Prerequisites

MATLAB for Financial Applications and knowledge of risk management concepts

This program has been approved by GARP and qualifies for 7 GARP CPD credit hours. If you are a Certified FRM or ERP, please record this activity in your credit tracker at http://www.garp.org/cpd.

Market Risk Management with MATLAB

This one-day course provides a comprehensive introduction to market risk management using MATLAB^® and computational finance toolboxes. The course is intended for risk analysts, risk managers, portfolio managers, and other financial professionals with prior experience of MATLAB who require to analyze, assess, and manage market risk. The course uses examples from market risk, although the techniques demonstrated are applicable in most risk areas, including liquidity, interest-rate and operational risk. High-level course themes include:

Constructing baselines for market risk assessment and analysis
Assessing the impact of market risk and relative portfolio performance
Portfolio backtesting and computing commonly used risk metrics
Parametric and non-parametric market risk models
Monte Carlo simulation and analysis
Volatility risk and the SABR model
Creating and analyzing GARCH risk-oriented models
Extreme-value theory, copulas and filtered historical simulation
Value at risk backtesting

See detailed course outline

Prerequisites

MATLAB for Financial Applications and knowledge of risk management concepts

This program has been approved by GARP and qualifies for 7 GARP CPD credit hours. If you are a Certified FRM or ERP, please record this activity in your credit tracker at http://www.garp.org/cpd.

Day 1 of 1
Evaluating Portfolio Risk	Objective: Assess and analyze the market risk associated with a given portfolio of assets. Creating market and sector baselines Portfolio analytics Computing risk metrics for a given portfolio Evaluating portfolio betas Estimating relative portfolio risk
Modeling and Simulating Portfolio Risk	Objective: Model and simulate the market risk associated with a given portfolio of assets. Fitting parametric and non-parametric distributions to market and portfolio data Analyzing scenarios generated from distribution models Calibrating multivariate SDE risk models Monte Carlo simulation and analysis Reconstructing, analyzing and reducing data using principal components
Analyzing Implied Volatility Risk	Objective: Estimate and interpolate implied volatility curves and surfaces from option data observed in the market. Organizing option data Estimating implied volatility Interpolating volatility curves and surfaces Calibrating the SABR model
Creating and Analyzing Time-Series Risk Models	Objective: Create and analyze risk-oriented GARCH time-series models for the purpose of market risk analysis and simulation. Identifying autocorrelation and GARCH effects in time series Creating and fitting risk-oriented time-series models Modeling distribution tails using extreme-value theory Modeling correlated series using copulas Simulating from combined models
Bootstrapping and Filtered Historical Simulation	Objective: Perform market risk analysis by applying filtered historical simulation. Performing filtered historical simulation from the residuals of a model Automating analysis with bootstrapping
Validating Value at Risk Models	Objective: Assess and validate the effectiveness, performance and accuracy of value at risk and expected shortfall models using formal backtesting on historical market data. Preparing risk models for backtesting Performing hypothesis tests on predicted output values from risk models Summarizing and reporting backtest results Running multiple backtests

Guaranteed to Run

When you register for one of these courses, you can rely on the fact that it won't be canceled or rescheduled for any reason.

Course Schedule

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Dates	Location	Language	Price	Register
12 Dec 2019	United Kingdom, London	English	GBP 600

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