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on Information and Communication Technologies |
| By: | Chen, H.; Pau, L-F. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University) |
| Abstract: | This paper introduces individual tariffs at service and content bundle level in mobile communications. It gives a theoretical framework (economic, sociological) as well as a computational game solution method. The user can be an individual or a community. Individual tariffs are decided through interactions between the user and the supplier. A numerical example from mobile music illustrates the concepts. |
| Keywords: | Individual tariffs;mobile communication services;computational games;risks; |
| Date: | 2007–08–28 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:300011778&r=ict |
| By: | Kalogeropoulos, Konstantinos; Roberts, Gareth O.; Dellaportas, Petros |
| Abstract: | We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A novel MCMC scheme which overcomes the inherent difficulties of time change transformations is also presented. The algorithm is fast to implement and applies to models with stochastic volatility. The methodology is tested through simulation based experiments and illustrated on data consisting of US treasury bill rates. |
| Keywords: | Imputation; Markov chain Monte Carlo; Stochastic volatility |
| JEL: | C13 G12 C15 C11 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:5697&r=ict |
| By: | Kalogeropoulos, Konstantinos; Dellaportas, Petros; Roberts, Gareth O. |
| Abstract: | We address the problem of likelihood based inference for correlated diffusion processes using Markov chain Monte Carlo (MCMC) techniques. Such a task presents two interesting problems. First, the construction of the MCMC scheme should ensure that the correlation coefficients are updated subject to the positive definite constraints of the diffusion matrix. Second, a diffusion may only be observed at a finite set of points and the marginal likelihood for the parameters based on these observations is generally not available. We overcome the first issue by using the Cholesky factorisation on the diffusion matrix. To deal with the likelihood unavailability, we generalise the data augmentation framework of Roberts and Stramer (2001 Biometrika 88(3):603-621) to d-dimensional correlated diffusions including multivariate stochastic volatility models. Our methodology is illustrated through simulation based experiments and with daily EUR /USD, GBP/USD rates together with their implied volatilities. |
| Keywords: | Markov chain Monte Carlo; Multivariate stochastic volatility; Multivariate CIR model; Cholesky Factorisation. |
| JEL: | C13 G12 C15 C11 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:5696&r=ict |