nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒03‒10
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Modified Profile Likelihood Inference and Interval Forecast of the Burst of Financial Bubbles By Vladimir Filimonov; Guilherme Demos; Didier Sornette
  2. Sparse Change-Point Time Series Models By Dufays, A.; Rombouts, V.
  3. A dynamic component model for forecasting high-dimensional realized covariance matrices By BAUWENS, L.; BRAIONE, M.; STORTI, G.
  4. Determining the number of factors after stationary univariate transformations By Francisco Corona; María Pilar Poncela; Esther Ruiz
  5. Forecasting Daily Stock Volatility Using GARCH-CJ Type Models with Continuous and Jump Variation By BOUSALAM, Issam; HAMZAOUI, Moustapha; ZOUHAYR, Otman
  6. New Distribution Theory for the Estimation of Structural Break Point in Mean By Jiang Liang; Wang Xiaohu; Jun Yu

  1. By: Vladimir Filimonov; Guilherme Demos; Didier Sornette
    Abstract: We present a detailed methodological study of the application of the modified profile likelihood method for the calibration of nonlinear financial models characterised by a large number of parameters. We apply the general approach to the Log-Periodic Power Law Singularity (LPPLS) model of financial bubbles. This model is particularly relevant because one of its parameters, the critical time $t_c$ signalling the burst of the bubble, is arguably the target of choice for dynamical risk management. However, previous calibrations of the LPPLS model have shown that the estimation of $t_c$ is in general quite unstable. Here, we provide a rigorous likelihood inference approach to determine $t_c$, which takes into account the impact of the other nonlinear (so-called "nuisance") parameters for the correct adjustment of the uncertainty on $t_c$. This provides a rigorous interval estimation for the critical time, rather than a point estimation in previous approaches. As a bonus, the interval estimations can also be obtained for the nuisance parameters ($m,\omega$, damping), which can be used to improve filtering of the calibration results. We show that the use of the modified profile likelihood method dramatically reduces the number of local extrema by constructing much simpler smoother log-likelihood landscapes. The remaining distinct solutions can be interpreted as genuine scenarios that unfold as the time of the analysis flows, which can be compared directly via their likelihood ratio. Finally, we develop a multi-scale profile likelihood analysis to visualize the structure of the financial data at different scales (typically from 100 to 750 days). We test the methodology successfully on synthetic price time series and on three well-known historical financial bubbles.
    Date: 2016–02
  2. By: Dufays, A. (Université catholique de Louvain, CORE, Belgium); Rombouts, V. (ESSEC Business School)
    Abstract: Change-point time series specifications constitute flexible models that capture unknown structural changes by allowing for switches in the model parameters. Nevertheless most models suffer from an over-parametrization issue since typically only one latent state vari- able drives the breaks in all parameters. This implies that all parameters have to change when a break happens. We introduce sparse change-point processes, a new approach for detecting which parameters change over time. We propose shrinkage prior distributions allowing to control model parsimony by limiting the number of parameters which evolve from one structural break to another. We also give clear rules with respect to the choice of the hyper parameters of the new prior distributions. Well-known applications are re-visited to emphasize that many popular breaks are, in fact, due to a change in only a subset of the model parameters. It also turns out that sizeable forecasting improvements are made over recent change-point models.
    Keywords: Time series, Shrinkage prior, Change-point model, Online forecasting
    JEL: C11 C15 C22 C51
    Date: 2015–07–10
  3. By: BAUWENS, L. (Université catholique de Louvain, CORE, Belgium); BRAIONE, M. (Université catholique de Louvain, CORE, Belgium); STORTI, G. (Université catholique de Louvain, CORE, Belgium)
    Abstract: The Multiplicative MIDAS Realized DCC (MMReDCC) model of Bauwens et al. [5] decomposes the dynamics of the realized covariance matrix of returns into short-run transitory and long-run secular components where the latter reflects the effect of the continuously changing economic conditions. The model allows to obtain positive-definite forecasts of the realized covariance matrices but, due to the high number of parameters involved, estimation becomes unfeasible for large cross-sectional dimensions. Our contribution in this paper is twofold. First, in order to obtain a computationally feasible estimation procedure, we propose an algorithm that relies on the maximization of an iteratively re-computed moment-based profile likelihood function. We assess the finite sample properties of the proposed algorithm via a simulation study. Second, we propose a bootstrap procedure for generating multi-step ahead forecasts from the MMReDCC model. In an empirical application on realized covariance matrices for fifty equities, we find that the MMReDCC not only statistically outperforms the selected benchmarks in-sample, but also improves the out-of-sample ability to generate accurate multi-step ahead forecasts of the realized covariances.
