Events

What happens when firms merge, and how do economists determine whether competition is harmed? This lecture offers an accessible introduction to merger analysis, combining theory with a real-world case study. It explores how economists define markets, measure competitive pressure, and assess the likely impact of mergers on consumers. The session also highlights how data-driven tools and empirical methods are increasingly central to competition policy. 

We consider the problem of identifying the optimal subset of past observations to be used for estimating the covariance matrix of asset returns when computing the global minimum variance portfolio. While the usual approach simply employs either all the available past returns or a rolling window of fixed size, we argue that such solutions are inefficient, and we instead propose to dynamically adjust the estimation window. To this end, we employ the PELT changepoints detection algorithm to identify outdated observations. We then set up a cross-validation procedure to determine the minimum number of upto-date observations that individual assets must have to be considered when estimating the covariance matrix. Assets that do not reach the selected threshold are excluded from the optimization and assigned equal weights. In an empirical application, our solution achieves a higher Sharpe ratio compared to both an expanding and rolling window estimation of the global minimum variance portfolio, and it also compares well with other benchmarks.

When financial markets become turbulent, how do investors price the fear of uncertainty itself? Does that fear spread across different corners of the global economy? This research investigates the Variance Risk Premium (VRP), defined as the gap between what investors expect volatility to be and what it actually turns out to be, across five distinct asset classes: equities, fixed income, foreign exchange, cryptocurrencies, and commodities. While VRP has been studied extensively in stock markets, its behaviour in bond markets, currency markets, commodity markets, and the rapidly growing cryptocurrency space remains poorly understood — particularly in a unified, comparative framework.
Using intraday and high-frequency data spanning 2018–2025 — a period capturing COVID-19, the 2022 rate hiking cycle, and multiple crypto boom-bust episodes — the research employs advanced econometric models including GARCH, HAR, VAR, VECM, and Dynamic Conditional Correlation to examine how VRP behaves, what drives it, and whether volatility risk in one market spills over into others.