The relationship between risk and return is one of the most studied topics in finance. The majority of the literature is based on a linear, parametric relationship between expected returns and conditional volatility. This paper models the contemporaneous relationship between market excess...

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and...

Many numerical methods have been developed in an attempt to find solutions to nonlinear rational expectations models. Because these algorithms are numerical in nature, they rely heavily on computing power and take sizeable cycles to solve. In this paper we present a numerical tool known as...

In this paper, I provide a plausible explanation as to why past studies have been unable to find support for the long-run Fisher effect. My argument is that exogenous shocks to the inflation rates in industrialized economies have not produced the permanent change to inflation necessary for...

By design, a wavelet's strength rests in its ability simultaneously to localize a process in time-scale space. The wavelet's ability to localize a time series in time-scale space directly leads to the computational efficiency of the wavelet representation of an N x N matrix operator by allowing...

Empirical support for the long-run Fisher effect, a hypothesis that a permanent change in inflation leads to an equal change in the nominal interest rate, has been hard to come by. This paper provides a plausible explanation of why past studies have been unable to find support for the long-run...

In this paper, a semiparametric, Bayesian estimator of the long-memory stochastic volatility model's fractional order of integration is presented. This new estimator relies on a highly efficient, Markov chain Monte Carlo (MCMC) sampler of the model's posterior distribution. The MCMC algorithm is...

By design a wavelet's strength rests in its ability to simultaneously localize a process in time-scale space. The wavelet's ability to localize a time series in time-scale space directly leads to the computational efficiency of the wavelet representation of a N X N matrix operator by allowing...

In this paper we apply wavelet analysis to the class of fractionally integrated processes to show that this class is a member of the $1/f$ family of processes as defined by Wornell (1993) and to produce an alternative method of estimating the fractional differencing parameter. Currently the...