Essays about: "Volatility Clustering"

Showing result 6 - 10 of 26 essays containing the words Volatility Clustering.

  1. 6. Understanding Traffic Cruising Causation : Via Parking Data Enhancement

    University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Author : Mirza Jasarevic; [2021]
    Keywords : Machine Learning; Unsupervised Learning; Time Series; Parking; Dynamic Time Warping;

    Abstract : Background. Some computer scientists have recently pointed out that it may be more effective for the computer science community to focus more on data preparation for performance improvements, rather than exclusively comparing modeling techniques. READ MORE

  2. 7. Hierarchical Clustering in Risk-Based Portfolio Construction

    University essay from KTH/Matematisk statistik

    Author : Natasha Nanakorn; Elin Palmgren; [2021]
    Keywords : Portfolio construction; asset allocation; risk-based asset allocation; hierarchical clustering; agglomerative clustering; hierarchical risk parity; risk; volatility; Portföljallokering; portföljhantering; portföljmetoder; riskbaserad portföljallokering; hierarkisk klustring; agglomerativ klustring; risk; volatilitet;

    Abstract : Following the global financial crisis, both risk-based and heuristic portfolio construction methods have received much attention from both academics and practitioners since these methods do not rely on the estimation of expected returns and as such are assumed to be more stable than Markowitz's traditional mean-variance portfolio. In 2016, Lopéz de Prado presented the Hierarchical Risk Parity (HRP), a new approach to portfolio construction which combines hierarchical clustering of assets with a heuristic risk-based allocation strategy in order to increase stability and improve out-of-sample performance. READ MORE

  3. 8. Modelling Dependency Structure with Application in Financial Markets: Copula-GARCH(1,1) Approach

    University essay from Linnéuniversitetet/Institutionen för matematik (MA)

    Author : Than Trang; [2021]
    Keywords : Copula-GARCH 1; 1 ;

    Abstract : The main objective of this thesis is to examine the dependency structure among different agricultural and energy commodity markets in the United States. For achieving this goal, the paper makes use of the Copula-GARCH(1,1) model to study the financial return volatility and the co-movement between pair of commodities including corn, soybean and gasoline over the pre-COVID 19 pandemic period (from 01-01-2018 to 01-01-2020) and the ongoing COVID 19 pandemic period (from 01-01-2020 to 01-04-2021). READ MORE

  4. 9. Modeling asymmetry in volatility response - non-Gaussian innovations approach

    University essay from Lunds universitet/Statistiska institutionen

    Author : Ludvig Göransson; [2020]
    Keywords : ARCH; GARCH; APARCH; Asymmetric GARCH; non-Gaussian innovations; Laplace distribution; Leverage effect; Stylized facts; Volatility process.; Mathematics and Statistics;

    Abstract : This thesis is an explorative note on the non-Gaussian innovations of the volatility process. More specifically, the thesis investigates if the decomposition of the Standard Classical Laplace (SCL) distribution to a difference of two exponential is a valid alternative to modelling the asymmetric volatility processes, taking volatility clustering, the leverage effect and asymmetric response in volatility into account. READ MORE

  5. 10. Emission Allowances in the European Union Emissions Trading System

    University essay from Handelshögskolan i Stockholm/Institutionen för finansiell ekonomi

    Author : Franziska Manke; [2020]
    Keywords : Emission Allowances; EU ETS; Volatility; GARCH; Cointegration;

    Abstract : The first part of the thesis analyses the short term behavior of daily emission allowance (EUA) log returns with a focus on volatility dynamics in the recent market environment. In this part, I present a historical overview of the European Union Emission Trading System (EU ETS), analyze the stylized facts of the time series, employ appropriate time series models, and assess model in-sample and out-of-sample performance. READ MORE