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Three Essays on Data-Driven Methods in Asset Pricing and Forecasting
Gregor, Barbora ; Baruník, Jozef (advisor) ; Chen, Cathy Yi-Hsuan (referee) ; Baumohl, Eduard (referee) ; Vácha, Lukáš (referee)
This dissertation thesis consists of three papers focusing on applications of data-driven methods in asset pricing and forecasting. In the first paper, we decompose the term structure of crude oil futures prices using dynamic Nelson-Siegel model and propose to forecast them with the generalized regression framework based on neural networks. We find the neural networks to produce significantly more accurate forecasts as compared to several benchmark models. The second paper demonstrates how time-varying coefficients model can help to explore dynamics in risk-return trade-off on sovereign bond market across entire term structure. Our extensive 12-year dataset of high-frequency data of U.S. and German sovereign bond prices of 2-year, 5-year, 10-year and 30-year tenors allows us to construct realized measures of risk as well as exploring risk-return relationship under various market conditions. In addition to realized volatility, we find realized kurtosis to be priced in bond returns. Importantly, we detect the risk factor captured by realized kurtosis to have positive effect on returns in crisis turning to negative values in calm periods. In the third paper, we use time- varying coefficients methodology and higher realized moments in bond volatility forecasting challenging the HAR model. We detect realized...
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Term structure of interest rates
Boháčková, Jana ; Hurt, Jan (advisor) ; Rusý, Tomáš (referee)
Bachelor thesis deals with interest rates and yield curves. Terms spot interest rate, forward interest rate and discount factor are established. Three models for describing yield curves are used, two parametric models: Nelson-Siegel model and Svensson model and one nonparametric model: kernel estimator. Function of a yield curve is decribed for all models and for parametric models and the parameters in parametric models are also described. Eventually, all models are used on real data. 1
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Forecasting Term Structure of Government Bonds Using High Frequency Data
Kožíšek, Jakub ; Baruník, Jozef (advisor) ; Horváth, Roman (referee)
This thesis investigates the use of realized volatility features from high frequency data in com- bination with neural networks to improve forecasts of the yield curve of government bonds. I use high frequency data on futures of four U.S. Treasury securities to estimate the Nelson-Siegel yield curve and realized variance of its parameters over the period of 25 years. The estimated parameters are used in prediction of the level, slope and curvature of the yield curve using an LSTM neural network and compared to the Dynamic Nelson-Siegel model. Results show that the use of realized variance and neural network outperforms autoregressive methods in prediction of the level and curvature in daily and monthly forecasts. The yield curve of government bonds itself has a predictive power on multiple macroeconomic variables, therefore improvements in its forecastability may have broader implications on forecasting the overall state of the economy.
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Forecasting Term Structure of Crude Oil Markets Using Neural Networks
Malinská, Barbora ; Baruník, Jozef (advisor) ; Polák, Petr (referee)
This thesis enhances rare literature focusing on modeling and forecasting of term structure of crude oil markets. Using dynamic Nelson-Siegel model, crude oil term structure is decomposed to three latent factors, which are further forecasted using both parametric and dynamic neural network approaches. In-sample fit using Nelson-Siegel model brings encouraging results and proves its applicability on crude oil futures prices. Forecasts obtained by focused time-delay neural network are in general more accurate than other benchmark models. Moreover, forecast error is decreasing with increasing time to maturity.
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