Sökning: "Volatility forecasting"
Visar resultat 16 - 20 av 153 uppsatser innehållade orden Volatility forecasting.
16. Volatility Forecasting with Artificial Neural Networks: Can we trust them?
Master-uppsats, Stockholms universitet/FinansieringSammanfattning : This thesis investigates how two types of artificial neural network models (ANN), feedforwardneural networks (FNN) and long short-term memory (LSTM), used for realized volatility (RV) forecasting, perform during high and low volatility regimes in comparison to the heterogeneousautoregressive (HAR) model. This is done for 23 stocks, constituents of the Swedish index OMXS30, between the 8th of February 2010 and the 31st of January 2022 using ten exogenous and three endogenous input variables. LÄS MER
17. The Influence of Gold Market on Bitcoin Prices : Is there a correlation?
Magister-uppsats, Jönköping University/Internationella HandelshögskolanSammanfattning : Background: This paper analyses the influence of fluctuation in gold market on bitcoin prices. Based on previous studies, in present market conditions, volatility in gold prices have caused price changes in several other major assets in the market, such as crude oil. Gold fluctuations are likely to stimulate uncertainty in some other major assets. LÄS MER
18. Volatility & The Black Swan : Investigation of Univariate ARCH-models, HARRV and Implied Volatility in Nasdaq100 amid Covid19
Master-uppsats, Uppsala universitet/Nationalekonomiska institutionenSammanfattning : Covid19 hit the world’s financial markets by surprise in March 2020 and ensuing volatility marked an end to the prior low-volatility environment. This Black Swan engendered numerous publications establishing how the equity market responded to the exogenous shock. LÄS MER
19. Symmetry or Asymmetry: A model comparison between different ARCH-class volatility models using Bitcoin returns
Magister-uppsats, Lunds universitet/Nationalekonomiska institutionenSammanfattning : This thesis will in turn evaluate the forecast performance of different ARCH-type models' forecast ability using Bitcoin returns from 01-04-2015 to 01-04-2022. More specifically, it is of interest to see if a simple GARCH(1,1) model can outperform more sophisticated models that incorporate the asymmetry in volatility. LÄS MER
20. Comparison of Indirect Inference and the Two Stage Approach
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Parametric models are used to understand dynamical systems and predict its future behavior. It is difficult to estimate the model’s parametric values since there are usually many parameters and they are highly correlated. LÄS MER