nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒05‒02
three papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Reducing overestimating and underestimating volatility via the augmented blending-ARCH model By Jun Lu; Shao Yi
  2. A multiplicative thinning-based integer-valued GARCH model By Aknouche, Abdelhakim; Scotto, Manuel
  3. On Robust Inference in Time Series Regression By Richard T. Baillie; Francis X. Diebold; George Kapetanios; Kun Ho Kim

  1. By: Jun Lu; Shao Yi
    Abstract: SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility. Though the SVR-GARCH model has achieved good performance in terms of various performance measurements, trading opportunities, peak or trough behaviors in the time series are all hampered by underestimating or overestimating the volatility. We propose a blending ARCH (BARCH) and an augmented BARCH (aBARCH) model to overcome this kind of problem and make the prediction towards better peak or trough behaviors. The method is illustrated using real data sets including SH300 and S&P500. The empirical results obtained suggest that the augmented and blending models improve the volatility forecasting ability.
    Date: 2022–03
  2. By: Aknouche, Abdelhakim; Scotto, Manuel
    Abstract: In this paper we introduce a multiplicative integer-valued time series model, which is defined as the product of a unit-mean integer-valued independent and identically distributed (iid) sequence, and an integer-valued dependent process. The latter is defined as a binomial thinning operation of its own past and of the past of the observed process. Furthermore, it combines some features of the integer-valued GARCH (INGARCH), the autoregressive conditional duration (ACD), and the integer autoregression (INAR) processes. The proposed model is semi-parametric and is able to parsimoniously generate very high overdispersion, persistence, and heavy-tailedness. The dynamic probabilistic structure of the model is first analyzed. In addition, parameter estimation is considered by using a two-stage weighted least squares estimate (2SWLSE), consistency and asymptotic normality (CAN) of which are established under mild conditions. Applications of the proposed formulation to simulated and actual count time series data are provided.
    Keywords: Integer-valued time series, INAR model, INGARCH model, multiplicative error model (MEM), ACD model, two-stage weighted least squares.
    JEL: C01 C13 C22 C25
    Date: 2022–03–20
  3. By: Richard T. Baillie; Francis X. Diebold; George Kapetanios; Kun Ho Kim
    Abstract: Least squares regression with heteroskedasticity and autocorrelation consistent (HAC) standard errors has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HAC estimation technology to time series environments. First, most economic time series have strong autocorrelation, which renders HAC regression parameter estimates highly inefficient. Second, strong autocorrelation similarly renders HAC conditional predictions highly inefficient. Finally, the structure of most popular HAC estimators is ill-suited to capture the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in hypothesis testing, in all but the largest sample sizes. We show that all three problems are largely avoided by the use of a simple dynamic regression (DynReg), which is easily implemented and also avoids possible problems concerning strong exogeneity. We demonstrate the advantages of DynReg with detailed simulations covering a range of practical issues.
    Date: 2022–03

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