Locally adaptive estimation methods with application to univariate time series

The paper offers a unified approach to the study of three locally adaptive estimation methods in the context of univariate time series from both theoretical and empirical points of view. A general procedure for the computation of critical values is given. The underlying model encompasses all distributions from the exponential family providing for great flexibility. The procedures are applied to simulated and real financial data distributed according to the Gaussian, volatility, Poisson, exponential and Bernoulli models. Numerical results exhibit a very reasonable performance of the methods.Comment: Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

A simple method for variance shift detection at unknown time points

Financial literature considers volatility as a good proxy for the risk level and thus the crucial parameter in many financial techniques and strategies. As such, the aim of this paper is to analyse the evolution of time series volatility and detect significant long-term variance changes. Building up on the variance ratio detection technique introduced by Tsay (1988), our paper extends it in two ways: first, we propose the computation of a moving variance ratio implemented on a selected part of the series, thus reducing the amount of calculus and increasing the reliability and second, as in reality permanent variance changes are almost inexistent, we proceed to an adjustment on a specified part of the series only after the detected variance change. Our moving variance ratio technique proves its efficiency in detecting variance changes and removing them from the series, both on simulated and real financial data. More specifically, two significant variance changes are detected within the series of the Hang Seng daily log-returns between 1994 and 2007: the first one on August 15, 1997 and can be linked to the Asian financial crisis, and the second one on July 27, 2001 corresponding to the beginning of a high volatility regime in emerging markets following the Internet bubble crash along with the first signs of the financial crisis in Argentina.Moving Variance Ratio, Variance Changes, Series Adjustment

A METHODOLOGY FOR DETECTING BREAKS IN THE MEAN AND COVARIANCE STRUCTURE OF TIME SERIES

Some structural break techniques defined in the time and frequency domains are presented to explore, at the same time, the empirical evidence of the mean and covariance instability by uncovering regime-shifts in some inflation series. To that effect, we pursue a methodology that combines two approaches; the first is defined in the time domain and is designed to detect mean-shifts, and the second is defined in the frequency domain and is adopted to study the instability problem of the covariance function of the series. The proposed methodology has a double interest since, besides the detection of regime-shifts occasioned in the covariance structure of the series, it allows taking into account the presence of mean-shifts in this series. Note that unlike the works existing in the literature which often adopt a single technique to study the break identification problem, our methodology combines two approaches, parametric and nonparametric, to examine this problem.Structural change, mean and variance shifts, parametric and nonparametric approaches.

Long run analysis of crude oil portfolios

This paper deals with the analysis of the long-run behavior of a set of mispricing portfolios generated by three crude oils, where one of the oils is the reference commodity and it is compared to a combination of the other two ones. To this aim, the long-term parameter related to the mispricing portfolio are estimated on empirical data. We pay particular attention to the cases of mispricing portfolios either of stationary type or following a Brownian motion: the former situation is associated to replication portfolios of a reference commodity; the latter one allows to implement forecasts. The theoretical setting is validated through empirical data on WTI, Brent and Dubai oils

Inflation persistence in the European Union, the euro area, and the United States

In this paper we report results on inflation persistence using 79 inflation series covering the EU countries, the euro area and the US for five different inflation variables. The picture that emerges is one of moderate inflation persistence across the board. In particular we find euro area inflation persistence to be broadly in line with US inflation persistence. The issue of allowing for intercept dummies in the underlying inflation models is found to be of paramount importance to avoid overestimation of the level of persistence. The use of alternative measures of persistence is found to be commendable on the grounds that they complement each other in practice. JEL Classification: E31, E52, C22, C12Inflation Dynamics, median unbiased estimates, Structural change

Assessing French Inflation Persistence with Impulse Saturation Break Tests and Automatic General-to-Specific Modelling

This paper has three different motivations. Firstly, we wish to contribute to the debate on whether French inflation has been persistent since the mid-eighties. Empirical evidence in this domain has been mixed. We use the standard method of testing for breaks in the mean of the inflation series to conclude whether possible unit root findings are the result of neglected breaks. Then, we build standard autoregressive representations of inflation, using an automatic general-to-specific approach. We conclude against inflation persistence in the sample period, and the point estimates of persistence we obtain are several percentage points below those achieved with other break tests and model selection methods. Moreover, our final model is congruent. Secondly, we provide the first empirical application of the new impulse saturation break test. The resulting estimates of the break dates are in line with other literature findings and have a sound economic meaning, confirming the good performance the test had revealed in theoretical and simulation studies. Finally, we also illustrate the shortcomings of the Bai-Perron test when applied to a small sample with high serial correlation. Indeed, we show the Bai- Perron break dates’ estimates would not allow us to build a congruent autoregressive representation of inflation.Inflation Persistence, Break Tests, Model Selection, General-to-Specific

Breaks, trends and the attribution of climate change: a time-series analysis

Climate change detection and attribution have been the subject of intense research and debate over at least four decades. However, direct attribution of climate change to anthropogenic activities using observed climate and forcing variables through statistical methods has remained elusive, partly caused by difficulties to correctly identify the time-series properties of these variables and by the limited availability of methods to relate nonstationary variables. This paper provides strong evidence concerning the direct attribution of observed climate change to anthropogenic greenhouse gases emissions by first investigating the univariate time-series properties of observed global and hemispheric temperatures and forcing variables and then by proposing statistically adequate multivariate models. The results show that there is a clear anthropogenic fingerprint on both global and hemispheric temperatures. The signal of the well-mixed Greenhouse Gases (GHG) forcing in all temperature series is very clear and accounts for most of their secular movements since the beginning of observations. Both temperature and forcing variables are characterized by piecewise linear trends with abrupt changes in their slopes estimated to occur at different dates. Nevertheless, their long-term movements are so closely related that the observed temperature and forcing trends cancel out. The warming experimented during the last century was mainly due to the increase in GHG which was partially offset by the effect of tropospheric aerosols. Other forcing sources, such as solar, are shown to only contribute to (shorter-term) variations around the GHG forcing trend.Published versio

Testing for Volatility Changes in Grain Markets

We use newly nonparametric volatility measures and break techniques to estimate common breaks across grain futures over the recent ten years. Our results show one structural change in realized volatilities occurred in 2006 for corn and in 2007 for soybean. But the date difference between them cannot be negligible. We disaggregate the realized volatilities into a continuous component and a jump part and found the source of structural beak in realized volatilities is from jumps.structural change, grain, volatility measures, Financial Economics,

Detecting the Presence of Informed Price Trading Via Structural Break Tests

The occurrence of abnormal returns before unscheduled announcements is usually identified with informed price movements. Therefore, the detection of these observations beyond the range of returns due to the normal day-to-day activity of financial markets is a concern for regulators monitoring the right functioning of financial markets and for investors concerned about their investment portfolios. In this article we introduce a novel method to detect informed price movements via structural break tests for the intercept of an extended CAPM model describing the risk premium of financial returns. These tests are based on the use of a U-statistic type process that is sensitive to detecting changes in the intercept that occur very early in the evaluation period and that can be used to construct a consistent estimator of the timing of the change. As a byproduct, we show that estimators of the timing of change constructed from standard CUSUM statistics are inconsistent and therefore fail to provide useful information about the presence of informed price movements