03145ntm a22005057i 4500 000715938 CZ-PrVSE 20240623000300.0 m d cr n|||||||||| 240622s2024 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Pavlyuk, Kateryna ISIS:119353 dis Odhad realizované volatility pomocí neuronových sítí eng Forecasting Realized Volatility Using Neural Networks / Kateryna Pavlyuk 2024 ?? stran : digital, PDF soubor Vedoucí práce: Milan Fičura Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví, 2024 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2024 This diploma thesis investigates the effectiveness of neural network models, encompassing both feedforward and LSTM architectures, in forecasting volatility across three major forex currency pairs. Traditional econometric models often struggle to capture the intricate dynamics of financial data, whereas NNs show the ability to cope with these complexities. The analysis focuses on applying both NN frameworks to volatility prediction and juxtaposing their performance against the commonly employed Heterogeneous Autoregressive models. The modeling phase entails deploying both feedforward and LSTM NNs while taking into account also supplementary parameters such as jumps, quarticities and semivariance. The findings indicate that simpler models like HAR demonstrate superior performance in out-of-sample forecasting owing to their enhanced generalization capabilities, whereas NNs exhibit strong in-sample performance. Upon examining the distribution of out-of-sample data, NNs demonstrate superiority up to the 95th percentile, particularly during extreme events, while HAR models excel in the most extreme scenarios. Notably, jumps and quarticities emerge as valuable additional parameters during extreme events. Způsob přístupu: Internet finanční inženýrství [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Realized volatility Neural Networks LSTM HAR Fičura, Milan vse2015859798 ths Drahokoupil, Matěj ISIS:119458 opn Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví kn20010709398 dgg https://insis.vse.cz/zp/78442/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/78442 Hlavní práce https://insis.vse.cz/zp/78442/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/78442/posudek/oponent/78915 Oponentura https://insis.vse.cz/zp/78442/podrobnosti dc:identifier NEPOSILAT VSKP vse78442 240619 78442