A team of biophysicists from Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) presents a mathematically concise method for comparing different pricing models in their latest publication in Nature Communications. This enables researchers to predict more accurately how parameters such as the volatility of stock prices change over time.*
The ups and downs of stock prices are the result of a complex interplay between traditional investors, day-traders and high-frequency hedge funds. The seemingly erratic short-term price fluctuations can be characterized by a diffusion constant – called volatility. However, volatility itself changes significantly over longer time scales. For example, unexpected Twitter announcements may trigger abrupt volatility spikes, while economic policy changes may induce gradual variations of volatility. Financial analysts notoriously struggle to estimate how volatility changes over time and often base their predictions on unsubstantiated assumptions.
Instead of evaluating the uncertainty of different model predictions analytically, Christoph Mark and colleagues from the Biophysics group at FAU developed a numerical implementation of the principle of ‘Occam’s razor’, which favors those models that describe the data with the least number of assumptions.
Read more at University of Erlangen-Nürnberg