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Visar resultat 6 - 10 av 76 uppsatser som matchar ovanstående sökkriterier.
6. Modelling Risk in Real-Life Multi-Asset Portfolios
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : We develop a risk factor model based on data from a large number of portfolios spanning multiple asset classes. The risk factors are selected based on economic theory through an analysis of the asset holdings, as well as statistical tests. LÄS MER
7. Copula approach to fitting bivariate time series
Master-uppsats, Lunds universitet/Matematisk statistikSammanfattning : We apply the GARCH-copula method to estimate Value at Risk (VaR) for European and Stockholm stock indices. First, marginal distributions are estimated by the ARMA-GARCH model with normal, Student-t, and skewed t distributions. LÄS MER
8. En magisk investeringsstrategi på Sveriges aktiemarknad : En undersökning av den magiska formeln ijämförelse med OMXS30
Kandidat-uppsats, Uppsala universitet/Företagsekonomiska institutionenSammanfattning : Avsikten med studien är att undersöka den magiska formelns prestation på den svenska aktiemarknaden mellan åren 2017–2021. Syftet är att undersöka om denmagiska formeln kan uppnå en högre riskjusterad avkastning än OMXS30 underundersökningsperioden. LÄS MER
9. Backtesting Expected Shortfall : A qualitative study for central counterparty clearing
Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistikSammanfattning : Within Central Counterparty Clearing, the Clearing House collects Initial Margin from its Clearing Members. The Initial Margin can be calculated in many ways, one of which is by applying the commonly used risk measure Value-at-Risk. However, Value-at-Risk has one major flaw, namely its inability to encapsulate Tail Risk. LÄS MER
10. Predicting stock trading outcomes with deep learning
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : Multiple deep learning approaches are applied on price data of Swedish stocks, including traditional artificial neural networks as well as recurrent neural networks. The trained models are evaluated using average returns from backtesting rather than training and/or validation error in order to approximate real-world trading performance. LÄS MER