Scienze del Governo e dell'Amministrazione | Seminari Didattici | Presentazioni e Incontri
prof. Richard Gerlach - University of Sydney
Short Course on “Financial Tail Risk Forecasting”
prof. Richard Gerlach - University of Sydney
Aula Multimediale Economia (Scienze ECONOMICHE E STATISTICHE) (Edificio C2)
Short Course on
“Financial Tail Risk Forecasting”
prof. Richard Gerlach
Discipline of Business Analytics
University of Sydney
Abstract:
Quantitative financial tail risk measurement and forecasting provide a fundamental toolkit for financial risk management, investment decisions, capital allocation and external regulation. Value-at-Risk (VaR) and Expected Shortfall (ES) are tail risk measures that are employed, as part of this toolkit, to measure and control financial risk. In this tutorial, we begin with an introduction to the most common tail risk measures: Value-at-Risk (VaR) & Expected Shortfall (ES) and to the three main types of the financial tail risk forecasting models in the literature: parametric, non-parametric and semi-parametric. We also discuss various realized measures of volatility, commonly used in literature. Second, we focus on introducing and implementing parametric models like GARCH, GJR-GARCH and EGARCH and examine their performance as tail risk forecasting models for financial return data from the S&P500 index. Next, several semi-parametric tail risk forecasting models, starting with the well-known Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaR) model (Engle and Manganelli, 2004), then the Conditional Autoregressive Expectile (CARE, Taylor, 2008), model which jointly estimates quantiles, expectiles and implicitly ES too, are introduced and examined. An introduction to estimation, both frequentist via loss functions and Bayesian via MCMC methods, is given. Then, the realized GARCH model of Hansen et al 2011 is presented, followed by a semi-parametric Realized-CARE framework of models and their implementation is presented. This latter framework extends the CARE model by incorporating a measurement equation that contemporaneously links the latent conditional expectile with the realized measure. Next, the recent finding of a class of joint VaR and ES loss functions motives the development of semi-parametric tail risk models that jointly estimate and forecast VaR and ES. We start this part with introducing a joint ES and quantile regression framework (based on the ES-CAViaR, Taylor, 2018). Then two innovative frameworks extending that model class, which allow separate dynamics for the ES equation and/or allow a separate measurement equation are presented. The tutorial will be conducted using Matlab, and many illustrations through real forecasting examples of financial return series will be presented and discussed. Participants will be provided with Matlab code and encouraged to (perhaps bring their laptops with Matlab installed or use Matlab on the PCs in the lab) to replicate our work and examples during and after the workshop.
Il corso si terrà nelle seguenti date e orari presso l’Aula Informatica e Multimediale del DISES:
2/10/2019 ore 14.30 – 17.30
3/10/2019 ore 8.30 – 11.30
8/10/2019 ore 14.30 – 17.30
Mappa:
