Internal Research Seminars

In 2025, a survey of 8,000 students and employees at ten Czech universities analysed commuting behaviour using a logit model of mode choice. Public transport dominated (47%), followed by car (32%) and walking or cycling (21%), with higher car use among employees and higher-income respondents. Distance, age (non-linear effect), and children significantly increased the likelihood of car use, particularly beyond 10 km. Differences across universities reflected urban size and topography, with higher public transport shares in large agglomerations. Key barriers to sustainable mobility were habits, distance, and public transport quality, while faster connections, lower costs, improved cycling infrastructure, and limited parking were identified as main drivers of modal shift.

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 To this end, we employ the PELT changepoints detection algorithm to identify outdated observations.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.