This paper develops a new estimation procedure for characteristic-based factor models of stock returns. It describes a factor model in which the factor betas are smooth nonlinear functions of observed security characteristics. It develops an estimation procedure that combines nonparametric...

This paper develops a new estimation procedure for characteristic-based factor models of security returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time-varying weights, and a set of univariate non-parametric functions...

This study empirically examines the Fama-French three-factor model of stock returns for India. We find evidence for pervasive market, size and book-to-market factors in India stock returns. We find that cross-section means returns are explained by exposures to these three factors, and not by the...

This paper estimates a structural times series model of return volatility. We argue that the structural time series approach to GARCH modelling first suggested by Engle and Lee, has the potential to improve the empirical reliability of GARCH models, and greatly enhance their interpretability. In...

International Asset Management (‘IAM’) is the proud sponsor of the IAM Hedge Fund Research Programme of the Financial Markets Group. Within this programme the LSE team undertakes independent research into aspects of the hedge fund industry. It is hoped that the results of this research will...

We investigate a new separable nonparametric model for time series, which includes many ARCH models and AR models already discussed in the literature. We also propose a new estimation procedure called LIVE, or local instrumental variable estimation, that is based on a localization of the...

We examine the relationship between the risk premium on the S&P500 index total return and its conditional variance. We propose a new semiparametric model in which the conditional variance process is parametric, while the conditional mean is an arbitrary function of the conditional variance. For...

We propose a new estimator of multivariate ex-post volatility that is robust to microstructure noise and asynchronous data timing. The method is based on Fourier domain techniques, which have been widely used in discrete time series analysis. The advantage of this method is that it does not...

We introduce a general and flexible framework for hedge fund performance evaluation and asset allocation: stochastic dominance (SD) theory. Our approach utilizes statistical tests for stochastic dominance to compare the returns of hedge funds. We form hedge fund portfolios by using SD criteria...

We investigate a class of estimators for Linear Regression models where the dependent variable is subject to bid-ask censoring.  Our estimation method is based on a definition of error that is zero when the predictor lies between the actual bid price and ask price, and linear outside this...