Portfolio

We propose option-implied measures of conditional dispersion and asymmetry based on quantiles and expectiles inferred from weekly options. All quantities are by construction forward looking and non-parametrically estimated with a novel arbitrage-free natural smoothing spline technique that produces quick and empricially accurate volatility smiles. We find that the proposed option implied indicators exhibit short, medium and long-term predictive ability for the U.S. equity risk premium and market volatility, both in- and out-of-sample, and outperform equal indicators inferred from historical returns.

We extend Long Short Term Memory (LSTM) networks with parametric volatility model prediction heads that enable arbitrage-free forecasting and interpolation of the entire implied volatility surface dynamics within a single model. The approach is tested on out-of-sample daily SPX options data and shows strong forecasting power on liquid expiries.

We combine Temporal Difference learning (TD-λ) with the backpropagation (BP) algorithm to achieve sample efficient estimation of conditional expectations for path dependent events (across time & space). We benchmark our method and reach comparable results to Monte Carlo simulation with only two thousandth of the number of trajectories on a range of well known stochastic processes.

I discuss (in Swedish) use-cases of AI in the healthcare industry.

I present our paper Smiling in Action about dynamic implied volatility surface modeling using LSTMs.

I discuss my early career beginnings, and the exciting advancements happening in machine learning.