Finance Research Graz (FiRe)
FiRe Graz is a loosely organized research platform, affiliated with the University of Graz. Founded and operated by the Institute of Banking and Finance and the Institute of Finance, the platform is open to all finance researchers with a connection to the University of Graz and is intended to reach out across organisation boundaries. Its main research areas are empirical and experimental research in finance.
Successful online workshop of the Austrian Working Group on Banking and Finance01.12.2020
The Institute of Banking and Finance and the Institute of Finance organized the 35th workshop of the Austrian Working Group on Banking and Finance (AWG) on November 26 and 27, 2020. Initially scheduled to be held in Graz, the COVID-19 pandemic necessitated an online format. Despite the virtual meeting, the workshop was a great success. A total of 22 scientists from 12 institutions and 4 different countries presented and discussed their research on topics like portfolio optimization and performance, credit risk, central bank policy and market quality, sustainability and behavioral insights. With about 40 participants, the workshop also attracted an audience that was at least as big as many previous workshops that had been held offline.
The organizers wish to thank all participants and invite them to next year's workshop, which is scheduled to be held, in Graz, on November 26 and 27, 2021.
New paper on the effect of option listings on prices of discount certificates 16.11.2020
The paper "Listing of classical options and the pricing of discount certificates” by FiRe member Andrea Schertler has been accepted for publication in the Journal of Banking and Finance.
In this paper, Andrea investigates whether new listings of EUREX options affect the prices of discount certificates that are replicated with precisely such a newly listed option. An event study provides a significantly negative average abnormal margin change on the day on which the EUREX option of the replication portfolio is listed. The papers models the abnormal margin changes as a function of hedging cost, unhedgeable risk, and price pressure. Higher hedging costs, higher opportunity costs from unhedgeable risk, and lower intra-EUWAX competition lead to significantly lower abnormal margin changes. The paper interprets the effect of intra-EUWAX competition as a price pressure effect. Economically, rebalancing and opportunity costs from unhedgeable risk are the most important drivers of abnormal margin changes when EUREX options are listed.
New paper on portfolio optimization based on qualitative information 13.11.2020
Generating accurate estimates of expected asset returns is a mammoth task in portfolio optimization and especially prone to estimation errors, which negatively influence portfolio allocation decisions. While the classical mean-variance approach solves the portfolio optimization as a deterministic problem, the model by Black and Litterman (BL; Black, Litterman, 1991, Black, Litterman, 1992) accounts for uncertainties in the input data and allows the inclusion of statements regarding absolute returns. In this paper platform members Roland Mestel and Ulrich Pferschy, co-authored with Eranda Cela and Stephan Hafner, extend the traditional BL model by allowing qualitative views, in particular ordering information among expected asset returns.
The authors assume investor views to be stochastic and present a new and competitive approach for translating expected asset return rankings into quantitative estimates of expected asset returns for portfolio optimization. The new estimator for the posterior expectation of returns is computed by applying an importance sampling technique. Using data from the EUROSTOXX 50 and the S&P 100, respectively, the authors empirically evaluate the forecast quality of their new approach in comparison to existing, but methodologically different, approaches from the literature and assess the performance of their model in a mean-variance portfolio context. They find that the new approach mostly achieves the highest predictive power, irrespective of the dataset, the assumed level of accuracy of the ordering information, and mostly irrespective of the investor’s confidence in the qualitative view, even though the improvement resulting from their approach is moderate. They observe a similar behaviour in the context of portfolio performance analysis.
Çela, E., Hafner, S., Mestel, R., Pferschy, U, (2021). Mean-variance portfolio optimization based on ordinal information, Journal of Banking and Finance, Vol. 122, DOI: https://doi.org/10.1016/j.jbankfin.2020.105989.