nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒05‒17
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Gaussian-Chain Filters for Heavy-Tailed Noise with Application to Detecting Big Buyers and Big Sellers in Stock Market By Li-Xin Wang
  2. Quantum Brownian motion model for stock markets By Xiangyi Meng; Jian-Wei Zhang; Hong Guo
  3. The finite-sample size of the BDS test for GARCH standardized residuals By Fernandes, Marcelo; Preumont, Pierre-Yves
  4. Stochastic Volatility Estimation with GPU Computing By António Alberto Santos; João Andrade
  5. Markov-Switching Quantile Autoregression By Liu, Xiaochun
  6. Forecasting Time-Varying Correlation using the Dynamic Conditional Correlation (DCC) Model By Mapa, Dennis S.; Paz, Nino Joseph I.; Eustaquio, John D.; Mindanao, Miguel Antonio C.
  7. Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models By Albis, Manuel Leonard F.; Mapa, Dennis S.

  1. By: Li-Xin Wang
    Abstract: We propose a new heavy-tailed distribution --- Gaussian-Chain (GC) distribution, which is inspirited by the hierarchical structures prevailing in social organizations. We determine the mean, variance and kurtosis of the Gaussian-Chain distribution to show its heavy-tailed property, and compute the tail distribution table to give specific numbers showing how heavy is the heavy-tails. To filter out the heavy-tailed noise, we construct two filters --- 2nd and 3rd-order GC filters --- based on the maximum likelihood principle. Simulation results show that the GC filters perform much better than the benchmark least-squares algorithm when the noise is heavy-tail distributed. Using the GC filters, we propose a trading strategy, named Ride-the-Mood, to follow the mood of the market by detecting the actions of the big buyers and the big sellers in the market based on the noisy, heavy-tailed price data. Application of the Ride-the-Mood strategy to five blue-chip Hong Kong stocks over the recent two-year period from April 2, 2012 to March 31, 2014 shows that their returns are higher than the returns of the benchmark Buy-and-Hold strategy and the Hang Seng Index Fund.
    Date: 2014–05
  2. By: Xiangyi Meng; Jian-Wei Zhang; Hong Guo
    Abstract: We investigate the relevance between quantum open systems and stock markets. A Quantum Brownian motion model is proposed for studying the interaction between the Brownian system and the reservoir, i.e., the stock index and the entire stock market. Based on the model, we investigate the Shanghai Stock Exchange of China from perspective of quantum statistics, and thereby examine the behaviors of the stock index violating the efficient market hypothesis, such as fat-tail phenomena and non-Markovian features. Our interdisciplinary works thus help to discovery the underlying quantum characteristics of stock markets and develop new research fields of econophysics.
    Date: 2014–05
  3. By: Fernandes, Marcelo; Preumont, Pierre-Yves
    Abstract: This paper uses a multivariate response surface methodology to analyze the size distortion of the BDS test when applied to standardized residuals of rst-order GARCH processes.The results show that the asymptotic standard normal distribution is an unreliable approximation, even in large samples. On the other hand, a simple log-transformation of the squared standardized residuals seems to correct most of the size problems. Nonethe-less, the estimated response surfaces can provide not only a measure of the size distortion, but also more adequate critical values for the BDS test in small samples.
    Date: 2014–05–05
  4. By: António Alberto Santos (Faculty of Economics, University of Coimbra and GEMF, Portugal); João Andrade (Instituto de Telecomunicações, Dept. Electrical and Comp. Eng., University of Coimbra, Portugal)
    Abstract: In this paper, we show how to estimate the parameters of stochastic volatility models using Bayesian estimation and Markov chain Monte Carlo (MCMC) simulations through the approximation of the a-posteriori distribution of parameters. Simulated independent draws are made possible by using Graphics Processing Units (GPUs) to compute several Markov chains in parallel. We show that the higher computational power of GPUs can be harnessed and put to good use by addressing two challenges. Bayesian estimation using MCMC simulations benefit from powerful processors since it is a complex numerical problem. Moreover, sequential approaches are characterized for drawing highly correlated samples which reduces the Effective Sample Size (ESS) associated with the simulated values obtained from the posterior distribution under a Bayesian analysis. However, under the proposed parallel expression of the algorithm, we show that a faster convergence rate is possible by running independent Markov chains, drawing lower correlations and therefore increase the ESS. The results obtained with this approach are presented for the Stochastic Volatility (SV) model, basic and with leverage.
