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Volatility of Bitcoin in a European Context

Sjöberg, Emilia LU (2019) STAN40 20182
Department of Statistics
Abstract
In 2009, Bitcoin was introduced to the world. Today, ten years later, there are still gaps in the research of how to model the cryptocurrency. In this thesis, the capacities of different volatility models to capture the high volatility of Bitcoin returns are investigated. The models used are GARCH-type models: GARCH(1,1), IGARCH(1,1) and GJR-GARCH(1,1). Jumps, or outliers, are also detected and filtered using two different methods to see how these observations affect the suitability of the models. The volatility of Bitcoin is modelled with other explanatory variables, namely the biggest stock market indices in Europe. The reason for this is to investigate if the volatility of the chosen indices can explain the volatility of Bitcoin. If so,... (More)
In 2009, Bitcoin was introduced to the world. Today, ten years later, there are still gaps in the research of how to model the cryptocurrency. In this thesis, the capacities of different volatility models to capture the high volatility of Bitcoin returns are investigated. The models used are GARCH-type models: GARCH(1,1), IGARCH(1,1) and GJR-GARCH(1,1). Jumps, or outliers, are also detected and filtered using two different methods to see how these observations affect the suitability of the models. The volatility of Bitcoin is modelled with other explanatory variables, namely the biggest stock market indices in Europe. The reason for this is to investigate if the volatility of the chosen indices can explain the volatility of Bitcoin. If so, the stock market indices may be used as a tool for forecasting the development of Bitcoin. The volatility of the stock market indices is also modelled with Bitcoin as an explanatory variable. The aim of this analysis is to examine if Bitcoin can be used as a tool for forecasting stock market events. After investigation, there are however no strong evidence that the volatility of Bitcoin can be explained by the volatility of the indices or vice versa.

Another conclusion is that the model assumptions are not completely fulfilled regardless of the chosen GARCH-type model when Bitcoin return is used as response variable. The innovations are autocorrelated and seem to follow a Laplace distribution. The modelling of Bitcoin volatility using GARCH-type models must therefore be supplemented, and one possible way of doing this is to filter the jumps. However, neither of the two methods used to detect and filter the jumps are fully satisfying, since, for example, the innovations still are autocorrelated after fitting GARCH-type models to the filtered Bitcoin returns. (Less)
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author
Sjöberg, Emilia LU
supervisor
organization
course
STAN40 20182
year
type
H1 - Master's Degree (One Year)
subject
keywords
GARCH, IGARCH, GRJ-GARCH, Jumps, Bitcoin, cryptocurrency, European market, Laplace distribution
language
English
id
8975096
date added to LUP
2019-06-11 09:21:14
date last changed
2019-06-11 09:21:14
@misc{8975096,
  abstract     = {In 2009, Bitcoin was introduced to the world. Today, ten years later, there are still gaps in the research of how to model the cryptocurrency. In this thesis, the capacities of different volatility models to capture the high volatility of Bitcoin returns are investigated. The models used are GARCH-type models: GARCH(1,1), IGARCH(1,1) and GJR-GARCH(1,1). Jumps, or outliers, are also detected and filtered using two different methods to see how these observations affect the suitability of the models. The volatility of Bitcoin is modelled with other explanatory variables, namely the biggest stock market indices in Europe. The reason for this is to investigate if the volatility of the chosen indices can explain the volatility of Bitcoin. If so, the stock market indices may be used as a tool for forecasting the development of Bitcoin. The volatility of the stock market indices is also modelled with Bitcoin as an explanatory variable. The aim of this analysis is to examine if Bitcoin can be used as a tool for forecasting stock market events. After investigation, there are however no strong evidence that the volatility of Bitcoin can be explained by the volatility of the indices or vice versa.

Another conclusion is that the model assumptions are not completely fulfilled regardless of the chosen GARCH-type model when Bitcoin return is used as response variable. The innovations are autocorrelated and seem to follow a Laplace distribution. The modelling of Bitcoin volatility using GARCH-type models must therefore be supplemented, and one possible way of doing this is to filter the jumps. However, neither of the two methods used to detect and filter the jumps are fully satisfying, since, for example, the innovations still are autocorrelated after fitting GARCH-type models to the filtered Bitcoin returns.},
  author       = {Sjöberg, Emilia},
  keyword      = {GARCH,IGARCH,GRJ-GARCH,Jumps,Bitcoin,cryptocurrency,European market,Laplace distribution},
  language     = {eng},
  note         = {Student Paper},
  title        = {Volatility of Bitcoin in a European Context},
  year         = {2019},
}