nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒04‒20
eight papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A Truncated Mixture Transition Model for Interval-valued Time Series By Gloria Gonzalez-Rivera; Yun Luo
  2. Bias optimal vol-of-vol estimation: the role of window overlapping By Giacomo Toscano; Maria Cristina Recchioni
  3. Forecasts with Bayesian vector autoregressions under real time conditions By Michael Pfarrhofer
  4. Solving the Milk Addiction Paradox By Davide Dragone; Davide Raggi
  5. Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow By Ye-Sheen Lim; Denise Gorse
  6. Persistence in the Realized Betas: Some Evidence for the Spanish Stock Market By Guglielmo Maria Caporale; Luis A. Gil-Alana; Miguel Martin-Valmayor
  7. mFLICA: An R package for Inferring Leadership of Coordination From Time Series By Chainarong Amornbunchornvej
  8. An Analysis of the Seasonal Cycle and the Business Cycle By Emara, Noha; Ma, Jinpeng

  1. By: Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside); Yun Luo
    Abstract: We propose a model for interval-valued time series (ITS), e.g. the collection of daily intervals of high/low stock returns over time, that specifies the conditional joint distribution of the upper and lower bounds of the interval as a mixture of truncated bivariate normal distribution. This specification guarantees that the natural order of the interval (upper bound not smaller than lower bound) is preserved. The model also captures the potential conditional heteroscedasticity and non-Gaussian features in ITS. The standard EM algorithm, when applied to the estimation of mixture models with truncated distribution, does not provide a closed-form solution in M step. We propose a new EM algorithm that solves this problem. We establish the consistency of the maximum likelihood estimator. Monte Carlo simulations show the new EM algorithm has good convergence properties. We apply the model to the interval-valued IBM daily stock returns and it exhibits superior performance over competing methods.
    Keywords: interval-valued data, mixture transition model, EM algorithm, truncated normal distribution.
    JEL: C01 C32 C34
    Date: 2020–03
  2. By: Giacomo Toscano; Maria Cristina Recchioni
    Abstract: We derive a feasible criterion for the bias-optimal selection of the tuning parameters involved in estimating the integrated volatility of the spot volatility via the simple realized estimator by Barndorff-Nielsen and Veraart (2009). Our analytic results are obtained assuming that the spot volatility is a continuous mean-reverting process and that consecutive local windows for estimating the spot volatility are allowed to overlap in a finite sample setting. Moreover, our analytic results support some optimal selections of tuning parameters prescribed in the literature, based on numerical evidence. Interestingly, it emerges that the window-overlapping is crucial for optimizing the finite-sample bias of volatility-of-volatility estimates.
    Date: 2020–04
  3. By: Michael Pfarrhofer
    Abstract: This paper investigates the sensitivity of forecast performance measures to taking a real time versus pseudo out-of-sample perspective. We use monthly vintages for the United States (US) and the Euro Area (EA) and estimate a set of vector autoregressive (VAR) models of different sizes with constant and time-varying parameters (TVPs) and stochastic volatility (SV). Our results suggest differences in the relative ordering of model performance for point and density forecasts depending on whether real time data or truncated final vintages in pseudo out-of-sample simulations are used for evaluating forecasts. No clearly superior specification for the US or the EA across variable types and forecast horizons can be identified, although larger models featuring TVPs appear to be affected the least by missing values and data revisions. We identify substantial differences in performance metrics with respect to whether forecasts are produced for the US or the EA.
    Date: 2020–04
  4. By: Davide Dragone; Davide Raggi
    Abstract: The milk addiction paradox refers to an empirical finding in which commodities that are typically considered to be non addictive, such as milk, appear instead to be addictive. This result seems more likely when there is persistence in consumption and when using aggregate data, and it suggests that the AR(2) model typically used in the addiction literature is prone to produce spurious result in favor of rational addiction. Using both simulated and real data, we show that the milk addiction paradox disappears when estimating the data using an AR(1) linear specification that describes the saddle-path solution of the rational addiction model. The AR(1) specification is able to correctly discriminate between rational addiction and simple persistence in the data, to test for the main features of rational addiction, and to produce unbiased estimates of the short and long-run elasticity of demand. These results hold both with individual and aggregated data, and they suggest that, for testing rational addiction, the AR(1) model is a better empirical alternative than the canonical AR(2) model.
