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 o... show full abstractAs 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. |