master's thesis
Jan Pristovnik (Author), Ljupčo Todorovski (Mentor), Bojan Basrak (Co-mentor)

Abstract

The master's thesis addresses analyzing and modeling the volatility of Bitcoin, the cryptocurrency with the largest marketcap. Volatility is a statistical measure of the dispersion of returns. We approximated it with realized volatility calculated on intra-daily log returns. We defined two baseline models based on a constant value and martingale property and tried to outperform them with both econometric and machine learning models. We used three error functions relative to our baseline models: MAE, MAPE, and RMSE. The best-performing econometric model is the HAR model. The best-performing machine learning model, which also outperforms the HAR model, is the LSTM recurrent neural network.

Keywords

volatility;forecasting;time series;cryptocurrencies;time-series analysis;machine learning;recurrent neural networks;LSTM;

Data

Language: English
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FMF - Faculty of Mathematics and Physics
Publisher: [J. Pristovnik]
UDC: 519.2
COBISS: 135369475 Link will open in a new window
Views: 560
Downloads: 111
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary title: Napovedovanje volatilnosti Bitcoina
Secondary abstract: Magistrsko delo obravnava analizo in modeliranje volatilnosti Bitcoina, kriptovalute z največjo tržno kapitalizacijo. Volatilnost je statistična mera razpršenosti donosov. Aproksimirali smo jo z realizirano historično volatilnostjo, na podlagi visoko frekvenčnih logaritemskih donosov. Definirali smo dva osnovna modela, bazirana na konstantni vrednosti in martingalski lastnosti, ter ju poskušali preseči z ekonometričnimi modeli in modeli strojnega učenja. Uporabili smo tri različne funkcije napak, relativno na naše osnovne modele: MAE, MAPE in RMSE. Najuspešnejši ekonometrični model je model HAR, najuspešnejši model strojnega učenja je rekurenčna nevronska mreža tipa LSTM. Slednja je boljša tudi od modela HAR.
Secondary keywords: volatilnost;napovedovanje;časovne vrste;kriptovalute;analiza časovnih vrst;strojno učenje;rekurenčne nevronske mreže;LSTM;
Type (COBISS): Master's thesis/paper
Study programme: 0
Thesis comment: Univ. v Ljubljani, Fak. za matematiko in fiziko, Oddelek za matematiko, Finančna matematika - 2. stopnja
Pages: XV, 73 str.
ID: 17547218