Presented by Dr Malvina Marchese (Cass Business School, City, University of London)
Course Timetable: 10am - 12pm & 2pm - 4pm
Risk modelling is about modelling and quantification of risk. For the financial industry, the cases of credit-risk quantifying potential losses due, e.g., to bankruptcy of debtors, or market-risks quantifying potential losses due to negative fluctuations of a portfolio's market value are of particular relevance.
The aim of this course is to offer a comprehensive introduction to risk modelling and forecasting with EViews 12. The course offers one introductory day on risk modelling and forecasting via parametric methods and then builds on it to discuss the econometrics methods for stress testing, Value at Risk and fundamental measures of risk.
The course targets researchers, practitioners and policy makers who are interested in gaining an in-depth knowledge of risk modelling and forecasting techniques and learning how to use them in their current research.
Each session briefly introduces the different methodologies, discussing strengths and weaknesses with a focus on the interpretation of the results. The sessions are hands on with many practical examples of risk models and evaluation tools. Specific attention is dedicated to risk forecasting.
By the end of the two-day online course participants should be able to model and forecast risk with EViews 12 proficiently.
Day 1: Risk Modelling using EViews
- Session 1: Risk Models. Risk forecasting. Risk forecasting evaluation tools. Market and Credit Risk Models. Univariate short memory GARCH models for volatilities
- Session 2: Univariate long memory GARCH models for volatilities. Multivariate GARCH models. Global Minimum Variance portfolios
Day 2: Value at Risk
- Session 1: Value at Risk and Conditional Value at Risk. The RiskMetrics. Forecasting V@R under different distributional assumptions in EViews.
- Session 2: The historical simulation approach , Monte Carlo simulation approach in EViews , the parametric approach with MGARCH in EViews
Basic knowledge of linear regression and time series of econometrics is assumed. An introductory level of EViews helps but is not necessary
Terms & Conditions
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations.
- Attendees are provided with temporary licenses for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course.
- Payment of course fees required prior to the course start date.
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.