Details of Grant 

EPSRC Reference:	EP/K001264/1
Title:	Bayesian Inference for Diffusion Processes from Partial Observations and Expectations
Principal Investigator:	Kalogeropoulos, Dr K
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department:	Statistics
Organisation:	London School of Economics & Pol Sci
Scheme:	First Grant - Revised 2009
Starts:	01 February 2013	Ends:	31 January 2015	Value (£):	93,836
EPSRC Research Topic Classifications:	Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:	Financial Services
Related Grants:
Panel History:	Panel Date Panel Name Outcome 04 Jul 2012 Mathematics Prioritisation Panel Meeting July 2012 Announced
Summary on Grant Application Form
A substantial amount of publicly available datasets represent educated predictions on the evolution of stochastic processes. These include financial derivative instruments, such as option prices, that can be formulated as expectations of the underlying price process. The proposed project considers models with latent diffusion processes that can be linked to direct observations, but also to such conditional expectations. The goal is to utilise advanced computational methods to estimate the data generating mechanism from both datasets. Moreover, to develop a general inferential framework to handle parameter and model uncertainty. The main focus is on financial applications, and in particular estimating stochastic volatility models estimated from asset and option prices. In this context, the procedure may be viewed as a robust calibration technique for identifying the model driving the option prices while estimating the volatility and its parameters. This facilitates subsequent use for pricing financial derivatives and implementing hedging strategies. Desired features of the proposed methodology will include: applicability to various types of derivatives, accurate and controllable approximations of the intractable quantities in the model, and feasible computational schemes. Other objectives include developing and applying suitable procedures for specifying various aspects of the model in different observation settings, and generalising the existing computational framework to include jump diffusions, support Bayesian model choice and allow implementation in an online manner. The aim is also to develop a general inferential framework that will be able to accommodate applications from different areas. Specific examples in structural credit risk models, electricity markets and macroeconomics will be considered. The proposed research include simulation based inference for diffusions with stochastic volatility, used for asset pricing. It is therefore expected that it will be beneficial for various academic disciplines such as Computational Statistics, Mathematical Finance and Econometrics. Moreover, as techniques for data calibration and hedging will be developed, it will also be of interest to the financial sector and power markets.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Project URL:
Further Information:
Organisation Website:	http://www.lse.ac.uk