Detection of Change--Points in the Spectral Density. With Applications to ECG Data

We propose a new method for estimating the change-points of heart rate in the orthosympathetic and parasympathetic bands, based on the wavelet transform in the complex domain and the study of the change-points in the moments of the modulus of these wavelet transforms. We observe change-points in the distribution for both bands.Comment: proceeding of the workshop 'Fouille de donn\'ees temporelles et analyse de flux de donn\'ees' EGC'2009, january 27, Strasbourg, Franc

Adaptive estimation for a time inhomogeneous stochastic-volatility model

Let a process SI , ... ,ST obey the conditionally heteroskedastic equation St = Vt Et whcrc Et is a random noise and Vt is the volatility coefficient which in turn obeys an autoregression type equation log v t = w + a S t- l + nt with an additional noise nt. We consider the situation which the parameters w and a might also depend on the time t, and we study the problem of online estimation of current values of w = w(T) and a = a(T) from the observations SI , ... ,ST. We propose an adaptive method of estimation which does not use any information about time homogenity of the obscured process. We apply this model to two series of FX daily returns on DEM/USD and GBP/USD

Modelling Exchange Rates Volatility with Multivariate Long-Memory ARCH Processes

We consider two multivariate long-memory ARCH models, which extend the univariate long-memory ARCH models, we first consider a long-memory extension of the restricted constant conditional correlations (CCC) model introduced by Bollerslev (1990), and we propose a new unrestricted conditional covariance matrix model which models the conditional covariances as long-memory ARCH processes. We apply these two models to two daily returns on foreign exchanges (FX) rates series, the Pound-US dollar, and the Deutschmark-US dollar. The estimation results for both models show: (i) that the unrestricted model outperforms the restricted CCC model, and (ii) that all the elements of the conditional covariance matrix share the same degree of long-memory for the period April 1979 - January 1997. However, this result does not hold for the floating periods March 1973 - January 1997 and September 1971 - January 1997. This break in the long-term structure may be caused by the European Monetary System inception in March 1979

Microeconomic Models for Long-Memory in the Volatility of Financial Time Series

We show that a class of microeconomic behavioral models with interacting agents, derived from Kirman (1991, 1993), can replicate the empirical long-memory properties of the two first conditional moments of financial time series. The essence of these models is that the forecasts and thus the desired trades of the individuals in the markets are influenced, directly, or indirectly by those of the other participants. These "field effects" generate "herding" behaviour which affects the structure of the asset price dynamics. The series of returns generated by these models display the same empirical properties as financial returns: returns are I(0), the series of absolute and squared returns display strong dependence, while the series of absolute returns do not display a trend. Furthermore, this class of models is able to replicate the common long-memory properties in the volatility and co-volatility of financial time series, revealed by Teyssiɷre (1997, 1998a). These properties are investigated by using various model independent tests and estimators, i.e., semiparametric and nonparametric, introduced by Lo (1991), Kwiatkowski, Phillips, Schmidt and Shin (1992), Robinson (1995), Lobato and Robinson (1998), Giraitis, Kokoszka, Leipus and Teyssiɷre (2000, 2001). The relative performance of these tests and estimators for long-memory in a non-standard data generating process is then assessed

Bubbles and long-range dependence in asset prices volatilities

A model for a financial asset is constructed with two types of agents. The agents differ in terms of their beliefs. The proportions of the two types change over time according to a stochastic process which models the interaction between the agents. Thus, unlike other models, agents do not persist in holding "wrong" beliefs. Bubble-like phenomena in the assetprice occur. We consider several tests for detecting long range dependence and change-points in the conditional variance process. Although the model seems to generate long-memory properties of the volatility series, we show that this is due to the switching of regimes which are detected by the tests we propose.interaction, bubbles, testing, long-memory, heteroskedasticity, change-point