Recursive Thick Modeling and the Choice of Monetary Policy in Mexico.

The choice of monetary policy is the most important concern of central banks. However, this choice is always confronted, inter alia, with two relevant aspects of economic policy: parameter instability and model uncertainty. This paper deals with both types of uncertainty using a very specific class of models in an optimal control framework. For optimal policy rates series featuring the first two moments similar to those of the actual nominal interest rates in Mexico, we show that recursive thick modeling gives a better approximation than recursive thin modeling. We complement previous work by evaluating the usefulness of both recursive thick modeling and recursive thin modeling in terms of direction-of-change forecastability.Macroeconomic policy, Model uncertainty, Optimal control, Monetary policy, Inflation targeting

Analysis of the Performance of Mexican Pension Funds: Evidence from a Stationary Bootstrap Application

This paper assesses the performance of Mexican pension funds (AFORES) by using an asset pricing model that includes macroeconomic factors and benchmark portfolios to explain returns. We apply a bootstrap statistical technique to obtain the cross-sectional distribution of performance measures (alphas) across all pension funds. This is done to determine whether a pension fund manager adds value to the portfolio before commissions charges, or if the performance observed, after controlling for the relevant factors, is simply explained by luck. Moreover, by comparing pension fund alphas to the distributions of alphas corresponding to lower rankings, we can find out if a particular fund statistically distinguishes itself from others. Our results provide evidence that pension funds managers do not add value to the portfolio and that funds are not distinguishable from each other.Pension funds, Performance evaluation, Stationary bootstrap

A generalization of the cumulant expansion. Application to a scale-invariant probabilistic model

As well known, cumulant expansion is an alternative way to moment expansion to fully characterize probability distributions provided all the moments exist. If this is not the case, the so called escort mean values (or q-moments) have been proposed to characterize probability densities with divergent moments [C. Tsallis et al, J. Math. Phys 50, 043303 (2009)]. We introduce here a new mathematical object, namely the q-cumulants, which, in analogy to the cumulants, provide an alternative characterization to that of the q-moments for the probability densities. We illustrate this new scheme on a recently proposed family of scale-invariant discrete probabilistic models [A. Rodriguez et al, J. Stat. Mech. (2008) P09006; R. Hanel et al, Eur. Phys. J. B 72, 263268 (2009)] having q-Gaussians as limiting probability distributions

Lovelock gravities from Born-Infeld gravity theory

We present a Born-Infeld gravity theory based on generalizations of Maxwell symmetries denoted as $\mathfrak{C}_{m}$. We analyze different configuration limits allowing to recover diverse Lovelock gravity actions in six dimensions. Further, the generalization to higher even dimensions is also considered.Comment: v3, 15 pages, two references added, published versio

Rational characteristic functions and markov chains

Abstract 1 We investigate in this paper how to estimate the density function of a random variable using a parametric ARMA model for its characteristic function. The choice of this model is motivated by the fact that this type of density characterizes the duration of staying at an N-states Markov chain, but the approach is general enough to be applied to many practical problems. Both ML and moment-based linear estimates are derived, the former being based on the optimization of a highly non-linear function. 1.Peer ReviewedPostprint (published version

Physical consequences of P≠\neqNP and the DMRG-annealing conjecture

Computational complexity theory contains a corpus of theorems and conjectures regarding the time a Turing machine will need to solve certain types of problems as a function of the input size. Nature {\em need not} be a Turing machine and, thus, these theorems do not apply directly to it. But {\em classical simulations} of physical processes are programs running on Turing machines and, as such, are subject to them. In this work, computational complexity theory is applied to classical simulations of systems performing an adiabatic quantum computation (AQC), based on an annealed extension of the density matrix renormalization group (DMRG). We conjecture that the computational time required for those classical simulations is controlled solely by the {\em maximal entanglement} found during the process. Thus, lower bounds on the growth of entanglement with the system size can be provided. In some cases, quantum phase transitions can be predicted to take place in certain inhomogeneous systems. Concretely, physical conclusions are drawn from the assumption that the complexity classes {\bf P} and {\bf NP} differ. As a by-product, an alternative measure of entanglement is proposed which, via Chebyshev's inequality, allows to establish strict bounds on the required computational time.Comment: Accepted for publication in JSTA

Universality class of the depinning transition in the two-dimensional Ising model with quenched disorder

With Monte Carlo methods, we investigate the universality class of the depinning transition in the two-dimensional Ising model with quenched random fields. Based on the short-time dynamic approach, we accurately determine the depinning transition field and both static and dynamic critical exponents. The critical exponents vary significantly with the form and strength of the random fields, but exhibit independence on the updating schemes of the Monte Carlo algorithm. From the roughness exponents $\zeta, \zeta_{loc}$ and $\zeta_s$, one may judge that the depinning transition of the random-field Ising model belongs to the new dynamic universality class with $\zeta \neq \zeta_{loc}\neq \zeta_s$ and $\zeta_{loc} \neq 1$. The crossover from the second-order phase transition to the first-order one is observed for the uniform distribution of the random fields, but it is not present for the Gaussian distribution.Comment: 16 pages, 16 figures, 3 table