"Quantitative Finance Made Accessible" Webinar - Class 7: Held on November 18, 2023
Channel: Alejandro Rodriguez Dominguez
Date: 2023-11-19

I presented a framework for portfolio optimization that many of you know, but I included new work in which sensitivities of portfolio constituents with respect to optimal portfolio drivers are modelled by Vasicek and Hull-White model, calibrated to sensitivities' data obtained by automatic differentiation with neural nets. Once the SDEs are calibrated, sensitivities' paths are simulated into the future in which we want to position our portfolio. We then apply the same methodology as in our last paper (Rodríguez Domínguez 2023) for each future timestamp. We can embed portfolio constituents in a sensitivity space, one different for each future timestamp, with the simulated sensitivities from the paths at each timestamp as coordinates. Compute a distante matrix for each time stamp and combine all of these distance matrices in a compounded one for the simulated future interval, to which we can apply Hierarchical Clustering (like in our last paper but to a compound matrix which has path dependent future sensitivity information) to solve the convex optimization problem using numerical methods from the literature that can use the hiararchical structure of the sensitivities and their volatility clusters. The perfomance is improved with respect to our last paper, adding more real future diversification with trajectory information about diversification in the PO process.

Thank you very much Florian for making Quant Finance more accesible to everyone.

link to the previous paper in: https://lnkd.in/dzTWXudv