    Keywords: Bayesian Estimation; Graphics Processing Unit; Parallel Computing; Simulation; State-Space Models; Stochastic Volatility.
    JEL: C11 C13 C15 C53 C63 C87
    Date: 2014–04
  5. By: Liu, Xiaochun
    Abstract: This paper considers the location-scale quantile autoregression in which the location and scale parameters are subject to regime shifts. The regime changes are determined by the outcome of a latent, discrete-state Markov process. The new method provides direct inference and estimate for different parts of a nonstationary time series distribution. Bayesian inference for switching regimes within a quantile,via a three-parameter asymmetric-Laplace distribution, is adapted and designed for parameter estimation. The simulation study shows reasonable accuracy and precision in model estimation. From a distribution point of view, rather than from a mean point of view, the potential of this new approach is illustrated in the empirical applications to reveal the countercyclical risk pattern of stock markets and the asymmetric persistence of real GDP growth rates and real trade-weighted exchange rates.
    Keywords: Asymmetric-Laplace Distribution, Metropolis-Hastings, Block-at-a-Time, Asymmetric Dynamics, Transition Probability
    JEL: C51 C58 E0 E3 E32 G1
    Date: 2013–10–07
  6. By: Mapa, Dennis S.; Paz, Nino Joseph I.; Eustaquio, John D.; Mindanao, Miguel Antonio C.
    Abstract: Hedging strategies have become more and more complicated as assets being traded have become more interrelated to each other. Thus, the estimation of risks for optimal hedging does not involve only the quantification of individual volatilities but also include their pairwise correlations. Therefore a model to capture the dynamic relationships is necessary to estimate and forecast correlations of returns through time. Engle’s dynamic conditional correlation (DCC) model is compared with other models of correlation. Performance of the correlation models are evaluated in this paper using only the daily log returns of the closing prices of the Peso-Dollar Exchange Rate and Philippine Stock Exchange index. Ultimately, Engle’s DCC model is adopted because of its consistency with expectations. Though generally negative, correlation between these two returns is not really constant as the results indicated. The forecast evaluation of the models was divided into in-sample and out-of-sample forecast performance with short-term (i.e., 22-day, 60-day, and 125-day) and medium-term (250-day and 500-day) rolling window correlations, or realized correlations, as proxies for the actual correlation. Based on the root mean squared error and mean absolute error, the integrated DCC model showed optimal forecast performance for the in-sample correlation patterns while the mean-reverting DCC model had the most desirable forecast properties for dynamic long-run forecasts. Also, the Diebold-Mariano tests showed that the integrated DCC has greater predictive accuracy in terms of the 3-month realized correlations than the rest of the models.
    Keywords: dynamic conditional correlation, Peso-Dollar exchange rate, PSE index, hedging
    JEL: C5 C52 C58 E47
    Date: 2014
  7. By: Albis, Manuel Leonard F.; Mapa, Dennis S.
    Abstract: The estimated Vector AutoRegressive (VAR) model is sensitive to model misspecifications, such as omitted variables, incorrect lag-length, and excluded moving average terms, which results in biased and inconsistent parameter estimates. Furthermore, the symmetric VAR model is more likely misspecified due to the assumption that variables in the VAR have the same level of endogeneity. This paper extends the Bayesian Averaging of Classical Estimates, a robustness procedure in cross-section data, to a vector time-series that is estimated using a large number of Asymmetric VAR models, in order to achieve robust results. The combination of the two procedures is deemed to minimize the effects of misspecification errors by extracting and utilizing more information on the interaction of the variables, and cancelling out the effects of omitted variables and omitted MA terms through averaging. The proposed procedure is applied to simulated data from various forms of model misspecifications. The forecasting accuracy of the proposed procedure was compared to an automatically selected equal lag-length VAR. The results of the simulation suggest that, under misspecification problems, particularly if an important variable and MA terms are omitted, the proposed procedure is better in forecasting than the automatically selected equal lag-length VAR model.
    Keywords: BACE, AVAR, Robustness Procedures
    JEL: C5 C52 C58
    Date: 2014

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