Essays about: "generalized Gaussian distribution"
Showing result 1 - 5 of 11 essays containing the words generalized Gaussian distribution.
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1. Forecasting Volatility of Electricity Intraday Log Returns with Generalized Autoregressive Score Models
University essay from Göteborgs universitet/Graduate SchoolAbstract : We forecast volatility of electricity intraday log returns with Generalized Autoregressive Score (GAS) models. We extend our GAS models with variables representing the difference between the public’s expectation of weather and energy load and the actual outcome using a restricted ARMA(4,4) model. READ MORE
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2. Exploring Normalizing Flow Modifications for Improved Model Expressivity
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Normalizing flows represent a class of generative models that exhibit a number of attractive properties, but do not always achieve state-of-the-art performance when it comes to perceived naturalness of generated samples. To improve the quality of generated samples, this thesis examines methods to enhance the expressivity of discrete-time normalizing flow models and thus their ability to capture different aspects of the data. READ MORE
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3. Improving Zero-Shot Learning via Distribution Embeddings
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Zero-Shot Learning (ZSL) for image classification aims to recognize images from novel classes for which we have no training examples. A common approach to tackling such a problem is by transferring knowledge from seen to unseen classes using some auxiliary semantic information of class labels in the form of class embeddings. READ MORE
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4. A simple model of volatility in financial data - An alternative to GARCH models
University essay from Lunds universitet/Statistiska institutionenAbstract : Financial return series are often characterized by volatility clusters and a leptokurtic distribution. Many models that account for these properties exist, with the GARCH model proposed by Bollerslev (1986) being the most popular. This thesis explores an alternative model to capture the stochastic volatility in financial time series. READ MORE
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5. How Low Can You Go? : Quantitative Risk Measures in Commodity Markets
University essay from Uppsala universitet/Statistiska institutionenAbstract : 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. READ MORE