    JEL: D11 D12 I12 L66
    Date: 2020–04
  5. By: Ye-Sheen Lim; Denise Gorse
    Abstract: In this paper we propose a deep recurrent model based on the order flow for the stationary modelling of the high-frequency directional prices movements. The order flow is the microsecond stream of orders arriving at the exchange, driving the formation of prices seen on the price chart of a stock or currency. To test the stationarity of our proposed model we train our model on data before the 2017 Bitcoin bubble period and test our model during and after the bubble. We show that without any retraining, the proposed model is temporally stable even as Bitcoin trading shifts into an extremely volatile "bubble trouble" period. The significance of the result is shown by benchmarking against existing state-of-the-art models in the literature for modelling price formation using deep learning.
    Date: 2020–03
  6. By: Guglielmo Maria Caporale; Luis A. Gil-Alana; Miguel Martin-Valmayor
    Abstract: This paper examines the stochastic behaviour of the realized betas within the one-factor CAPM for the six companies with the highest market capitalization included in the Spanish IBEX stock market index. Fractional integration methods are applied to estimate their degree of persistence at the daily, weekly and monthly frequency over the period 1 January 2000 – 15 November 2018 using 1, 3 and 5-year samples. On the whole, the results indicate that the realized betas are highly persistent and do not exhibit mean-reverting behaviour. However, the findings are rather sensitive to the choice of frequency and time span (number of observations).
    Keywords: realized beta, CAPM, persistence, mean reversion, long memory
    JEL: C22 G11
    Date: 2020
  7. By: Chainarong Amornbunchornvej
    Abstract: Leadership is a process that leaders influence followers to achieve collective goals. One of special cases of leadership is the coordinated pattern initiation. In this context, leaders are initiators who initiate coordinated patterns that everyone follows. Given a set of individual-multivariate time series of real numbers, the mFLICA package provides a framework for R users to infer coordination events within time series, initiators and followers of these coordination events, as well as dynamics of group merging and splitting. The mFLICA package also has a visualization function to make results of leadership inference more understandable. The package is available on Comprehensive R Archive Network (CRAN) at A.
    Date: 2020–04
  8. By: Emara, Noha; Ma, Jinpeng
    Abstract: Robert Barsky and Jeffrey Miron (1989) revealed the seasonal cycle of the U.S. economy from 1948 to 1985 was characterized by a “bubble-like” expansion in the second and fourth quarters, a “crash-like” contraction in the first quarter, and a mild contraction in the third quarter. We replicate, in part, their seasonal cycle analysis from 1946 to 2001. Our results are largely in line with theirs. Nonetheless, we find the seasonal cycle is not stable and can evolve across time. In particular, the Great Moderation affected both the business cycle and the seasonal cycle. Robert Barsky and Jeffrey Miron also found real aggregates, like the output, move together in the seasonal cycle across broadly defined sectors, similar to a phenomenon observed under the conventional business cycle. They posed a challenge question concerning why “the seasonal and the conventional business cycles are so similar.” To answer their question, we focus on a number of aggregate variables with a recursive application of the HP filter and find that aggregates, such as the GDP, consumption, the S&P 500 Index, and so forth, have a “bubble-like” expansion and a “crash-like” contraction in their cyclical trends in business cycle frequencies. Although preference shifts and production synergy are the two major forces that drive the seasonal cycle, we find the time-varying stochastic discount factor is the main cause of the business cycle and plays a more important role in macroeconomic fluctuations in business cycle frequencies than other factors.
    Keywords: Seasonal cycles, business cycles, jobless claims, unemployment rates, labor productivity, GDP, S&P 500
    JEL: C53 C82 E24
    Date: 2019

This nep-ets issue is ©2020 by Jaqueson K. Galimberti. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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