Sökning: "kreditrisk modeller"

Visar resultat 1 - 5 av 30 uppsatser innehållade orden kreditrisk modeller.

1. 1. Credit Index Forecasting: Stability of an Autoregressive Model

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Melker Wallén; Erik Grimlund; [2023]
   Nyckelord :Credit spreads; Time Series; Credit Risk; Index Modeling; Forecasting; Kreditspreadar; Tidsserier; Kreditrisk; Indexmodellering; Prognoser;

   Sammanfattning : This thesis investigates the robustness and stability of total return series for credit bond index investments. Dueto the challenges which arise for financial institutes and investors in achieving these objectives, we aim to createa forecasting model which matches the statistical properties of historical data, while remaining robust, stable andeasy to calibrate. LÄS MER

3. 2. 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

4. 3. Multi-factor approximation : An analysis and comparison ofMichael Pykhtin's paper “Multifactor adjustment”

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Michael Zanetti; Philip Güzel; [2023]
   Nyckelord :Credit risk; Value at Risk; Expected Shortfall; Monte Carlo simulation; Advanced Internal Rantings-Based models; Kreditrisk; Value at Risk; Expected Shortfall; Monte Carlo simulation; Advanced Internal Rantings-Based-modeller;

   Sammanfattning : The need to account for potential losses in rare events is of utmost importance for corporations operating in the financial sector. Common measurements for potential losses are Value at Risk and Expected Shortfall. These are measures of which the computation typically requires immense Monte Carlo simulations. LÄS MER

5. 4. 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

7. 5. Deep Learning Approach for Time- to-Event Modeling of Credit Risk

   Master-uppsats, KTH/Matematisk statistik

   Författare :Mehnaz Kazi; Natalija Stanojlovic; [2022]
   Nyckelord :Survival Analysis; Credit Risk; Credit Scoring; Time-To-Event; Default Probability; Överlevnadsanalys; Kreditrisk; Kreditprövning; Tid-till-utfall; Sannolikhet för fallissemang;

   Sammanfattning : This thesis explores how survival analysis models performs for default risk prediction of small-to-medium sized enterprises (SME) and investigates when survival analysis models are preferable to use. This is examined by comparing the performance of three deep learning models in a survival analysis setting, a traditional survival analysis model Cox Proportional Hazards, and a traditional credit risk model logistic regression. LÄS MER