FIN-417: Quantitative risk management

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Lectures in this course (43)

Financial Time Series: Stylized Facts and Models

Explores financial time series, including ARMA and GARCH processes, emphasizing risk estimation.

Financial Time Series: GARCH Processes

Covers GARCH processes for financial time series analysis.

Financial Time Series: ARCH and GARCH Models

Covers regression analysis, multivariate linear regression, principal component analysis, and factor models.

ARCH and GARCH Models: Volatility Forecasting

Covers ARCH and GARCH models for volatility forecasting in risk-factor changes.

Copulas: Modeling Dependence in Financial Engineering

Explores the fundamentals of copulas and their role in modeling dependence in financial engineering.

Copulas: Properties and Applications

Covers copulas, Sklar's Theorem, meta distributions, and various dependence measures like rank correlations and coefficients of tail dependence.

Copulas: Properties and Applications

Covers the properties and applications of copulas, including examples and coefficients of tail dependence.

Dependence Measures: Rank Correlations

Covers rank correlations, tail dependence, and copula fitting methods.

Copula Densities: Tail Dependence

Covers copula densities and tail dependence in quantitative risk management.

Copulas: Dependence Structures and Simulation

Covers copulas, dependence structures, simulation techniques, and properties of copula densities.

Extreme Value Theory: GEV and GPD

Covers Extreme Value Theory, focusing on GEV and GPD distributions and the POT Model for threshold exceedances.

Copulas and Tail Dependence

Explores copulas, rank correlations, and tail dependence measures in risk management.

Quantitative Risk Management: Copulas and Generative Adversarial Networks

Explores copulas, simulation algorithms, fitting data with rank correlations, and GANs for image generation.

Credit Risk Management: Models and Challenges

Explores credit risk management challenges, models, and credit derivatives.

Modelling Dependence Among X's

Explores the impact of dependence among variables on credit risk modelling.

Extreme Value Theory: Limiting Distributions and Applications

Covers Extreme Value Theory, GEV distributions, GPD, and threshold exceedances.

Threshold Exceedances: Generalized Pareto Distribution

Explores threshold exceedances and the Generalized Pareto Distribution for modeling extreme data points above a specified level.

Credit Derivatives: Pricing and Risk Measures

Covers challenges in credit risk and pricing of credit derivatives.

Peaks-Over-Threshold Model: Implications and Estimation

Explores the Peaks-Over-Threshold model assumptions, implications, and estimation methods, including fitting a GPD model to exceedances.

Machine Learning in Credit Risk Modeling

Delves into the challenges and opportunities of machine learning in credit risk modeling, comparing traditional statistical models with machine learning methods.

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