Sökning: "Credit Risk Modelling"

Visar resultat 1 - 5 av 38 uppsatser innehållade orden Credit Risk Modelling.

1. 1. Modelling Proxy Credit Cruves Using Recurrent Neural Networks

   Master-uppsats, KTH/Matematisk statistik

   Författare :Lucas Fageräng; Hugo Thoursie; [2023]
   Nyckelord :Deep Neural Networks; Credit Risk; Financial Modelling; LSTM; Credit Default Swaps; Credit Valuation Adjustment; Djupa Neurala Nätverk; Kreditrisk; Finansiell Modellering; LSTM; Kreditswappar; Kreditvärderingsjustering;

   Sammanfattning : Since the global financial crisis of 2008, regulatory bodies worldwide have implementedincreasingly stringent requirements for measuring and pricing default risk in financialderivatives. Counterparty Credit Risk (CCR) serves as the measure for default risk infinancial derivatives, and Credit Valuation Adjustment (CVA) is the pricing method used toincorporate this default risk into derivatives prices. LÄS MER

3. 2. A Study of Risk Factor Models: Theoretical Derivations and Practical Applications

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Yuanlin Dong; [2023]
   Nyckelord :interest rates; foreign exchange rates; models; counterparty credit risk; räntor; valutakurser; modeller; motpartsrisk;

   Sammanfattning : This thesis provides an end-to-end picture of the modelling of interest rates and Foreign Exchange (FX) rates. We start by defining the FX rates and the interest rates. After having a good understanding of the basics, we take a deep dive into the approaches commonly used to model interest rates and FX rates respectively. LÄS MER

4. 3. Portfolio Risk Modelling in Venture Debt

   Master-uppsats, KTH/Matematisk statistik

   Författare :John Eriksson; Jacob Holmberg; [2023]
   Nyckelord :Startup Default Probability; Venture Debt; Gaussian Copula; Value-at-Risk; Expected Shortfall; Exposure at Default; Loss Given Default; Forecast; Linear Dynamic System; ARIMA Time Series; Monte Carlo Simulation; Linear Regression; Central Limit Theorem;

   Sammanfattning : This thesis project is an experimental study on how to approach quantitative portfolio credit risk modelling in Venture Debt portfolios. Facing a lack of applicable default data from ArK and publicly available sets, as well as seeking to capture companies that fail to service debt obligations before defaulting per se, we present an approach to risk modeling based on trends in revenue. LÄS MER

5. 4. Credit Exposure Modelling Using Differential Machine Learning

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Måns Karp; Samuel Wagner; [2023]
   Nyckelord :Counterparty credit risk; Differential machine learning; Exposure modelling; Heston model; Option pricing; Mathematics and Statistics;

   Sammanfattning : Exposure modelling is a critical aspect of managing counterparty credit risk, and banks worldwide invest significant time and computational resources in this task. One approach to modelling exposure involves pricing trades with a counterparty in numerous potential future market scenarios. LÄS MER

7. 5. Applying the Shadow Rating Approach: A Practical Review

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Viktor Barry; Carl Stenfelt; [2023]
   Nyckelord :Shadow Rating; probability of default; low default portfolio; credit risk; statistical learning; financial regulation; Basel; Pluto and Tasche; Skuggrating; sannolikhet av fallissemang; lågfallissemangsportfölj; kreditrisk; statistisk inlärning; finansiella regelverk; Basel; Pluto och Tasche;

   Sammanfattning : The combination of regulatory pressure and rare but impactful defaults together comprise the domain of low default portfolios, which is a central and complex topic that lacks clear industry standards. A novel approach that utilizes external data to create a Shadow Rating model has been proposed by Ulrich Erlenmaier. LÄS MER