Aplikace umělé inteligence při předvídání volatility cen finančních aktiv - Mikulovská, Andrea; Fičura, Milan; Vyletelka, Michal; Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví (2021)
As the high-frequency trading and, in more general, high-frequency data modelling has come to the fore in the last decades, this thesis focuses on a such modelling. In high frequency data arises a problem with intraday seasonality which is not as easy to solve as interday or less frequent seasonality. The aim of this master thesis is modelling, predicting and coping with seasonality of 1-minute intraday data of the Tesla time series, using benchmark model MC-GARCH and comparing it with models of artificial neural networks, more precisely, feedforward neural network autoregression model with single hidden layer and LSTM network with two hidden layers, and also k-nearest neighbourhood algorithm was used. For each model, three different n-period ahead rolling predictions are made. Predictive ability of models is then compared using RMSE metric.
Vedoucí práce: Milan Fičura
Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví, 2022
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2022
Způsob přístupu: Internet
Keywords: finanční inženýrství [obor dipl. práce]; diplomové práce; master's theses