[Forum SIS] Short Course on “Financial Tail Risk Forecasting”, Prof. Richard Gerlach, Discipline of Business Analytics - University of Sydney

[statlab@unisa.it statlab@unisa.it]

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:

https://www.dises.unisa.it/uploads/rescue/457/47/mappa.pdf
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