    Keywords: Realized covariance, dynamic component models, multi-step forecasting, MIDAS, targeting, model confidence set
    Date: 2016–02–01
  4. By: Francisco Corona; María Pilar Poncela; Esther Ruiz
    Abstract: A very common practice when extracting factors from non-stationary multivariate timeseries is to differentiate each variable in the system. As a consequence, the ratiobetween variances and the dynamic dependence of the common and idiosyncraticdifferentiated components may change with respect to the original components. In thispaper, we analyze the effects of these changes on the finite sample properties of somepopular procedures to determine the number of factors. In particular, we consider theinformation criteria of Bai and Ng (2002), the edge distribution of Onastki (2010) andthe ratios of eigenvalues proposed by Ahn and Horenstein (2013). The performance ofthese procedures when implemented to differentiated variables depend on both theratios between variances and dependences of the differentiated factor and idiosyncraticnoises. Furthermore, we also analyze the role of the number of factors in the originalnon-stationary system as well as of its temporal and cross-sectional dimensions.
    Keywords: Dynamic Factor Model , Eigenvalue ratios , Edge distribution , Information criteria , Principal Components factor extraction
  5. By: BOUSALAM, Issam; HAMZAOUI, Moustapha; ZOUHAYR, Otman
    Abstract: In this paper we decompose the realized volatility of the GARCH-RV model into continuous sample path variation and discontinuous jump variation to provide a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. By using 5-minute high-frequency data of MASI Index in Morocco for the period (January 15, 2010 - January 29, 2016), we estimate parameters of the constructed GARCH and EGARCH-type models (namely, GARCH, GARCH-RV, GARCH-CJ, EGARCH, EGARCH-RV, and EGARCH-CJ) and evaluate their predictive power to forecast future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility while the discontinuous jump variation contains relatively less information for forecasting volatility. More interestingly, the findings show that the GARCH-CJ-type models have stronger predictive power for future volatility than the other two types of models. These results have a major contribution in financial practices such as financial derivatives pricing, capital asset pricing, and risk measures.
    Keywords: GARCH-CJ; Jumps variation; Realized volatility; MASI Index; Morocco.
    JEL: C22 F37 F47 G17
    Date: 2016–01–20
  6. By: Jiang Liang (Singapore Management University); Wang Xiaohu (The Chinese University of Hong Kong); Jun Yu (Singapore Management University)
    Abstract: Based on the Girsanov theorem, this paper rst obtains the exact distribution of the maximum likelihood estimator of structural break point in a continuous time model. The exact distribution is asymmetric and tri-modal, indicating that the estimator is seriously biased. These two properties are also found in the nite sample distribution of the least squares estimator of structural break point in the discrete time model. The paper then builds a continuous time approximation to the discrete time model and develops an in- ll asymptotic theory for the least squares estimator. The obtained in- ll asymptotic distribution is asymmetric and tri-modal and delivers good approximations to the nite sample distribution. In order to reduce the bias in the estimation of both the continuous time model and the discrete time model, a simulation-based method based on the indirect estima- tion approach is proposed. Monte Carlo studies show that the indirect estimation method achieves substantial bias reductions. However, since the binding function has a slope less than one, the variance of the indirect estimator is larger than that of the original estimator.
    Keywords: Structural break, Bias reduction, Indirect estimation, Exact distribution, In- ll asymptotics
    JEL: C11 C46
    Date: 2016–01

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