TSSV

Tempo-spatial stochastic volatility: Modelling and statistical inference

Coordinatore	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE    more  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Ms. Nome: Brooke Cognome: Alasya Email: send email Telefono: +44 207 594 1181 Fax: +44 207 594 1418
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	100˙000 €
EC contributo	100˙000 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2012-CIG
Funding Scheme	MC-CIG
Anno di inizio	2012
Periodo (anno-mese-giorno)	2012-10-01   -   2016-09-30

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE  Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ contact info Titolo: Ms. Nome: Brooke Cognome: Alasya Email: send email Telefono: +44 207 594 1181 Fax: +44 207 594 1418	UK (LONDON)	coordinator	100˙000.00

Mappa

 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

 Obiettivo del progetto (Objective)

'Statistics for tempo-spatial data is one of the most important research frontiers in modern statistics. This project proposes to introduce and develop the concept of tempo-spatial stochastic volatility, which allows one to model volatility clusters both in time and in space. Empirical evidence for stochastic volatility is ubiquitous, and hence, it is vital and urgent that statistical models and estimation methods will be developed to account for this key quantity. Stochastic volatility is a latent variable, meaning that it is not directly observable but needs to be estimated from other (observable) variables. The main research objectives are to construct novel, non-parametric estimators for tempo-spatial stochastic volatility and to establish the corresponding asymptotic theory for constructing confidence regions. Moreover, fully parametric classes of stochastic volatility models will be developed, and the corresponding statistical inference techniques will be derived. The objectives will be achieved by developing the concept of realised quadratic variation for random fields, which results in a non-parametric proxy for tempo-spatial stochastic volatility. Also, novel, parametric estimation procedures will be designed to estimate the parameters of new tempo-spatial stochastic volatility models based on a quasi-maximum-likelihood framework. The completion of this project will be a major breakthrough in statistics and will solidify Europe’s leadership in this field. Moreover, it is of key relevance to the Work Programme since it strongly contributes to the initiative “sustainable growth”: The results of this research project are directly applicable to, e.g. measuring and modelling the risk associated with climate change and to finding an optimal design for wind farms, making renewable sources of energy more efficient and reliable.'