nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒04‒15
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Forecasting Australian Macroeconomic Variables Using a Large Dataset By Sarantis Tsiaplias; Chew Lian Chua
  2. Change in persistence tests for panels: An update and some new results By Cerqueti, Roy; Costantini, Mauro; Gutierrez, Luciano
  3. Testing fractional order of long memory processes : a Monte Carlo study. By Laurent Ferrara; Dominique Guegan; Zhiping Lu
  4. A multi-horizon scale for volatility. By Alexander Subbotin
  5. Do survey indicators let us see the business cycle ? A frequency decomposition By Luc Dresse; Christophe Van Nieuwenhuyze
  6. Are Spectral Estimators Useful for Implementing Long-Run Restrictions in SVARs? By Elmar Mertens;
  7. Forecasting Realized Volatility: A Bayesian Model Averaging Approach By Chun Liu; John M Maheu
  8. A Hull and White formula for a general stochastic volatility jump-diffusion model with applications to the study of the short-time behavior of the implied volatility By Elisa Alòs; Jorge A. León; Monique Pontier; Josep Vives
  9. Modeling International Financial Returns with a Multivariate Regime Switching Copula By Chollete, Loran; Heinen, Andreas; Valdesogo, Alfonso
  10. Optimal Linear Filtering, Smoothing and Trend Extraction for Processes with Unit Roots and Cointegration By Dimitrios Thomakos
  11. A Note on Optimal Linear Filtering, Smoothing and Trend Extraction for Processes with Unit Roots with Drift By Dimitrios Thomakos

  1. By: Sarantis Tsiaplias (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne); Chew Lian Chua
    Abstract: This paper investigates the forecasting performance of the diffusion index approach for the Australian economy, and considers the forecasting performance of the diffusion index approach relative to composite forecasts. Weighted and unweighted factor forecasts are benchmarked against composite forecasts, and forecasts derived from individual forecasting models. The results suggest that diffusion index forecasts tend to improve on the benchmark AR forecasts. We also observe that weighted factors tend to produce better forecasts than their unweighted counterparts. We find, however, that the size of the forecasting improvement is less marked than previous research, with the diffusion index forecasts typically producing mean square errors of a similar magnitude to the VAR and BVAR approaches. JEL Classification: C22; C53; E17
    Keywords: PDiffusion indexes; Forecasting; Australia.
    Date: 2008–02
  2. By: Cerqueti, Roy; Costantini, Mauro; Gutierrez, Luciano
    Abstract: In this paper we propose a set of new panel tests to detect changes in persistence. The test statistics are used to test the null hypothesis of stationarity against the alternative of a change in persistence from I(0) to I(1), from I(1) to I(0) and in an unknown direction. The limiting distributions of the panel tests are derived, and small sample properties are investigated by Monte Carlo experiments under the hypothesis that the individual series are independently cross-section distributed. These tests have a good size and power properties. Cross-sectional dependence is also considered. A procedure of de-factorizing, proposed by Stock and Watson (2002), is applied. The defactored panel tests have good size and power. The empirical results obtained from applying these tests to a panel covering 21 OECD countries observed between 1970 and 2007 suggest that inflation rate changes from I(1) to I(0) when cross-correlation is considered.
    Keywords: Persistence, Stationarity, Panel data
    JEL: C12 C23
    Date: 2008–03–31
  3. By: Laurent Ferrara (Centre d'Economie de la Sorbonne et DGEI-DAMEP, Banque de France); Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics); Zhiping Lu (Centre d'Economie de la Sorbonne et East China Normal University)
    Abstract: Testing the fractionally integrated order of seasonal and non-seasonal unit roots is quite important for the economic and financial time series modelling. In this paper, Robinson test (1994) is applied to various well-known long memory models. Via Monte Carlo experiments, we study and compare the performances of this test using several sample sizes.
    Keywords: Long memory processes, test, Monte Carlo simulations.
    JEL: C12 C15 C22
    Date: 2008–02
  4. By: Alexander Subbotin (Centre d'Economie de la Sorbonne et Higher School of Economics (Moscow))
    Abstract: We decompose volatility of a stock market index both in time and scale using wavelet filters and design a probabilistic indicator for valatilities, analogous to the Richter scale in geophysics. The peak-over-threshold method is used to fit the generalized Pareto probability distribution for the extreme values in the realized variances of wavelet coefficients. The indicator is computed for the daily Dow Jones Industrial Averages index data from 1986 to 2007 and for the intraday CAC 40 data from 1995 to 2006. The results are used for comparison and structural multi-resolution analysis of extreme events on the stock market and for the detection of financial crises.
    Keywords: Stock market, volatility, wavelets, multi-resolution analysis, financial crisis.
