Econometrics

Since the last decade we live in a digitalized world where many actions in human and economic life are monitored. This produces a continuous stream of new, rich and high quality data in the form of panels, repeated cross-sections and long time series . These data resources are available to many researchers at a low cost. This new erais fascinating for econometricians who can adress many open economic questions. To do so, new models are developed that call for elaborate estimation techniques. Fast personal computers play an integral part in making it possible to deal with this increased complexity. --

Option pricing with asymmetric heteroskedastic normal mixture models

This paper uses asymmetric heteroskedastic normal mixture models to fit return data and to price options. The models can be estimated straightforwardly by maximum likelihood, have high statistical fit when used on S&P 500 index return data, and allow for substantial negative skewness and time varying higher order moments of the risk neutral distribution. When forecasting out-of-sample a large set of index options between 1996 and 2009, substantial improvements are found compared to several benchmark models in terms of dollar losses and the ability to explain the smirk in implied volatilities. Overall, the dollar root mean squared error of the best performing benchmark component model is 39% larger than for the mixture model. When considering the recent financial crisis this difference increases to 69%.asymmetric heteroskadastic models, finite mixture models, option pricing, out-of- sample prediction, statistical fit

Asymptotic properties of the Bernstein density copula for dependent data

Copulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based. This paper proposes nonparametric estimation of the density copula for α-mixing data using Bernstein polynomials. We study the asymptotic properties of the Bernstein density copula, i.e., we provide the exact asymptotic bias and variance, we establish the uniform strong consistency and the asymptotic normality

Semiparametric multivariate volatility models

Estimation of multivariate volatility models is usually carried out by quasi maximum likelihood (QMLE), for which consistency and asymptotic normality have been proven under quite general conditions. However, there may be a substantial efficiency loss of QMLE if the true innovation distribution is not multinormal. We suggest a nonparametric estimation of the multivariate innovation distribution, based on consistent parameter estimates obtained by QMLE. We show that under standard regularity conditions the semiparametric efficiency bound can be attained. Without reparametrizing the conditional covariance matrix (which depends on the particular model used), adaptive estimation is not possible. However, in some cases the e?ciency loss of semiparametric estimation with respect to full information maximum likelihood decreases as the dimension increases. In practice, one would like to restrict the class of possible density functions to avoid the curse of dimensionality. One way of doing so is to impose the constraint that the density belongs to the class of spherical distributions, for which we also derive the semiparametric efficiency bound and an estimator that attains this bound. A simulation experiment demonstrates the e?ciency gain of the proposed estimator compared with QMLE. --Multivariate volatility,GARCH,semiparametric efficiency,adaptivity

A nonparametric copula based test for conditional independence with applications to granger causality

This paper proposes a new nonparametric test for conditional independence, which is based on the comparison of Bernstein copula densities using the Hellinger distance. The test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the data. In fact, to apply the test, only a bandwidth is needed for the nonparametric copula. We prove that the test statistic is asymptotically pivotal under the null hypothesis, establish local power properties, and motivate the validity of the bootstrap technique that we use in finite sample settings. A simulation study illustrates the good size and power properties of the test. We illustrate the empirical relevance of our test by focusing on Granger causality using financial time series data to test for nonlinear leverage versus volatility feedback effects and to test for causality between stock returns and trading volume. In a third application, we investigate Granger causality between macroeconomic variablesNonparametric tests, Conditional independence, Granger non-causality, Bernstein density copula, Bootstrap, Finance, Volatility asymmetry, Leverage effect, Volatility feedback effect, Macroeconomics

Asymptotic properties of the Bernstein density copula for dependent data

Copulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based. This paper proposes nonparametric estimation of the density copula for a-mixing data using Bernstein polynomials. We study the asymptotic properties of the Bernstein density copula, i.e., we provide the exact asymptotic bias and variance, we establish the uniform strong consistency and the asymptotic normality.Nonparametric estimation, Copula, Bernstein polynomial, a-mixing, Asymptotic properties, Boundary bias

A comparison of forecasting procedures for macroeconomic series: the contribution of structural break models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.forecasting, change-points, Markov switching, Bayesian inference

Electrostatic confinement of electrons in graphene nano-ribbons

Coulomb blockade is observed in a graphene nanoribbon device with a top gate. When two pn junctions are formed via the back gate and the local top gate, electrons are confined between the pn junctions which act as the barriers. When no pn junctions are induced by the gate voltages, electrons are still confined, as a result of strong disorder, but in a larger area. Measurements on five other devices with different dimensions yield consistent results.Comment: 4 figures, 1 table, 4.4page

Coupled multiple-mode theory for s±s_{\pm} pairing mechanism in iron based superconductors

We investigate the interplay between the magnetic and the superconducting degrees of freedom in unconventional multi-band superconductors such as iron pnictides. For this purpose a dynamical mode-mode coupling theory is developed based on the coupled Bethe-Salpeter equations. In order to investigate the region of the phase diagram not too far from the tetracritical point where the magnetic spin density wave, (SDW) and superconducting (SC) transition temperatures coincide, we also construct a Ginzburg - Landau functional including both SC and SDW fluctuations in a critical region above the transition temperatures. The fluctuation corrections tend to suppress the magnetic transition, but in the superconducting channel the intraband and interband contribution of the fluctuations nearly compensate each other.Comment: 17 pages, 5 figure

TaIrTe4 a ternary Type-II Weyl semi-metal

In metallic condensed matter systems two different types of Weyl fermions can in principle emerge, with either a vanishing (type-I) or with a finite (type-II) density of states at the Weyl node energy. So far only WTe2 and MoTe2 were predicted to be type-II Weyl semi-metals. Here we identify TaIrTe4 as a third member of this family of topological semi-metals. TaIrTe4 has the attractive feature that it hosts only four well-separated Weyl points, the minimum imposed by symmetry. Moreover, the resulting topological surface states - Fermi arcs connecting Weyl nodes of opposite chirality - extend to about 1/3 of the surface Brillouin zone. This large momentum-space separation is very favorable for detecting the Fermi arcs spectroscopically and in transport experiments