There is a large literature on forecasting inflation using the generalized Phillips curve (i.e. using forecasting models where inflation depends on past inflation, the unemployment rate and other predictors). The present paper extends this literature through the use of econometric methods which...

This paper investigates a non-parametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogenous of degree one with respect to the underlying index...

A new option pricing formula is presented that unifies several results of the existing literature on pricing exotic options under Lèvy processes. To demonstrate the flexibility of the formula a few examples are given which provide new valuation formulas within the Lévy framework

In this paper we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...

Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term...

A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...