve the power of testing cointegration in very small sample sizes while staying agnostic about the underlying data generating processes. An example using the Chinese data finds a cointegration relationship with two structural breaks between the national household consumption expenditures, the retail sales of consumer goods and the investment in fixed assets during the last four decades.
    Keywords: Time series, cointegration, structural change, very small sample, emerging economies
    JEL: C32 E17
    Date: 2022
  6. By: Chen, Zezhun Chen; Dassios, Angelos; Tzougas, George
    Abstract: Motivated by the extended Poisson INAR(1), which allows innovations to be serially dependent, we develop a new family of binomial-mixed Poisson INAR(1) (BMP INAR(1)) processes by adding a mixed Poisson component to the innovations of the classical Poisson INAR(1) process. Due to the flexibility of the mixed Poisson component, the model includes a large class of INAR(1) processes with different transition probabilities. Moreover, it can capture some overdispersion features coming from the data while keeping the innovations serially dependent. We discuss its statistical properties, stationarity conditions and transition probabilities for different mixing densities (Exponential, Lindley). Then, we derive the maximum likelihood estimation method and its asymptotic properties for this model. Finally, we demonstrate our approach using a real data example of iceberg count data from a financial system.
    Keywords: Count data time series; Binomial-Mixed Poisson INAR(1) models; mixed Poisson distribution; overdispersion; maximum likelihood estimation; T&F deal
    JEL: C1
    Date: 2021–11–01
  7. By: Hamermesh, Daniel S. (Barnard College); Pfann, Gerard A. (Maastricht University)
    Abstract: Using Dutch time-diary data from 1975-2005 covering over 10,000 respondents for 7 consecutive days each, we show that individuals' sleep time exhibits both variability and volatility characterized by stationary autoregressive conditional heteroscedasticity: The absolute values of deviations from a person's average sleep on one day are positively correlated with those on the next day. Sleep is more variable on weekends and among people with less education, who are younger and who do not have young children at home. Volatility is greater among parents with young children, slightly greater among men than women, but independent of other demographics. A theory of economic incentives to minimize the dispersion of sleep predicts that higher-wage workers will exhibit less dispersion, a result demonstrated using extraneous estimates of earnings equations to impute wage rates. Volatility in sleep spills over onto volatility in other personal activities, with no reverse causation onto sleep. The results illustrate a novel dimension of economic inequality and could be applied to a wide variety of human behavior and biological processes.
    Keywords: time use, ARCH, economic incentives in biological processes, volatility
    JEL: C22 J22 I14
    Date: 2022–01
  8. By: Kohei Hayashi; Kei Nakagawa
    Abstract: In this paper, we focus on generation of time-series data using neural networks. It is often the case that input time-series data, especially taken from real financial markets, is irregularly sampled, and its noise structure is more complicated than i.i.d. type. To generate time series with such a property, we propose fSDE-Net: neural fractional Stochastic Differential Equation Network. It generalizes the neural SDE model by using fractional Brownian motion with Hurst index larger than half, which exhibits long-term memory property. We derive the solver of fSDE-Net and theoretically analyze the existence and uniqueness of the solution to fSDE-Net. Our experiments demonstrate that the fSDE-Net model can replicate distributional properties well.
    Date: 2022–01

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