We derive analytic series representations for European option prices in polynomial stochastic volatility models. This includes the Jacobi, Heston, Stein-Stein, and Hull-White models, for which we provide numerical case studies. We find that our polynomial option price series expansion performs...

We study why widely used low-frequency liquidity cost estimators based on high, low, and close prices perform well in some markets and poorly in others, often yield negative or indeterminate estimates, and how to quantify estimation bias empirically. Using the high-low spread estimator as our...

We introduce a data driven and model free approach for computing conditional expectations. The new method is based on classical techniques combined with machine learning methods. In particular, we consider kernel density estimation based on simulated risk factors combined with a control variate....

We explore simple finite sample adjustments to simulated spot FX rates, zero bonds, forward IBORs and the numeraire to ensure the martingale asset pricing property of linear IR and FX products holds exactly with a finite number of Monte Carlo simulations. The impact on CVA, DVA, and CVA-DVA...

Indirect Inference (I‐I) estimation of structural parameters θ requires matching observed and simulated statistics, which are most often generated using an auxiliary model that depends on instrumental parameters β. The estimators of the instrumental parameters will encapsulate the...

The valuation of options and many other derivative instruments requires an estimation of exante or forward looking volatility. This paper adopts a Bayesian approach to estimate stock price volatility. We find evidence that overall Bayesian volatility estimates more closely approximate the...