Predicting Bitcoinʼs volatility
master's thesis
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: | 2022 |
| Typology: | 2.09 - Master's Thesis |
| Organization: | UL FMF - Faculty of Mathematics and Physics |
| Publisher: | [J. Pristovnik] |
| UDC: | 519.2 |
| COBISS: |
135369475
|
| Views: | 560 |
| Downloads: | 111 |
| Average score: | 0 (0 votes) |
| Metadata: |
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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 |
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