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Essays on the Modelling of Quantiles for Forecasting and Risk Estimation

Mitrodima, Evangelia (2015) Essays on the Modelling of Quantiles for Forecasting and Risk Estimation. Doctor of Philosophy (PhD) thesis, University of Kent,. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:53549)

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Language: English

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Abstract

This thesis examines the use of quantile methods to better estimate the time-varying conditional asset

by taking into account some features of financial returns.

Value at Risk (VaR). We find that the model provides us with improved estimates and forecasts, and

the economic performance of existing models through the use of past aggregate return information in

properties of quantile models, such as the types of issues that arise in their estimation.

quantile functions. Thus, there is a need for a model that considers the correct quantile ordering. In

applications. We speculate that this can be done by decomposing the conditional distribution in a natural

incorporate more than one probability levels and the dynamic of the scale. We find that by accounting

Apart from being able to address the monotonicity of quantile functions, this setting offers valuable

and shape over time separately and obtain satisfactory VaR forecasts. We deliver estimates for this model

classical approach.

multiple quantile settings. In particular, we find that the Bayesian methodology is useful for addressing

the multi-modality of the objective function and estimating the uncertainty of the model parameters.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Oberoi, Jaideep
Thesis advisor: Griffin, Jim E.
Uncontrolled keywords: Value-at-Risk, Quantile regression, MCMC, multiple quantiles
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: Users 1 not found.
Date Deposited: 21 Dec 2015 16:00 UTC
Last Modified: 06 Feb 2020 04:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/53549 (The current URI for this page, for reference purposes)
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