Effect of noise filtering on predictions : on the routes of chaos

The detection of chaotic behaviors in commodities, stock markets and weather data is usually complicated by large noise perturbation inherent to the underlying system. It is well known, that predictions, from pure deterministic chaotic systems can be accurate mainly in the short term. Thus, it will be important to be able to reconstruct in a robust way the attractor in which evolves the data, if this attractor exists. In chaotic theory, the deconvolution methods have been largely studied and there exist different approaches which are competitive and complementary. In this work, we apply two methods : the singular value method and the wavelet approach. This last one has not been investigated a lot of filtering chaotic systems. Using very large Monte Carlo simulations, we show the ability of this last deconvolution method. Then, we use the de-noised data set to do forecast, and we discuss deeply the possibility to do long term forecasts with chaotic systems.Deconvolution, chaos, SVD, state space method, wavelets method.

Contagion Between the Financial Sphere and the Real Economy. Parametric and non Parametric Tools: A Comparison

The aim of this chapter is to dsicuss the contagionbetween the financial sphere and the real sphere. We define the concept of contagion, then we introduce some parametric models used to detect the contagion phenomenum, then we introduce some non-parametric tools focusing on copulas. Interdependence between national economies is investigated through these tools. Finally we investigate the interdependence between the financial and the real spheres.Contagion ;Setar ;markov switching ;Copulas ;real sphere ;financial sphere

Chaos in economics and finance

In this article, we specify the different approaches followed by the economists and the financial economists in order to use chaos theory. We explain the main difference using this theory with other research domains like the mathematics and the physics. Finally, we present tools necessary for the economists and financial economists to explore this domain empirically.Chaos theory ; attractor ; Economy ; Finance ; estimation theory ; forecasting

Non-stationarity and meta-distribution

In this paper we deal with the problem of non-stationarity encountered in a lot of data sets, mainly in financial and economics domains, coming from the presence of multiple seasonnalities, jumps, volatility, distorsion, aggregation, etc. Existence of non-stationarity involves spurious behaviors in estimated statistics as soon as we work with finite samples. We illustrate this fact using Markov switching processes, Stopbreak models and SETAR processes. Thus, working with a theoretical framework based on the existence of an invariant measure for a whole sample is not satisfactory. Empirically alternative strategies have been developed introducing dynamics inside modelling mainly through the parameter with the use of rolling windows. A specific framework has not yet been proposed to study such non-invariant data sets. The question is difficult. Here, we address a discussion on this topic proposing the concept of meta-distribution which can be used to improve risk management strategies or forecasts.Non-stationarity, switching processes, SETAR processes, jumps, forecast, risk management, copula, probability distribution function.

A prospective study of the k-factor Gegenbauer processes with heteroscedastic errors and an application to inflation rates

We investigate some statistical properties of the new k-factor Gegenbauer process with heteroscedastic noises One of the goals of the paper is to give tools which permit to use this model to explain the behaviour of certain data sets in finance and in macroeconomics. Monte Carlo experiments are provided to calibrate the theoretical properties. Applications on consumer price indexes and inflation rates are done;GIGARCH process – estimation theory – Inflation rates – prices indexes.

On the use of nearest neighbors in finance

The paper focuses on the conditions of the use of the nearest neighbors method analysing the impact of the Euclidean distance and in sample predictions, the choice of the neighbors, the number of neighbors and the distance between the neighbors.Nearest neighbors – Forecasting

A note on self-similarity for discrete time series

The purpose of this paper is to study the self-similar properties of discrete-time long memory processes. We apply our results to specific processes such as GARMA processes and GIGARCH processes, heteroscedastic models and the processes with switches and jumps.Covariance stationary, Long memory processes, short memory processes, self-similar, asymptotically second-order self-similar, autocorrelation function.

Hedging tranches index products : illustration of model dependency

In this paper, index tranches'properties and several hedging strategies are discussed. Model risk and correlation risk are analysed through the study of the efficiency of several factor based copula models, like the Gaussian, the double-t and the double NIG using implied correlation and a particular NIG one factor model, using historical data in terms of hedging capabilities.CDO – Factor models – NIG distribution

Alternative methods for forecasting GDP

An empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modelling is conducted on the euro area GDP. Using both methods for nowcasting and forecasting the GDP, through the estimation of economic indicators plugged in the bridge equations, we get more accurate forecasts when using nearest neighbor method. We prove also the asymptotic normality of the multivariate k-nearest neighbor regression estimator for dependent time series, providing confidence intervals for point forecast in time series.Forecast, economic indicators, GDP, Euro area, VAR, multivariate k-nearest neighbor regression, asymptotic normality.

Fractional seasonality: Models and Application to Economic Activity in the Euro Area

In this paper, we recall some concepts on seasonal long memory, we review the diverse fractionally integrated seasonal time series models and we discuss their statistical properties. Then, we compare the empirical performances of those models on euro area economic data and we show that generalized long memory models offer competitive alternatives to classical SARIMA models, avoiding over-differentiation and providing a better goodness of fit.Fractional seasonality, long-range dependence, generalized long memory models, economic activity.