This paper is intended to test and estimate time-varying elasticities for gasoline demand in Switzerland. For this purpose, a smooth time-varying cointegrating parameters model is investigated in order to describe smooth mutations of the Swiss gasoline demand. The methodology, based on Chebyshev...

This paper aims at extracting a common factor from major carbon pricing. For this purpose, we use a Dynamic Factor Model (DFM) for which the unobserved common factors and idiosyncratic noises are potentially non-stationary processes. The two-step Kalman smoother procedure is used to estimate the...

This paper investigates whether a common factor, which can be interpreted as a shadow carbon price, can be extracted from price series in 13 major national and subnational emission trading systems (ETSs). For this purpose, we estimate a dynamic factor model accounting for information...

The purpose of this paper is to provide a flexible parametric methodology to measure nonmonotone relationships between two variables. Indeed, in this context, the Pearson rho measure fails because it is only consistent for linear monotone dependence. Using the well-known return-volume...

We consider the problem of estimating the volatility of a financial asset from a time series record of length T. We believe the underlying volatility process is smooth, possibly stationary, and with potential abrupt changes due to market news. By drawing parallels between time series and...

We consider Taylor's stochastic volatility model when the innovations of the hidden log-volatility process have a Laplace distribution (l1 exponential density), rather than the standard Gaussian distribution (l2) usually employed. Using a distribution with heavier tails allows better modeling of...

In this paper we extend the FMLS-based CUSUM cointegration test (Xiao and Phillips, 2002) for testing the smooth time-varying cointegration null hypothesis. For this purpose we use Chebyshev time polynomials to specify time-varying coefficients under the null. We derive the limiting distribution...