    JEL: G10 G14
    Date: 2008–03
  5. By: Luc Dresse (National Bank of Belgium, Research Department); Christophe Van Nieuwenhuyze (National Bank of Belgium, Research Department)
    Abstract: This paper uses a frequency domain approach to gain insight into the correlation between survey indicators and year-on-year GDP growth. Using the Baxter-King filter, we split up each series into three components: a short-term, a business cycle (oscillations between 18 and 96 months) and a long-term component. We then calculate how much of the variation of the survey series and GDP growth can be ascribed to these different components. Finally, we use this information together with an analysis of the correlation between survey indicators and year-on-year GDP growth at the different frequencies to explain their overall correlation. We show that survey indicators, similar to year-on-year GDP growth, do not perfectly reflect business cycle movements but contain cycles of other frequencies. Long-term cycles, in particular, are a nontrivial part of the series' variance. Furthermore, there exist some clear relations between the weight of these cycles in the survey indicators and their correlation with GDP growth. In general, the larger the business cycle component, the larger the correlation, while the opposite is true for the short-term component. The evidence for the long-term component is mixed: although a long-term component seems necessary as the correlation at this frequency is the highest, strong or weak long-term components are typically idiosyncratic, dragging down the overall correlation between the indicator and year-on-year GDP growth. The paper applies this methodology to the euro area countries (EC survey indicators) and to Belgium separately (NBB business survey indicators). The results are highly comparable
    Keywords: Baxter-King, spectral analysis, survey indicators, correlation
    JEL: C22 E32
    Date: 2008–03
  6. By: Elmar Mertens (Study Center Gerzensee and University of Lausanne);
    Abstract: No, not really. Responding to lingering concerns about the reliability of SVARs, Christiano et al (NBER Macro Annual, 2006, "CEV") propose to combine OLS estimates of a VAR with a spectral estimate of long-run variance. In principle, this could help alleviate specification problems of SVARs in identifying long-run shocks. But in practice, spectral estimators suffer from small sample biases similar to those from VARs. Moreover, the spectral estimates contain information about serial correlation in VAR residuals and the VAR dynamics must be adjusted accordingly. Otherwise, a naive application of the CEV procedure would misrepresent the data's variance.
    Date: 2008–03
  7. By: Chun Liu; John M Maheu
    Abstract: How to measure and model volatility is an important issue in finance. Recent research uses high frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive (HAR) specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility.
    Keywords: power variation, bipower variation, Gibbs sampling, model risk
    JEL: C11 C22 G12
    Date: 2008–04–03
  8. By: Elisa Alòs; Jorge A. León; Monique Pontier; Josep Vives
    Abstract: In this paper, generalizing results in Alòs, León and Vives (2007b), we see that the dependence of jumps in the volatility under a jump-diffusion stochastic volatility model, has no effect on the short-time behaviour of the at-the-money implied volatility skew, although the corresponding Hull and White formula depends on the jumps. Towards this end, we use Malliavin calculus techniques for Lévy processes based on Løkka (2004), Petrou (2006), and Solé, Utzet and Vives (2007).
    Keywords: Hull and White formula, Malliavin calculus, Ito’s formula for the Skorohod integral, jumpdiffusion stochastic volatility models
    JEL: G12 G13
    Date: 2008–04
  9. By: Chollete, Loran; Heinen, Andreas; Valdesogo, Alfonso
    Abstract: In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.
    Keywords: Asymmetric dependence; Canonical vine copula; International returns; Regime-Switching; Risk Management; Value-at-Risk.
    JEL: C32 G1 C35
    Date: 2008–02
  10. By: Dimitrios Thomakos
    Abstract: In this paper I propose a novel optimal linear filter for smoothing, trend and signal extraction for time series with a unit root. The filter is based on the Singular Spectrum Analysis (SSA) methodology, takes the form of a particular moving average and is different from other linear filters that have been used in the existing literature. To best of my knowledge this is the first time that moving average smoothing is given an optimality justification for use with unit root processes. The frequency response function of the filter is examined and a new method for selecting the degree of smoothing is suggested. I also show that the filter can be used for successfully extracting a unit root signal from stationary noise. The proposed methodology can be extended to also deal with two cointegrated series and I show how to estimate the cointegrating coefficient using SSA and how to extract the common stochastic trend component. A simulation study explores some of the characteristics of the filter for signal extraction, trend prediction and cointegration estimation for univariate and bivariate series. The practical usefulness of the method is illustrated using data for the US real GDP and two financial time series.
    Keywords: cointegration, forecasting, linear filtering, singular spectrum analysis, smoothing, trend extraction and prediction, unit root.
    Date: 2008
  11. By: Dimitrios Thomakos
    Abstract: In this note I show that the method proposed in Thomakos (2008) for optimal linear filtering, smoothing and trend extraction for a unit root process can be applied with no changes when a drift parameter is added to the process. The method in the aforementioned paper is based on Singular Spectrum Analysis (SSA) and here I also derive an SSA-based consistent estimator of the drift parameter.
    Keywords: drift, forecasting, linear filtering, singular spectrum analysis, smoothing, trend extraction and prediction, unit root.
    Date: 2008

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