Volatility Forecasting An Empirical Study on Bitcoin Using Garch and Stochastic Volatility models
(2018) NEKP01 20181Department of Economics
- Abstract (Swedish)
- Cryptocurrencies are on the rise, with new financial assets, new frameworks
need to be developed. This thesis sets out to the examine the GARCH(1,1),
the bivariate-BEKK(1,1), and the Standard stochastic volatility model’s volatility
forecasting performance on BTC/USD, where the bivariate model is estimated
on both BTC/USD and ETH/USD closing price data. Furthermore,
three loss functions are used to evaluate the forecast accuracy for each model.
The functions are estimated using realized volatility based on BTC/USD data
on a minute per minute basis. The result indicates that the GARCH(1,1) is
the model that performs best regarding forecast accuracy. All three loss functions
rank the models accordingly; first the GARCH(1,1), second... (More) - Cryptocurrencies are on the rise, with new financial assets, new frameworks
need to be developed. This thesis sets out to the examine the GARCH(1,1),
the bivariate-BEKK(1,1), and the Standard stochastic volatility model’s volatility
forecasting performance on BTC/USD, where the bivariate model is estimated
on both BTC/USD and ETH/USD closing price data. Furthermore,
three loss functions are used to evaluate the forecast accuracy for each model.
The functions are estimated using realized volatility based on BTC/USD data
on a minute per minute basis. The result indicates that the GARCH(1,1) is
the model that performs best regarding forecast accuracy. All three loss functions
rank the models accordingly; first the GARCH(1,1), second the bivariate-
BEKK(1,1), and finally the Stochastic volatility model. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8958504
- author
- Hultman, Hugo LU
- supervisor
- organization
- course
- NEKP01 20181
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Volatility forecasting, Stochastic volatility, GARCH, Bitcoin
- language
- English
- id
- 8958504
- date added to LUP
- 2018-09-26 09:13:39
- date last changed
- 2018-09-26 09:13:39
@misc{8958504, abstract = {{Cryptocurrencies are on the rise, with new financial assets, new frameworks need to be developed. This thesis sets out to the examine the GARCH(1,1), the bivariate-BEKK(1,1), and the Standard stochastic volatility model’s volatility forecasting performance on BTC/USD, where the bivariate model is estimated on both BTC/USD and ETH/USD closing price data. Furthermore, three loss functions are used to evaluate the forecast accuracy for each model. The functions are estimated using realized volatility based on BTC/USD data on a minute per minute basis. The result indicates that the GARCH(1,1) is the model that performs best regarding forecast accuracy. All three loss functions rank the models accordingly; first the GARCH(1,1), second the bivariate- BEKK(1,1), and finally the Stochastic volatility model.}}, author = {{Hultman, Hugo}}, language = {{eng}}, note = {{Student Paper}}, title = {{Volatility Forecasting An Empirical Study on Bitcoin Using Garch and Stochastic Volatility models}}, year = {{2018}}, }