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Time Series Methods for Risk Analysis

Time Series Methods for Risk Analysis

Learn with our expert faculty

facultyLuca Gambetti
PhD, Universitat Pompeu Fabra
UAB and BSE
Director
facultyChristian Brownlees
PhD, University of Florence
UPF and BSE
Instructor

Course overview

Identifying, quantifying and predicting macroeconomic and financial risk is key for the decision making process and the design of sound economic policies. The geopolitical events of the last years have exacerbated the need of techniques and tools to analyze and understand risk. This course presents an in-depth overview of the state-of-art techniques for risk in macroeconomics and finance. After this course, which include both theory sessions and hands-on exercises in practical sessions, you will be able to apply these methods to your own research.

The first part of the course introduces univariate time series models used for the analysis of time-varying volatility (GARCH models); multivariate time series models for the analysis of time-varying correlations (DCC models) and the quantile regression model for the analysis of downside risk. The second part of the course presents empirical applications. In the first application, GARCH-DCC models are used to construct a number of systemic risk measures recently proposed in the literature (CoVaR, SRISK). In the second application GARCH models and the quantile regression model are used for Growth-at-Risk forecasting.

Learn risk modeling methodologies to get key insights on the evolution of macroeconomic and financial risk

The course consists of theory sessions (10 hours) and practical sessions (10 hours). During the practice sessions, we will use MATLAB to replicate the methodology as well as the empirical findings documented in the lectures.

You will participate in an active and fruitful environment with international colleagues without incurring high costs, thanks to online live delivery.

Sessions will be recorded and videos will be available for a month once the course has finished.

Get up to speed with the latest developments of the econometric literature on risk modeling and get practical hand-on experience

Central bankers, finance professionals, and academics took part in this course last year. You can read more about the participants from last year here.

This course will be taught online but it will be live and interactive. 

INTENSIVE COURSE

Time Series Methods for Risk Analysis

Applications will open soon!
  ONLINE
Regular Fee 1250 €
Reduced Fee 750 €

10% early-bird discount applies to payments made on or before January 27, 2023 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Early-bird payment deadline: January 27, 2023

  ONLINE
Regular Fee 1250 €
Reduced Fee 750 €

10% early-bird discount applies to payments made on or before January 27, 2023 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Last day to apply: February 23, 2023

  ONLINE
Regular Fee 1250 €
Reduced Fee 750 €

10% early-bird discount applies to payments made on or before January 27, 2023 at 23:59 (CET)

See below for reduced fee eligibility

This edition is closed. Next edition TBA.

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Schedule

Times listed are Central European Time (CET). Compare with your time zone on time.is

Schedule for Week 1
Day / Time Mon Tue Wed Thu Fri
14:30 - 16:30 Lecture Practical Lecture Practical Lecture
Schedule for Week 2
Day / Time Mon Tue Wed Thu Fri
14:30 - 16:30 Practical Lecture Practical Lecture Practical

Course content

Read an outline of topics and bibliography for this course

Course outline

  1. Modeling and Forecasting Time-Varying Volatility using GARCH Models
    GARCH models: Properties, inference and prediction. Volatility Forecast Evaluation.
  2. Modeling and Forecasting Time-Varying Correlations using DCC Models
    DCC models: Properties, inference and prediction
  3. Modeling and Forecasting Quantile using Quantile Regression
    Quantile regression: Inference and prediction. Variable selection.
  4. Applications: Systemic Risk
    CoVaR, MES and SRISK prediction using GARCH-DCC models in the 2007-2009 financial crisis.
  5. Applications: Growth-At-Risk
    GaR prediction using GARCH and quantile regression in the largest OECD economies.

References

  • Adrian, T. , Boyarchenko, N. and Giannone, D. (2019) Vulnerable growth. American Economic Review, 109, 1263–1289 .
  • Adrian, T. and Brunnermeier, M. K. (2016). CoVaR. American Economic Review , 106, 1705–1741.
  • Alizadeh, S., Brandt, M. W., and Diebold, F. X. (2002). Range-based estimation of stochastic volatility models. The Journal of Finance, 57, 1047–1091.
  • Andersen, T. G. and Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review , 39, 885–905.
  • Andersen, T. G., Bollerslev, T., Diebold, F. X., and Ebens, H. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 61, 43–76.
  • Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71, 579–625.
  • Bollerslev, T. (2008). Glossary to arch (garch). Research Paper 2008-49.
  • Bollerslev, T., Engle, R., and Nelson, D. B. (1994). ARCH Models, Handbook of Econometrics, volume IV, chapter 49, pages 2959–3038. Elsevier Science B.V.
  • Brownlees, C. and Engle, R. (2016). SRISK: A Conditional Capital Shortfall Measure of Systemic Risk. Review of Financial Studies, forthcoming.
  • Brownlees, C. and Souza, M. B. A. (2021). Backtesting Global Growth-at-Risk. Journal of Monetary Economics, 118, 312-330
  • Cappiello, L., Engle, R., and Shephard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.
  • Christoffersen, P. F. (1998). Evaluation of interval forecasts. International Economic Review, 39, 841–862.
  • Ding, Z., Granger, C. W. J., and Engle, R. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106. 
  • Engle, R. (2002a). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20, 339–350.
  • Engle, R. (2002b). New frontiers for ARCH models. Journal of Applied Econometrics, 17, 425–446.
  • Engle, R. (2009). Anticipating correlations: a new paradigm for risk management. Princeton University Press.
  • Hansen, P. R. and Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20, 873–889.
  • Kristensen, D. and Linton, O. (2004). Consistent standard errors for target variance approach to GARCH estimation. Econometric Theory, 20, 990–993.
  • Mezrich, J. and Engle, R. (1996). GARCH for groups. Risk, 9, 36–40.

Prerequisites

To follow and get the most out of this course, participants are expected to have an economics/finance academic and/or professional background, and be familiar with quantitative methods.

Course materials

Every participant will receive a time-limited personal free license of MATLAB several days before the start of the course. You will need to install the MATLAB software on your own computer for use during the practical sessions.

Additional class materials will be provided before and during the sessions. The instructors will also be available throughout the course to discuss your individual research ideas and projects.

Participant Profile

This course is specifically aimed at:

  • Researchers who want to use the latest advances in macroeconometrics

  • Masters and PhD students who want to extend their knowledge in the area of systemic risk in economics and finance  

  • Senior central bankers, specially those who work in the area of financial stability

  • Practitioners at private and public institutions who wish to update their knowledge and acquire the most recent techniques, tools, and methodologies

Fees

Discounts are available for participants with predefined criteria, please check the BSE Executive Education Courses Policies [pdf] for more information. 

 

* Reduced Fee applies for PhD or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.

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