Sökning: "Student s t-distribution"

Visar resultat 16 - 20 av 30 uppsatser innehållade orden Student s t-distribution.

1. 16. How Low Can You Go? : Quantitative Risk Measures in Commodity Markets

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Johan Forsgren; [2016]
   Nyckelord :Value at Risk; Expected Shortfall; GARCH; EGARCH; Extreme Value Theory; GPD;

   Sammanfattning : The volatility model approach to forecasting Value at Risk is complemented with modelling of Expected Shortfalls using an extreme value approach. Using three models from the GARCH family (GARCH, EGARCH and GJR-GARCH) and assuming two conditional distributions, normal Gaussian and Student t’s distribution, to make predictions of VaR, the forecasts are used as a threshold for assigning losses to the distribution tail. LÄS MER

3. 17. Imputation of Missing Data with Application to Commodity Futures

   Master-uppsats, KTH/Matematisk statistik

   Författare :Simon Östlund; [2016]
   Nyckelord :Missing Data; Bayesian Statistics; Expectation Conditional Maximization ECM ; Conditional Distribution; Robust Regression; MCMC; Copulas.; Saknad Data; Bayesiansk Statistik; Expectation Conditional Maximization ECM ; Betingad Sannolikhet; Robust Regression; MCMC; Copulas.;

   Sammanfattning : In recent years additional requirements have been imposed on financial institutions, including Central Counterparty clearing houses (CCPs), as an attempt to assess quantitative measures of their exposure to different types of risk. One of these requirements results in a need to perform stress tests to check the resilience in case of a stressed market/crisis. LÄS MER

4. 18. Volatility Forecasting In the Nordic Stock Market

   Kandidat-uppsats, Lunds universitet/Nationalekonomiska institutionen

   Författare :Niklas Hummel; [2015]
   Nyckelord :GARCH; volatility; forecasting; Business and Economics;

   Sammanfattning : This paper studies volatility prediction on OMX Stockholm 30, OMX Helsinki 25 and OMX Nordic 40. The models used are a historical variance model, an exponentially weighted moving average model and three models from the GARCH family. These are GARCH(1,1), EGARCH(1,1) and GJR(1,1), with normal and t-distribution respectively. LÄS MER

5. 19. Rising household consumer debt: Good or bad? Empirical research on U.S. stock market volatility using normal mixture GARCH-MIDAS model

   D-uppsats, Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi

   Författare :Hang Le; [2015]
   Nyckelord :Household debt; Income Inequality; Stock market volatility; Mixed Data Sampling; Normal Mixture Distribution;

   Sammanfattning : Household debt has been on the continuous rise to raise concern for its sustainability and its consequences to the financial system and the macro-economy as a whole. In this paper, I review empirical work on the growth of total household consumer debt ratio on long-term component of stock market volatility. LÄS MER

7. 20. A Regime Switching Model - Applied to the OMXS30 and Nikkei 225 indices

   Master-uppsats, Göteborgs universitet/Graduate School

   Författare :Ludvig Hjalmarsson; [2014-07-23]
   Nyckelord :;

   Sammanfattning : This Master of Science thesis investigates the performance of a Simple Regime Switching Model compared to the GARCH(1,1) model and rolling window approach. We also investigate how these models estimate the Value at Risk and the modified Value at Risk. The underlying distributions that we use are normal distribution and Student’s t-distribution. LÄS MER