– Current Unpublished Working Papers
Credit Valuation Adjustments for Interest Rate Portfolios
Work in Progress :: May 2011
Asset Value Correlation in a Financial Portfolio
Work in Progress :: With Calem :: May 2011
Dynamic Factor Value-at-Risk for Large Heteroskedastic Portfolios
Unpublished manuscript :: With Aramonte and Wu :: June 2011
Trading portfolios at large financial institutions are driven by many variables. These variables are often correlated with each other and exhibit time-varying volatilities. We propose a computationally efficient Value-at-Risk (VaR) methodology based on Dynamic Factor Models (DFM) that can be applied to portfolios with time-varying weights, and that, unlike the popular Historical Simulation (HS) and Filtered Historical Simulation (FHS) methodologies, can handle time-varying volatilities and correlations for a large set of financial variables. We test the DFM-VaR on three stock portfolios that cover the 2007-2009 financial crisis, and find that it reduces the number and average size of back-testing breaches relative to HS-VaR and FHS-VaR. DFM-VaR also outperforms HS-VaR when applied risk measurement of individual stocks that are exposed to systematic risk.
Do Analysts Trade Off Bias and Uncertainty? Analyst Earnings Expectations at Different Forecast Horizons
Unpublished manuscript :: With Aiolfi and Timmermann :: September 2008
Financial analysts' earnings forecasts are upwards biased with a bias that gets bigger, the longer the forecast horizon. One explanation of this bias is that it reflects asymmetric costs of positive and negative forecast errors: A positive bias may facilitate better access to companies' private information but also compromises the accuracy of analysts' forecasts. This paper proposes a simple theoretical model that relates the bias and accuracy of analysts' forecasts to the forecast horizon and studies its implications empirically.
Financial Analysts' Incentives and Forecast Biases
Unpublished manuscript :: December 2007
– Published Articles (Refereed Journals and Volumes)
Understanding Analysts' Earnings Expectations: Biases, Nonlinearities, and Predictability
Journal of Financial Econometrics 8(3), Summer 2010, 305-334 :: With Aiolfi and Timmermann
This paper studies the asymmetric behavior of negative and positive values of analysts' earnings revisions and links it to the conservatism principle of accounting. Using a new three-state mixture of lognormal models that accounts for differences in the magnitude and persistence of positive, negative, and zero revisions, we find evidence that revisions to analysts' earnings expectations can be predicted using publicly available information such as lagged interest rates and past revisions. We also find that our forecasts of revisions to analysts' earnings estimates help to predict the actual earnings figure beyond the information contained in analysts' earnings expectations.