nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒01‒24
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A parsimonious test of constancy of a positive definite correlation matrix in a multivariate time-varying GARCH model By Jian Kang; Johan Stax Jakobsen; Annastiina Silvennoinen; Timo Teräsvirta; Glen Wade
  2. Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach By Joshua C. C. Chan; Aubrey Poon; Dan Zhu
  3. Fractional integration and cointegration By Javier Haulde; Morten Ørregaard Nielsen
  4. Volatility of volatility estimation: central limit theorems for the Fourier transform estimator and empirical study of the daily time series stylized facts By Giulia Livieri; Maria Elvira Mancino; Stefano Marmi; Giacomo Toscano
  5. Stationarity analysis of the stock market data and its application to mechanical trading By Kazuki Kanehira; Norikazu Todoroki

  1. By: Jian Kang (School of Finance, Dongbei University of Finance and Economics); Johan Stax Jakobsen (Copenhagen Business School and CREATES); Annastiina Silvennoinen (NCER, Queensland University of Technology); Timo Teräsvirta (Aarhus University, CREATES, C.A.S.E, Humboldt-Universität zu Berlin); Glen Wade (NCER, Queensland University of Technology)
    Abstract: We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example is given.
    Keywords: Deterministically varying correlation, multiplicative time-varying GARCH, multivariate GARCH, nonstationary volatility, smooth transition GARCH
    JEL: C32 C52 C58
    Date: 2022–01–01
  2. By: Joshua C. C. Chan; Aubrey Poon; Dan Zhu
    Abstract: State-space mixed-frequency vector autoregressions are now widely used for nowcasting. Despite their popularity, estimating such models can be computationally intensive, especially for large systems with stochastic volatility. To tackle the computational challenges, we propose two novel precision-based samplers to draw the missing observations of the low-frequency variables in these models, building on recent advances in the band and sparse matrix algorithms for state-space models. We show via a simulation study that the proposed methods are more numerically accurate and computationally efficient compared to standard Kalman-filter based methods. We demonstrate how the proposed method can be applied in two empirical macroeconomic applications: estimating the monthly output gap and studying the response of GDP to a monetary policy shock at the monthly frequency. Results from these two empirical applications highlight the importance of incorporating high-frequency indicators in macroeconomic models.
    Date: 2021–12
  3. By: Javier Haulde (Department of Economics, Universidad Pública de Navarra); Morten Ørregaard Nielsen (Aarhus University, Department of Economics and Business Economics and CREATES)
    Abstract: In this chapter we present an overview of the main ideas and methods in the fractional integration and cointegration literature. We do not attempt to give a complete survey of this enormous literature, but rather a more introductory treatment suitable for a researcher or graduate student wishing to learn about this exciting field of research. With this aim, we have surely overlooked many relevant references for which we apologize in advance. Knowledge of standard time series methods, and in particular methods related to nonstationary time series, at the level of a standard graduate course or advanced undergraduate course is assumed.
    Keywords: Arfima model, cofractional, cointegration, fractional Brownian motion, fractional integration, long memory, long-range dependence, nonstationary, strong dependence
    JEL: C22 C32
    Date: 2022–01–10
  4. By: Giulia Livieri; Maria Elvira Mancino; Stefano Marmi; Giacomo Toscano
    Abstract: We study the asymptotic normality of two estimators of the integrated volatility of volatility based on the Fourier methodology, which does not require the pre-estimation of the spot volatility. We show that the bias-corrected estimator reaches the optimal rate 1/4, while the estimator without bias-correction has a slower convergence rate and a smaller asymptotic variance. Additionally, we provide simulation results that support the theoretical asymptotic distribution of the rate-efficient estimator and show the accuracy of the Fourier estimator in comparison with a rate-optimal estimator based on the pre-estimation of the spot volatility. Finally, we reconstruct the daily volatility of volatility of the S&P500 and EUROSTOXX50 indices over long samples via the rate-optimal Fourier estimator and provide novel insight into the existence of stylized facts about its dynamics.
    Date: 2021–12
  5. By: Kazuki Kanehira; Norikazu Todoroki
    Abstract: This study proposes a scheme for stationarity analysis of stock price fluctuations based on KM$_2$O-Langevin theory. Using this scheme, we classify the time-series data of stock price fluctuations into three periods: stationary, non-stationary, and intermediate. We then suggest an example of a low-risk stock trading strategy to demonstrate the usefulness of our scheme by using actual stock index data. Our strategy uses a trend-based indicator, moving averages, for stationary periods and an oscillator-based indicator, psychological lines, for non-stationary periods to make trading decisions. Finally, we confirm that our strategy is a safe trading strategy with small maximum drawdown by back testing on the Nikkei Stock Average. Our study, the first to apply the stationarity analysis of KM$_2$O-Langevin theory to actual mechanical trading, opens up new avenues for stock price prediction.
    Date: 2021–12

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