Chair of Financial Econometrics

Chair description

Can we predict stock returns? How can we best model the time-varying volatility of asset returns? How do stock returns comove during periods of economic distress? What factors drive the cross-sectional variation in asset returns? These are typical questions in financial economics. Financial econometrics develops the models and methodological tools needed to answer them.

While data-driven since its inception, financial econometrics is now increasingly shaped by the challenges and opportunities of big data. The growing availability of large and previously inaccessible datasets – such as high-frequency quotes and trade data, order book data, and financial text – has opened the door to a wide range of novel research areas. At the same time, machine learning methods offer new opportunities for modeling, prediction, and pattern recognition in financial data.

Research 

• Asset and Derivatives Pricing
• Risk Management
• Volatility modeling
• Textual analysis
• Consumer Transaction Data 

Teaching

• An Introduction to Time Series Econometrics (Bachelor)
• Time Series Econometrics (Master)
• Practice and Practicialities of Forecasting
• Derivatives (Master) 
• Asset Pricing (Phd)

Projects

Launched in 2020, Monitoring Consumption Switzerland is a project that uses payment data to shed light on consumer spending in Switzerland. In 2025, we introduced the Consumer Spending Index  for Switzerland.

Matthias Fengler has been granted a number of projects supported by the Swiss National Foundation:

• Structural Models of Volatility
• Analysis and models of cross asset dependency structures in high-frequency data
• Fresh evidence on the effects of monetary and fiscal policy based on transaction data