Testing stationarity of functional time series

Economic and financial data often take the form of a collection of curves observed consecutively over time. Examples include, intraday price curves, yield and term structure curves, and intraday volatility curves. Such curves can be viewed as a time series of functions. A fundamental issue that must be addressed, before an attempt is made to statistically model such data, is whether these curves, perhaps suitably transformed, form a stationary functional time series. This paper formalizes the assumption of stationarity in the context of functional time series and proposes several procedures to test the null hypothesis of stationarity. The tests are nontrivial extensions of the broadly used tests in the KPSS family. The properties of the tests under several alternatives, including change-point and I(1), are studied, and new insights, present only in the functional setting are uncovered. The theory is illustrated by a small simulation study and an application to intraday price curves.

[1]  D. Andrews Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation , 1991 .

[2]  M. Hallin,et al.  Dynamic functional principal components , 2015 .

[3]  Siegfried Hörmann,et al.  Functional Time Series , 2012 .

[4]  James O. Ramsay,et al.  Functional Data Analysis , 2005 .

[5]  Denis Bosq,et al.  Linear Processes in Function Spaces , 2000 .

[6]  A. Antoniadis,et al.  A functional wavelet–kernel approach for time series prediction , 2006 .

[7]  D. Bosq Linear Processes in Function Spaces: Theory And Applications , 2000 .

[8]  Donald W. K. Andrews,et al.  An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator , 1992 .

[9]  M. B. Priestley,et al.  A Test for Non‐Stationarity of Time‐Series , 1969 .

[10]  Pranab Kumar Sen,et al.  Rank tests for short memory stationarity , 2013 .

[11]  Victor M. Panaretos,et al.  Cramér–Karhunen–Loève representation and harmonic principal component analysis of functional time series , 2013 .

[12]  W. Fuller,et al.  LIKELIHOOD RATIO STATISTICS FOR AUTOREGRESSIVE TIME SERIES WITH A UNIT ROOT , 1981 .

[13]  Piotr Kokoszka,et al.  Testing the stability of the functional autoregressive process , 2010, J. Multivar. Anal..

[14]  Siegfried Hörmann,et al.  A FUNCTIONAL VERSION OF THE ARCH MODEL , 2011, Econometric Theory.

[15]  U. Grenander,et al.  Statistical Spectral Analysis of Time Series Arising from Stationary Stochastic Processes , 1953 .

[16]  Katharine Hayhoe,et al.  Testing the structural stability of temporally dependent functional observations and application to climate projections , 2011 .

[17]  X. Shao,et al.  Asymptotic spectral theory for nonlinear time series , 2006, math/0611029.

[18]  R. Leipus,et al.  Rescaled variance and related tests for long memory in volatility and levels , 2003 .

[19]  H. Müller,et al.  Functional data analysis for volatility , 2011 .

[20]  Haipeng Shen,et al.  Functional dynamic factor models with application to yield curve forecasting , 2012, 1209.6172.

[21]  Piotr Kokoszka,et al.  Inference for Functional Data with Applications , 2012 .

[22]  P. Kokoszka,et al.  Predictability of Shapes of Intraday Price Curves , 2013 .

[23]  Kristian Jonsson Testing Stationarity in Small‐ and Medium‐Sized Samples When Disturbances are Serially Correlated , 2011 .

[24]  C. Granger,et al.  Spectral Analysis for Economic Time Series , 1964 .

[25]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[26]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

[27]  Yazhen Wang,et al.  VAST VOLATILITY MATRIX ESTIMATION FOR HIGH-FREQUENCY FINANCIAL DATA , 2010, 1002.4754.

[28]  Piotr Kokoszka,et al.  Detecting changes in the mean of functional observations , 2009 .

[29]  B. M. Pötscher,et al.  Dynamic Nonlinear Econometric Models , 1997 .

[30]  D. Politis HIGHER-ORDER ACCURATE, POSITIVE SEMIDEFINITE ESTIMATION OF LARGE-SAMPLE COVARIANCE AND SPECTRAL DENSITY MATRICES , 2005, Econometric Theory.

[31]  Alexei Onatski,et al.  Curve Forecasting by Functional Autoregression , 2008 .

[32]  Anestis Antoniadis,et al.  Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes , 2003 .

[33]  J. O. Ramsay,et al.  Functional Data Analysis (Springer Series in Statistics) , 1997 .

[34]  Piotr Kokoszka,et al.  Tests for Error Correlation in the Functional Linear Model , 2010 .

[35]  P. Phillips BOOTSTRAPPING I(1) DATA BY PETER C. B. PHILLIPS COWLES FOUNDATION PAPER NO. 1310 COWLES FOUNDATION FOR RESEARCH IN ECONOMICS , 2010 .

[36]  D. Politis Adaptive bandwidth choice , 2003 .

[37]  N. Shephard,et al.  Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics , 2004 .

[38]  J. Kiefer,et al.  K-Sample Analogues of the Kolmogorov-Smirnov and Cramer-V. Mises Tests , 1959 .

[39]  Victor M. Panaretos,et al.  Fourier analysis of stationary time series in function space , 2013, 1305.2073.

[40]  J. Doob Stochastic processes , 1953 .

[41]  A. Lo Long-Term Memory in Stock Market Prices , 1989 .

[42]  Kristian Jönsson Finite-Sample Stability of the KPSS Test , 2006 .

[43]  P. Schmidt,et al.  A robust version of the KPSS test based on indicators , 2007 .

[44]  D. Dickey,et al.  Testing for unit roots in autoregressive-moving average models of unknown order , 1984 .

[45]  E. Kandel,et al.  Proceedings of the National Academy of Sciences of the United States of America. Annual subject and author indexes. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[46]  Ron Reeder,et al.  Estimation of the mean of functional time series and a two‐sample problem , 2011, 1105.0019.

[47]  Yogesh K. Dwivedi,et al.  A test for second‐order stationarity of a time series based on the discrete Fourier transform , 2009, 0911.4744.

[48]  J. Wellner,et al.  Empirical Processes with Applications to Statistics , 2009 .

[49]  P. Schmidt,et al.  On the Power of the KPSS Test of Stationarity Against Fractionally-Integrated Alternatives , 1996 .

[50]  P. Kokoszka,et al.  Monitoring the Intraday Volatility Pattern , 2013 .

[51]  A. Aue,et al.  Break detection in the covariance structure of multivariate time series models , 2009, 0911.3796.

[52]  B. M. Pötscher,et al.  Dynamic Nonlinear Econometric Models: Asymptotic Theory , 1997 .

[53]  P. Kokoszka,et al.  Weakly dependent functional data , 2010, 1010.0792.

[54]  Lajos Horváth,et al.  Weak invariance principles for sums of dependent random functions , 2013 .