COURSE OVERVIEW

 

The growth in financial instruments during the last decade has resulted in a significant development of econometric methods (financial econometrics) applied to financial data. The objective of our Multivariate Garch Models for Risk Management course is to provide participants with a comprehensive overview of the principal methodologies, both theoretical and applied, adopted for the analysis of risk in financial markets. To this end, the course focuses on the modelling and forecasting of financial time series and in particular modelling returns and volatility in asset returns; the modelling of cross market correlations, volatility spill-overs and contagion in financial asset markets; and the implementation of both factor models and principal components analysis for the identification of specific asset, country and global factors. The course concludes with an analysis of the available risk management tools/measures widely adopted in academia and the financial sector. During the course, a number of alternative GARCH models, models of conditional correlations, and Value at Risk models will be reviewed.

 

TARGET AUDIENCE

 

The course is of particular interest to: i) Master and Ph.D. Students and researchers in public and private research centres, and ii) professionals employed in risk management in the following sectors: asset management, exchange rate and market risk analysis, front office and research in investment banking and insurance, needing to acquire the necessary econometric/statistical toolset to independently conduct an empirical analysis of financial risk.

 

COURSE REQUISITES

 

Participants are required to have a basic knowledge of either econometrics or statistics. Previous experience with statistical software will facilitate the practical sessions.


PROGRAM

 

SESSION I: UNIVARIATE AND MULTIVARIATE CONDITIONAL MEAN FORECASTING

 

Estimation and forecasting: ARMA (p,q) Processes, Exponential Smoothing (ES), Holt-Winter’s ES (HWES)

 

Forecast Evaluation: ME, MAE, MSE, RMSE, Theil’s U, Diebold-Mariano test. Combination of Forecasts

 

Vector Autoregressive (VAR) models to model interdependencies

 

Empirical Applications: modelling and forecasting returns and equity premium, term structure and the bond markets, foreign exchange rates. Yield curve forecasting

 

SESSION II: VOLATILITY MODELS: GARCH

 

Analysis of financial time series features: stationarity, autocorrelation, conditional heteroscedasticity, fat tails

 

Modelling and forecasting asset returns volatility with univariate ARCH and GARCH models:

 

ARCH, GARCH, GARCH-in-mean
Integrated GARCH
RiskMetrics
Modelling asymmetric shock impacts on volatility with asymmetric GARCH models:
SAARCH
EGARCH
GJR
TGARCH
APARCH
News Impact Curve

 

SESSIONS III / IV: MULTIVARIATE VOLATILITY (MGARCH) MODELS. CONDITIONAL CORRELATION MODELS AND CONTAGION

 

Modelling cross-markets correlations and testing for volatility spillovers with:

 

Diagonal VECH (DVECH)
Constant Conditional Correlation (CCC)
Dynamic Conditional Correlation (DCC) models

 

 

 

 

Assessing contagion in fi nancial markets. Testing for high moments contagion cross-market correlation coeffi cients, Markov switching regressions, higher moments contagion

 

Empirical applications: forecasting volatility and correlations in fi nancial markets. Contagion between markets

 

SESSION V: FACTOR MODELS

 

Static and dynamic factors, factor estimation, determining the number of factors, nonstationary factor models

 

Identifying global, asset related and country specific factors in data with a large number of assets with principal component analysis and static and dynamic factor models

 

Applications of factor analysis to (bond and asset) portfolio management, stock liquidity and its determinants

 

SESSION VI: RISK MANAGEMENT TOOLS

 

Porfolio Value-at-Risk (VaR)
Parametric VaR
Historical simulation VaR
Monte Carlo VaR
Expected Shortfall (ES) and Tail Risk (TR)
Backtesting procedures
Unconditional coverage
Independence

Conditional coverage
Duration based tests of independence

 

 

USEFUL TEXTS

 

S. Boffelli and G. Urga (2016), Financial Econometrics Using Stata. Stata Press Publication.