Guinea, Alvaro (2022) Incorporating market attention in option pricing with applications to Bitcoin derivatives. PhD thesis, University of York.
Abstract
The attention that media or investors pay to the market affects the prices of stocks
and assets. This attention is usually called market attention or market interest. It
has been shown that this attention affects stocks and indexes. In recent years, due
to the development of cryptocurrencies, there is an increasing literature that analyzes
the relation between cryptocurrency prices and market attention. Because the value
of cryptocurrencies has increased during the last years, new exchanges have appeared
that offer European options on Bitcoin.
In this thesis we develop six different models that incorporate market attention
into the modelling of Bitcoin option prices. Firstly, we construct two continuous
time models that incorporate market attention into the volatility structure. For these two
models we show how we can estimate the parameters and give a closed formula for
pricing European options. Then we construct two continuous time models that contain
jumps in the price structure to take into account that the distribution of Bitcoin
returns has fat tails. Again, for these models, we estimate the parameters and develop
a closed formula for pricing Bitcoin options. Lastly, we construct two discrete time
models in which the volatility is explained by the market attention but also by an
unobserved process. The estimation of these models is quite complex. Because of
that, we will use sequential Monte Carlo methods for the estimation of these models.
Metadata
Supervisors: | Alet, Roux |
---|---|
Keywords: | Option pricing; Bitcoin; Stochastic volatility models with delay; Time changed models; Sequential Monte Carlo methods |
Awarding institution: | University of York |
Academic Units: | The University of York > Mathematics (York) |
Identification Number/EthosID: | uk.bl.ethos.861195 |
Depositing User: | Mr Alvaro Guinea |
Date Deposited: | 14 Sep 2022 12:18 |
Last Modified: | 21 Oct 2022 09:53 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:31227 |
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