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Macroeconometrics

Modeling and Forecasting Macroeconomic Risk

Introduction to modern econometric and statistical methods for modeling and forecasting macroeconomic downside risk.

17.5h (5 days)

€1,399

Face-to-face

English

Program date: June 29-July 3, 2026

Early bird deadline: April 15, 2026

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Macroeconometrics

Modeling and Forecasting Macroeconomic Risk

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Who is this Course for?

Modeling and Forecasting Macroeconomic Risk

Course overview

Downside risk analysis has gained growing prominence in both academic research and policy practice as economies confront episodes of heightened uncertainty and financial fragility.

This course explores modern econometric and statistical methods for modeling and forecasting macroeconomic downside risk.

The course introduces state-of-the-art risk measurement techniques, with an emphasis on tools used in academic research and by central banks, regulators, and financial institutions. Course participants will study models for conditional quantiles, time-varying volatility and correlation, systemic risk indicators, and macro-financial linkages that drive aggregate instability. Importantly, participants will learn the statistical tools to forecast downside risk exposures and evaluate them.

After completing the course, students will be able to use modern econometric tools to analyze macro-financial data and build models that improve risk assessment and policy analysis. The course balances solid methodology with practical application, ensuring that forecasts are clear, interpretable, and useful for real-world macroeconomic risk management.

Faculty

Who is this course for?

This course could be of interest to:

Central bank economists and policy analysts responsible for monitoring vulnerabilities, stress-testing the system, and evaluating downside risks in policy decisions
Quantitative finance professionals looking to enhance risk management strategies, particularly in asset management, trading, or risk mitigation, by accurately forecasting potential losses and tail events
Private sector economists at consulting firms, corporations, and financial institutions who require reliable downside risk assessments to guide strategic and financial decision-making
PhD students and academic researchers aiming to strengthen their toolkit for analyzing downside risk and extreme events in economic and financial forecasting
Data scientists transitioning to economics and finance who want to understand the unique challenges of modeling extreme outcomes and downside risks in economic time series

Learning outcomes

By the end of the course, participants will have:

Learned advanced analytical techniques that turn complex macroeconomic and financial data into actionable insights.
Learned in a program that emphasizes empirical analysis, independent investigation, and critical thinking to tackle today’s key economic questions.

Key topics for Macroeconomic Forecasting with Machine Learning course

Here is a brief outline of what we will cover.

Introduction to macroeconomic downside risk measurement

Downside risk measures
Downside risk forecasting and forecast evaluation

Methods: Quantile regression

Conditional quantile estimation
Check (pinball) loss minimization
Linear programming optimization
Inference

Methods: Time series model for downside risk

Conditional variance modeling
Maximum likelihood estimation (MLE)
Asymmetric and long-memory extensions (EGARCH, GJR, FIGARCH)
Volatility forecast evaluation and backtesting
Dynamic conditional correlation (DCC) estimation for time-varying dependence

Applications: Vulnerable Growth

Quantile regression for US GDP growth at lower quantiles
Macro-financial conditioning variables for tail dependence
International evidence

Applications: Downside Risk in the US Economy

Tail Dependence Across Macroeconomic Series
Backtesting downside risk predictions
Which factors drive downside risk?

List of References

Here is a list of references that may help you prepare for the course.

Articles and Books

Adrian, T. and Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106, 1705–1741.
Adrian, T., Boyarchenko, N., and Giannone, D. (2019). Vulnerable Growth. American Economic Review, 109(4), 1263–1289.
Adams, P., Adrian, T., Boyarchenko, N., and Giannone, D. (2020). Forecasting Macroeconomic Risks. Federal Reserve Bank of New York Staff Report No. 914.
Adrian, T., Giannone, D., Luciani, M., and West, M. (2025). Scenario Synthesis and Macroeconomic Risk. IMF Working Paper.
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, Vol. IV, Chapter 49, 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, 71, 579–625.
Brownlees, C. and Souza, A. (2021). Backtesting Growth-at-Risk. Journal of Financial Econometrics, 19(4), 713–739.
Christoffersen, P. F. (1998). Evaluation of interval forecasts. International Economic Review, 39, 841–862.
Delle Monache, D., De Polis, A., and Petrella, I. (2024). Modeling and Forecasting Macroeconomic Downside Risk. Journal of Business & Economic Statistics, 42(3), 1010–1025.
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.
Forni, M., Gambetti, L., and Sala, L. (2024). The Effects of Monetary Policy on Macroeconomic Risk. European Economic Review, 167.
Gächter, M., Hasler, E., and Huber, F. (2023). A Tale of Two Tails: 130 Years of Growth-at-Risk. Working Paper.
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.

Software / Hardware

Software:

R &R Studio

Hardware:

Personal laptop capable of running statistical software
Minimum 8GB RAM recommended for computational exercises

Note

All necessary code examples and datasets will be provided

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers

2

Study in vibrant Barcelona

3

Learn from world-renowned faculty

Admissions and Requirements

Participants must check they are eligible to take the course before applying.

Program date: June 29-July 3, 2026

Early bird deadline: April 15, 2026

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Requirements for Modeling and Forecasting Macroeconomic Risk

Required knowledge:

Solid foundation in econometrics and regression analysis (equivalent to third year undergraduate degree)
Familiarity with basic time series and forecasting concepts
Understanding of statistical inference fundamentals

Technical skills:

Programming experience in R, Python, or MATLAB
Ability to work with economic datasets and perform statistical analysis

Schedule

Here is your schedule for this edition of BSE Macroeconometrics Summer School Nowcasting and Forecasting course.

Time

29

mon

30

tue

1

wed

2

thu

3

fri

11:30-13:30

Lecture

16:15-17:45

Practical

Credit Transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Fees

Multiple course discounts are available; see more information about available discounts. Fees for other courses listed in other Summer School programs may vary.

Course

Modeling and Forecasting Macroeconomic Risk

High-Dimensional Time Series Models

Introduction to Nowcasting and Forecasting

Introductory Bayesian Macroeconometrics

Bayesian Estimation of RANK and HANK Business Cycle Models

Macroeconomic Forecasting with Machine Learning

Time Series Models for Macroeconomic Analysis I

Time Series Models for Macroeconomic Analysis II

Modality

Face-to-face

Total Hours

17.5

ECTS

1

Regular Fee

1,399€

Reduced Fee*

799€

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

FAQ

Here are some commonly asked questions by participants. Any further queries, please contact our Admissions Team.

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

Is accommodation included in the course fee?

Unfortunately, accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts or request a personalized discount quote by email.

Can I take more than one course?

Yes! you can combine any of the Summer School courses (schedule permitting). See the full course calendar.

Cancelation and Refund Policy

Please consult BSE Summer School policies for more information.

Are there any evening activities during the course?

Yes, a social dinner is held once a week for all participants, it is free to attend.

Contact our Admissions Team

Mix and match your summer courses!

Remember that you can combine this program with courses in any of the other BSE Summer School programs (schedule permitting). Maximise your learning this summer and take advantage of our multiple-course discount.

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