Essays about: "modelling Financial risk"

Showing result 1 - 5 of 55 essays containing the words modelling Financial risk.

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

   University essay from KTH/Matematisk statistik

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

   Abstract : 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. READ MORE

3. 2. Volatility Modelling in the Swedish and US Fixed Income Market : A comparative study of GARCH, ARCH, E-GARCH and GJR-GARCH Models on Government Bonds

   University essay from Linköpings universitet/Nationalekonomi; Linköpings universitet/Filosofiska fakulteten

   Author : Sebastian Mortimore; William Sturehed; [2023]
   Keywords : GARCH; ARCH; GJR-GARCH; E-GARCH; ARMA; Government Bonds; Volatility; Loss functions; Fixed Income Market and realized volatility.; ARCH; GARCH; GJR-GARCH; E-GARCH; Statsobligationer och Volatilitet;

   Abstract : Volatility is an important variable in financial markets, risk management and making investment decisions. Different volatility models are beneficial tools to use when predicting future volatility. The purpose of this study is to compare the accuracy of various volatility models, including ARCH, GARCH and extensions of the GARCH framework. READ MORE

4. 3. Portfolio Risk Modelling in Venture Debt

   University essay from KTH/Matematisk statistik

   Author : John Eriksson; Jacob Holmberg; [2023]
   Keywords : 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;

   Abstract : 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. READ MORE

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

   University essay from KTH/Matematik (Avd.)

   Author : Viktor Barry; Carl Stenfelt; [2023]
   Keywords : 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;

   Abstract : 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. READ MORE

6. 5. Risk Assessment of International Mixed Asset Portfolio with Vine Copulas

   University essay from Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakulteten

   Author : Axel Nilsson; [2022]
   Keywords : Vine Copulas; Extreme Value Theory; Financial Risk Management; Vine Copulas; Extremvärdesteori; Finansiell riskhantering;

   Abstract : This thesis gives an example of assessing the risk of a financial portfolio with international assets, where the assets may be of different classes, by the use of Monte Carlo simulation and Extreme Value Theory. The simulation uses univariate modelling, models of the assets’ returns as stochastic processes, as well as vine copulas to create dependency between the variables. READ MORE