Do Government Guarantees Inhibit Risk Management? Evidence from Fannie Mae and Freddie Mac (Job Market Paper) Fannie Mae and Freddie Mac’s implicit government guarantee is widely argued to cause irresponsible risk taking. Despite moral-hazard concerns, this paper presents evidence that Fannie Mae and Freddie Mac (the GSEs) more effectively managed home price risks during the 2000-2006 housing boom than private insurers. Mortgage origination data reveal that the GSEs were selecting loans with increasingly higher percentage of down payments, or lower loan to value ratios (LTV), in boom areas than in other areas. Furthermore, the decline of LTVs in boom areas stems entirely from the segment insured by the GSEs only, and none of the decline stems from the segment co-insured by private mortgage insurers. Private mortgage insurers also did not lower their exposure to home price risks along other dimensions, including the percentage of high LTV GSE loans they insured. To quantify how the GSEs’ portfolios would have performed under alternative home price scenarios, I build an insurance valuation model based on competing-risk hazard regressions, calibrated Hull and White term-structure model, and forecasting prepayment and default speeds. I find that the GSEs’ risk management would have been sufficient for the historically average 32% mean reversion but insufficient for the realized 95% mean reversion between 2006 and 2011. My results highlight that post-crisis reform of the mortgage insurance industry should carefully consider additional factors besides moral hazard, such as mortgage insurers’ future home price assumptions.
Estimation of Quadratic Forms for High Dimensional Time Series: Correcting Finite Sample Bias in the Mean Variance Frontier with Alexander Aue and Debashis Paul Presented at: NBER-NSF Time Series Conference 2016, Columbia University; UC Berkeley Risk Management Seminar; University of Waterloo, Department of Statistics and Actuarial Science In finite samples, risk is known to be underestimated in the mean-variance frontier. This paper proposes a novel algorithm correcting this finite sample bias when asset returns follow a high dimensional time series. Assuming a linear process formulation studied in Liu, Aue, and Paul (2015), an algorithm is proposed to estimate the spectral distributions of the coefficient matrices of the linear process by making use of the asymptotic behavior of the empirical spectral distributions of symmetrized autocovariance matrices. This leads to the formulation of a strategy for the estimation of the mean-variance frontier, utilizing the estimates of the coefficient matrix spectra. The proposed method is extended to a setting in which the returns are assumed to have a factor model structure with observed factors, while the unknown idiosyncratic terms are assumed to belong to the aforementioned class of linear processes. The performance of the proposed methods is examined through extensive simulation studies.
JOURNAL PUBLICATIONS On Marcenko-Pastur Law for Linear Time Series, Annals of Statistics, vol. 43, no. 2, pp. 675-712, 2015. with Debashis Paul and Alexander Aue Presented at: NBER-NSF Time Series Conference 2013, Federal Reserve Board; USC Marshall School of Business, Data Sciences and Operations