nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒08‒14
eight papers chosen by
Jaqueson K. Galimberti
Asian Development Bank

  1. The contribution of realized covariance models to the economic value of volatility timing By Bauwens, Luc; Xu, Yongdeng
  2. Local Projections for Applied Economics By Òscar Jordà
  3. Asymptotics for the Generalized Autoregressive Conditional Duration Model By Giuseppe Cavaliere; Thomas Mikosch; Anders Rahbek; Frederik Vilandt
  4. New asymptotics applied to functional coefficient regression and climate sensitivity analysis By Qiying Wang; Peter C. B. Phillips; Ying Wang
  5. Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods By Ioanna-Yvonni Tsaknaki; Fabrizio Lillo; Piero Mazzarisi
  6. Panel Data Models with Time-Varying Latent Group Structures By Yiren Wang; Peter C. B. Phillips; Liangjun Su
  7. Impulse Response Analysis at the Zero Lower Bound By Luca Benati; Thomas A. Lubik
  8. Measuring Cause-Effect with the Variability of the Largest Eigenvalue By Alejandro Rodriguez Dominguez; Irving Ramirez Carrillo; David Parraga Riquelme

  1. By: Bauwens, Luc ((Université catholique de Louvain, CORE, Belgium); Xu, Yongdeng (Cardiff Business School)
    Abstract: Realized covariance models specify the conditional expectation of a realized covariance matrix as a function of past realized covariance matrices through a GARCH-type structure. We compare the forecasting performance of several such models in terms of economic value, measured through economic loss functions, on two datasets. Our empirical results indicate that the (HEAVY-type) models that use realized volatilities yield economic value and significantly surpass the (GARCH) models that use only daily returns for daily and weekly horizons. Among the HEAVY-type models, for a dataset of twenty-nine stocks, those that are specified to capture the heterogeneity of the dynamics of the individual conditional variance processes and to allow these to differ from the correlation processes (namely, DCC-type models) are more beneficial than the models that impose the same dynamics to the variance and covariance processes (namely, BEKK-type models), whereas for the dataset of three assets, the different models perform similarly. Finally, using a directly rescaled intra-day covariance to estimate the full-day covariance provides more economic value than using the overnight returns, as the latter tend to yield noisy estimators of the overnight covariance, impairing their predictive capacity.
    Keywords: volatility timing, realized volatility, high-frequency data, forecasting
    JEL: G11 G17 C32 C58
    Date: 2023–07
  2. By: Òscar Jordà
    Abstract: The dynamic causal effect of an intervention on an outcome is of paramount interest to applied macro- and micro-economics research. However, this question has been generally approached differently by the two literatures. In making the transition from traditional time series methods to applied microeconometrics, local projections can serve as a natural bridge. Local projections can translate the familiar language of vector autoregressions (VARs) and impulse responses into the language of potential outcomes and treatment effects. There are gains to be made by both literatures from greater integration of well established methods in each. This review shows how to make these connections and points to potential areas of further research.
    Keywords: local projections; vector autoregressions; panel data; potential outcomes
    Date: 2023–07–14
  3. By: Giuseppe Cavaliere; Thomas Mikosch; Anders Rahbek; Frederik Vilandt
    Abstract: Engle and Russell (1998, Econometrica, 66:1127--1162) apply results from the GARCH literature to prove consistency and asymptotic normality of the (exponential) QMLE for the generalized autoregressive conditional duration (ACD) model, the so-called ACD(1, 1), under the assumption of strict stationarity and ergodicity. The GARCH results, however, do not account for the fact that the number of durations over a given observation period is random. Thus, in contrast with Engle and Russell (1998), we show that strict stationarity and ergodicity alone are not sufficient for consistency and asymptotic normality, and provide additional sufficient conditions to account for the random number of durations. In particular, we argue that the durations need to satisfy the stronger requirement that they have finite mean.
    Date: 2023–07
  4. By: Qiying Wang (University of Sydney); Peter C. B. Phillips (Cowles Foundation, Yale University); Ying Wang (Renmin University of China)
    Abstract: A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and the functional covariate are nonstationary. Simulations show finite sample performance matching well with the asymptotic theory and having broad relevance to applications, while revealing how dual nonstationarity in regressors and covariates raises sensitivity to bandwidth choice and the impact of dimensionality in nonparametric regression.
    Date: 2023–06
  5. By: Ioanna-Yvonni Tsaknaki; Fabrizio Lillo; Piero Mazzarisi
    Abstract: Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.
    Date: 2023–07
  6. By: Yiren Wang (Singapore Management University); Peter C. B. Phillips (Cowles Foundation, Yale University); Liangjun Su (Tsinghua University)
    Abstract: This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism the model may have different numbers of groups and/or different group memberships before and after the break. With the preliminary nuclear norm-regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. It is shown that the break point, the number of groups and the group memberships can each be estimated correctly with probability approaching one. Asymptotic distributions of the estimators of the slope coefficients are established. Monte Carlo simulations demonstrate excellent finite sample performance for the proposed estimation algorithm. An empirical application to real house price data across 377 Metropolitan Statistical Areas in the US from 1975 to 2014 suggests the presence both of structural breaks and of changes in group membership.
    Date: 2023–06
  7. By: Luca Benati; Thomas A. Lubik
    Abstract: We study whether the response of the economy to structural shocks changes at the zero lower bound. Monte Carlo evidence suggests that VARs have a limited ability to detect changes in impulse response functions at the ZLB compared to the standard environment with positive interest rates. This issue is confounded given the short sample lengths that characterize ZLB episodes. This is especially the case for timevarying parameter VARs, whose estimates are two-sided, and therefore tend to smooth changes across regimes. In contrast, fixed-coefficient VARs estimated by sub-sample exhibit greater power. Pooled estimates from panel VARs for six countries based on (long-run and) sign restrictions detect in several instances changes in the IRFs. This evidence is, however, weaker than it appears. Based on (long-run and) sign restrictions we find that prior and posterior IRFs are often close, so that the concern raised by Baumeister and Hamilton (2015) appears to be relevant. Evidence from a multivariate permanent-transitory decomposition of GDP shocks is markedly sharper. It points towards material changes in the IRFs: at the ZLB the IRFs of GDP and unemployment exhibit more inertia, the response of prices is flatter, and the responses of interest rates are weaker.
    Keywords: Zero Lower Bound; Bayesian VARs; structural VARs; monetary policy; sign restrictions
    JEL: C32 C52
    Date: 2023–06
  8. By: Alejandro Rodriguez Dominguez; Irving Ramirez Carrillo; David Parraga Riquelme
    Abstract: We present a method to test and monitor structural relationships between time variables. The distribution of the first eigenvalue for lagged correlation matrices (Tracy-Widom distribution) is used to test structural time relationships between variables against the alternative hypothesis (Independence). This distribution studies the asymptotic dynamics of the largest eigenvalue as a function of the lag in lagged correlation matrices. By analyzing the time series of the standard deviation of the greatest eigenvalue for $2\times 2$ correlation matrices with different lags we can analyze deviations from the Tracy-Widom distribution to test structural relationships between these two time variables. These relationships can be related to causality. We use the standard deviation of the first eigenvalue at different lags as a proxy for testing and monitoring structural causal relationships. The method is applied to analyse causal dependencies between daily monetary flows in a retail brokerage business allowing to control for liquidity risks.
    Date: 2023–07

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