In this paper, we show how we can deploy machine learning techniques in the context of traditional quant problems. We illustrate that for many classical problems, we can arrive to speed-ups of several orders of magnitude by deploying machine learning techniques based on Gaussian process...

Complex insurance risks typically have multiple exposures. Options on multiple underliers with a short maturity are employed to hedge this exposure. Hedges are illustrated for GMWBVA accounts invested in the nine sector ETF's of the US economy. The underliers are simulated risk neutrally by...

The paper provides a new hedging methodology permitting systematic hedging choices with wide applications. Dynamic concave bid price, and convex ask price functionals from the recent literature are employed to construct new hedging strategies termed dynamic conic hedging. The primary focus of...

Exponentials of squared returns in Gaussian densities, with their consequently thin tails, are replaced by the absolute return to form Laplacian and exponentially tilted Laplacian densities at unit time. Scaling provides densities at other maturities. Stochastic processes with these marginals...

For data on market prices on 246 cliquets we consider the task of pricing these exotic options using a relatively simple path space subsequently stressed to market implied and then predicted stress levels. An additive process transitioning from a Sato process to a Levy process is formulated and...

The problem studied is the pricing of options on the CBOE Skew index. The option pricing theory developed seeks to hedge the risk using positions in the market for options on a related asset and the option is then priced at the cost of this hedge. The theory is applied to pricing VIX options...