A Novel Fourier Transform B-spline Method for Option Pricing

We present a new efficient and robust framework for European option pricing under continuous-time asset models from the family of exponential semimartingale processes. We introduce B-spline interpolation theory to derivative pricing to provide an accurate closed-form representation of the option price under an inverse Fourier transform. We compare our method with some state-of-the-art option pricing methods, and demonstrate that it is extremely fast and accurate. This suggests a wide range of applications, including the use of more realistic asset models in high frequency trading. Examples considered in the paper include option pricing under asset models, including stochastic volatility and jumps, computation of the Greeks, and the inverse problem of cross-sectional calibration

Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States

Motivated by the problem of predicting sleep states, we develop a mixed effects model for binary time series with a stochastic component represented by a Gaussian process. The fixed component captures the effects of covariates on the binary-valued response. The Gaussian process captures the residual variations in the binary response that are not explained by covariates and past realizations. We develop a frequentist modeling framework that provides efficient inference and more accurate predictions. Results demonstrate the advantages of improved prediction rates over existing approaches such as logistic regression, generalized additive mixed model, models for ordinal data, gradient boosting, decision tree and random forest. Using our proposed model, we show that previous sleep state and heart rates are significant predictors for future sleep states. Simulation studies also show that our proposed method is promising and robust. To handle computational complexity, we utilize Laplace approximation, golden section search and successive parabolic interpolation. With this paper, we also submit an R-package (HIBITS) that implements the proposed procedure.Comment: Journal of Classification (2018