Why is Central Paris loosing jobs?

Brueckner et alii (1999) have explained urban population pattern through amenities distribution. Based on their model, this paper introduces a productive sector and helps understand employment suburbanization in a new way. Considering how amenities are valued, the 'people follow jobs' vs 'jobs follow people' case is discussed for CBD and hogh-brawn services firms. If they favour natural amenities, they might leave the historical center. A big constraint against that move is that the firm wants to keep its employees who may all live around the center. Despite conventionnal centripetal forces, they can settle in the suburbs before the households. People may than follow the firm in the suburbs.

Is Central Paris still that rich?

From 1975 to 1999, employment in Paris metropolitan area has become more and more decentralized. This deconcentration is almost half spread and half clustered. Parallel to the sprawl of jobs, the growth of a services oriented economy has led to an increase in sectoral concentration. But there are no clear evidences of a vertical spatial desintegration, because by the same time the places tend to diversify. An explanation might be that the sprawl relies both on endogenous job creations and on job relocations: the relocations tend to increase the specialisation of the clusters but endogenous growth is more diverse and residential.

Analysis of stability and bifurcations of limit cycles in Chua's circuit through the harmonic balance approach

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS: PART

Extreme Value Theory for Tail-Related Risk Measures

Many fields of modern science and engineering have to deal with events which are rare but have significant consequences. Extreme value theory is considered to provide the basis for the statistical modeling of such extremes. The potential of extreme value theory applied to financial problems has only been recognized recently. This paper aims at introducing the fundamentals of extreme value theory as well as practical aspects for estimating and assessing statistical models for tail-related risk measures.Extreme Value Theory; Generalized Pareto Distribution, Generalized Extreme Value Distribution; Quantile Estimation, Risk Measures; Maximum Likelihood Estimation; Profile Likelihood Confidence Intervals.

Bargaining and Collusion in a Regulatory Model

We consider the regulation of a monopolistic market when the prin- cipal delegates to a regulatory agency two tasks: the supervision of the firm's unknown costs and the arrangement of a pricing mechanism. As usual, the agency may have an incentive to hide information from the principal to share the informative rent with the firm. The novelty of this paper is that both the regulatory mechanism and the side con- tracting between the agency and the firm are modelled as a bargaining process. This negotiation between the regulator and the monopoly induces a radical change in the extraprofit from private information, which is now equal to the standard informational rent weighted by the agency’ bargaining power. This in turn a¤ects the collusive stage, in particular the firm has the greatest incentive to collude when fac- ing an agency with the same bargaining power. Then, we focus on the optimal organizational responses to the possibility of collusion. In our setting, where incompleteness of contracts prevents the design of a screening mechanism between the agency’ types and thus Tirole’ equivalence principle does not apply, we prove that the stronger the agency in the negotiation process, the greater the incentives for the principal to tolerate collusion in equilibrium.regulation, bargaining, collusion.

Implementing Binomial Trees

This paper details the implementation of binomial tree methods for the pricing of European and American options. Pseudocode and sample programmes for Matlab and R are given.Option pricing, Binomial trees, Numerical methods, Matlab, R

Robust regression with optimisation heuristics

Linear regression is widely-used in finance. While the standard method to obtain parameter estimates, Least Squares, has very appealing theoretical and numerical properties, obtained estimates are often unstable in the presence of extreme observations which are rather common in financial time series. One approach to deal with such extreme observations is the application of robust or resistant estimators, like Least Quantile of Squares estimators. Unfortunately, for many such alternative approaches, the estimation is much more difficult than in the Least Squares case, as the objective function is not convex and often has many local optima. We apply different heuristic methods like Differential Evolution, Particle Swarm and Threshold Accepting to obtain parameter estimates. Particular emphasis is put on the convergence properties of these techniques for fixed computational resources, and the techniques’ sensitivity for different parameter settings.Optimisation heuristics, Robust Regression, Least Median of Squares

Optimal enough?

An alleged weakness of heuristic optimisation methods is the stochastic character of their solutions. That is, instead of finding a truly optimal solution, they only provide a stochastic approximation of this optimum. In this paper we look into a particular application, portfolio optimisation. We demonstrate two points: firstly, the randomness of the ‘optimal’ solution obtained from the algorithm can be made so small that for all practical purposes it can be neglected. Secondly, and more importantly, we show that the remaining randomness is swamped by the uncertainty coming from the data. In particular, we show that as a result of the bad conditioning of the problem, minor changes in the solution lead to economically meaningful changes in the solution’s out-of-sample performance. The relationship between in-sample fit and out-of-sample performance is not monotonous, but still, we observe that up to a point better solutions in-sample lead to better solutions out-of-sample. Beyond this point, however, there is practically no more cause for improving the solution any further, since any improvement will only lead to unpredictable changes (noise) out-of-sample.Optimisation heuristics, Portfolio Optimisation, Threshold Accepting