nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒03‒16
four papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Testing the existence of moments for GARCH processes By Francq, Christian; Zakoian, Jean-Michel
  2. Data Normalization for Bilinear Structures in High-Frequency Financial Time-series By Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
  3. Estimation of Weak Factor Models By Yoshimasa Uematsu; Takashi Yamagatay
  4. The Long Memory of Equity Volatility and the Macroeconomy: International Evidence By Dräger, Lena; Nguyen, Duc Binh Benno; Prokopczuk, Marcel; Sibbertsen, Philipp

  1. By: Francq, Christian; Zakoian, Jean-Michel
    Abstract: It is generally admitted that many financial time series have heavy tailed marginal distributions. When time series models are fitted on such data, the non-existence of appropriate moments may invalidate standard statistical tools used for inference. Moreover, the existence of moments can be crucial for risk management, for instance when risk is measured through the expected shortfall. This paper considers testing the existence of moments in the framework of GARCH processes. While the second-order stationarity condition does not depend on the distribution of the innovation, higher-order moment conditions involve moments of the independent innovation process. We propose tests for the existence of high moments of the returns process which are based on the joint asymptotic distribution of the Quasi-Maximum Likelihood (QML) estimator of the volatility parameters and empirical moments of the residuals. A bootstrap procedure is proposed to improve the finite-sample performance of our test. To achieve efficiency gains we consider non Gaussian QML estimators founded on reparametrizations of the GARCH model, and we discuss optimality issues. Monte-Carlo experiments and an empirical study illustrate the asymptotic results.
    Keywords: Conditional heteroskedasticity, Efficiency comparisons, Non-Gaussian QMLE, Residual Bootstrap, Stationarity tests
    JEL: C12 C13 C22
    Date: 2019–12–02
  2. By: Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
    Abstract: Financial time-series analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities. This property presents great challenges and can significantly affect the performance of the subsequent analysis/forecasting steps. Recently, the Temporal Attention augmented Bilinear Layer (TABL) has shown great performances in tackling financial forecasting problems. In this paper, by taking into account the nature of bilinear projections in TABL networks, we propose Bilinear Normalization (BiN), a simple, yet efficient normalization layer to be incorporated into TABL networks to tackle potential problems posed by non-stationarity and multimodalities in the input series. Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 millions order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin.
    Date: 2020–03
  3. By: Yoshimasa Uematsu; Takashi Yamagatay
    Abstract: This paper proposes a novel estimation method for the weak factor models, a slightly stronger version of the approximate factor models of Chamberlain and Rothschild (1983), with large cross-sectional and time-series dimensions (N and T, respectively). It assumes that the kth largest eigenvalue of data covariance matrix grows proportionally to Nƒ¿k with unknown exponents 0
    Date: 2020–03
  4. By: Dräger, Lena; Nguyen, Duc Binh Benno; Prokopczuk, Marcel; Sibbertsen, Philipp
    Abstract: This paper examines long memory volatility in international stock markets. We show that long memory volatility is widespread in a panel dataset of eighty-two countries and that the degree of memory in the panel can be related to macroeconomic variables such as short- and long-run interest rates and unemployment. Moreover, we find that developed economies possess longer memory in volatility than emerging and frontier countries and that stock market jumps are negatively correlated with long memory of volatility. Overall, our results provide some evidence of a link between stock market uncertainty and macroeconomic conditions, which is prevalent across a large range of countries.
    Keywords: International; Long Memory; Volatility
    JEL: G15 C22 F30 F40
    Date: 2020–02

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