We propose regression-based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. We allow for state variables that are cross-sectional pricing factors, forecasting variables for the price of risk, and factors that are both. The...

Financial time series often exhibit properties that depart from the usual assumptions of serial independence and normality. These include volatility clustering, heavy-tailedness and serial dependence. A voluminous literature on different approaches for modeling these empirical regularities has...

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

Asset pricing models can reinforce asset allocation decisions and promote risk management gains. We compare the out-of-sample performance of mean-variance strategies when mean and covariance are sample estimators of (1) unfiltered excess returns; and (2) filtered excess returns through an asset...

This paper aims at exploring the causal relationship between net foreign institutional investment flows to the Indian equity market with its possible covariates based on daily data for the period September 2008 to July 2013. The data has been analyzed in a Vector Autoregressive framework for...

Since the seminal paper of Vasicek and Fong (1982), the term structures of interest rates have been fitted assuming that yields are cross-sectionally homoskedastic. We show that this assumption does not hold when there are differences in liquidity, even for bonds of the same issuer. Lower...

In this paper, we derive explicit expressions for certain joint moments of stock prices and option prices within a generic affine stochastic volatility model. Evaluation of each moment requires weighted inverse Fourier transformation of a function that is determined by the risk-neutral and...

At its core, portfolio and risk management is about gathering and processing market-related data in order to make effective investment decisions. To this end, risk and return statistics are estimated from relevant financial data and used as inputs within the investment process. It is this...

The majority of nancial data exhibit asymmetry and heavy tails, which makes forecasting the entire density critically important. Recently, a forecast combination methodology has been developed to combine predictive densities. We show that combining individual predictive densities that are skewed...

We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental factor models (FFMs). FFMs are the typical benchmark in the asset management industry and depart from the usual statistical factor models and the factor models with observed...