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Hawkes processes and some financial applications

Includes bibliographical references.

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Bibliographic Details
Main Author: Lapham, Brendon M
Other Authors: MacDonald, Iain L
Format: Thesis
Language:English
Published: Division of Actuarial Science 2014
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access_status_str Open Access
author Lapham, Brendon M
author2 MacDonald, Iain L
author_browse Lapham, Brendon M
MacDonald, Iain L
author_facet MacDonald, Iain L
Lapham, Brendon M
author_sort Lapham, Brendon M
collection Thesis
description Includes bibliographical references.
format Thesis
id oai:open.uct.ac.za:11427/8523
institution University of Cape Town (South Africa)
language eng
last_indexed 2026-06-10T12:34:00.978Z
license_str Not specified — see source repository
provenance_str_mv Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository
publishDate 2014
publishDateRange 2014
publishDateSort 2014
publisher Division of Actuarial Science
publisherStr Division of Actuarial Science
record_format dspace
source_str UCTD — University of Cape Town Open Access Repository
spelling oai:open.uct.ac.za:11427/8523 Hawkes processes and some financial applications Lapham, Brendon M MacDonald, Iain L Includes bibliographical references. The self-exciting point process, which is now more commonly known as the Hawkes process, is a model for a point process on the real line introduced by Hawkes (1971). The distinguishing feature of such processes is that they allow all past `events' to affect the intensity function at the current time. Over the years such processes have been applied in seismology and neurophysiology in particular, and in more recent years there have been significant financial applications. In almost all of these applications, the route used to find the maximum likelihood estimates (MLEs) is direct numerical maximisation (DNM) of the likelihood. An EM algorithm, which makes use of the Poisson cluster process interpretation of the Hawkes process, is an alternative route to the MLEs. This particular EM algorithm has received attention in the literature and has been claimed to have advantages over DNM of the likelihood. We carry out a simulation study for a simple Hawkes process to clarify statements made in the literature about these advantages. For the simple Hawkes process models that we consider, DNM of the likelihood is the preferable route to finding the MLEs. We then use DNM of the likelihood to _t marked Hawkes process models to South African asset data. These applications to South African data include the modelling of extreme asset returns and the forecasting of conditional value-at-risk (VaR) and expected shortfall (ES). The models investigated include mostly models found in the literature, but also include some variations introduced here. In a backtesting exercise, we compare the conditional VaR and ES forecasts found by using the marked Hawkes process models with those found via some nonstandard stochastic volatility (SV) models. We find that the marked Hawkes process models give mostly competitive forecasts of conditional VaR and ES when compared with the nonstandard SV models. 2014-10-17T10:09:51Z 2014-10-17T10:09:51Z 2014 Master Thesis Masters MBusSc http://hdl.handle.net/11427/8523 eng application/pdf Division of Actuarial Science Faculty of Commerce University of Cape Town
spellingShingle Lapham, Brendon M
Hawkes processes and some financial applications
thesis_degree_str Master's
title Hawkes processes and some financial applications
title_full Hawkes processes and some financial applications
title_fullStr Hawkes processes and some financial applications
title_full_unstemmed Hawkes processes and some financial applications
title_short Hawkes processes and some financial applications
title_sort hawkes processes and some financial applications
url http://hdl.handle.net/11427/8523
work_keys_str_mv AT laphambrendonm hawkesprocessesandsomefinancialapplications