scholarly journals Insurance Cycles, Spanning and Regulation

2019 ◽  
Vol 5 (4) ◽  
pp. p465
Author(s):  
Arthur M.B. Hogan ◽  
David Nickerson

This paper offers a novel explanation of the financial underwriting cycle in the property-liability insurance industry. By doing so it resolves that significant anomaly in asset pricing theory posed by cycles in the efficient pricing of insurance coverage. In contrast to the reliance on a variety of institutional or capital market failures underlying all previous explanations of this cycle, we directly augment the complete-markets environment of traditional asset-pricing models through the presence of a single source of risk that cannot be fully hedged through existing financial markets. We realistically interpret this source of risk as unforecastable noise in the implementation of insurance regulations. Cycles in the value of underwriting insurance coverage can arise in this simple variant of a standard complete-markets pricing model owing to the effect of such regulatory risk. We offer a sufficient condition for a stable cycle to endogenously exist in market equilibrium and illustrate this condition in the context of a representative insurance firm and a regulator pursuing a countercyclical policy with noisy implementation. Interestingly, while insurance pricing is efficient in the absence of the regulator, cyclic pricing and underwriting profitability can be induced by a countercyclical regulator policy designed to stabilize the very cycle it creates.

Author(s):  
Gurdip S. Bakshi ◽  
Dilip B. Madan ◽  
George Panayotov

2005 ◽  
Vol 29 (10) ◽  
pp. 1737-1764 ◽  
Author(s):  
Christophette Blanchet-Scalliet ◽  
Nicole El Karoui ◽  
Lionel Martellini

Author(s):  
Arjun K. Gupta ◽  
Wei-Bin Zeng ◽  
Yanhong Wu

2018 ◽  
Vol 18 (4) ◽  
pp. 656-714 ◽  
Author(s):  
Bertille Antoine ◽  
Kevin Proulx ◽  
Eric Renault

Abstract This article is motivated by the need to bridge some gap between modern asset pricing theory and recent developments in econometric methodology. While asset pricing theory enhances the use of conditional pricing models, econometric inference of conditional models can be challenging due to misspecification or weak identification. To tackle the case of misspecification, we utilize the conditional Hansen and Jagannathan (1997) (HJ) distance as studied by Gagliardini and Ronchetti (2016), but we set the focus on interpretation and estimation of the pseudo-true value defined as the argument of the minimum of this distance. While efficient Generalized Method of Moments (GMM) has no meaning for estimation of a pseudo-true value, the HJ-distance not only delivers a meaningful loss function, but also features an additional advantage for the interpretation and estimation of managed portfolios whose exact pricing characterizes the pseudo-true pricing kernel (stochastic discount factor (SDF)). For conditionally affine pricing kernels, we can display some managed portfolios which are well-defined independently of the pseudo-true value of the parameters, although their exact pricing is achieved by the pseudo-true SDF. For the general case of nonlinear SDFs, we propose a smooth minimum distance (SMD) estimator (Lavergne and Patilea, 2013) that avoids a focus on specific directions as in the case of managed portfolios. Albeit based on kernel smoothing, the SMD approach avoids instabilities and the resulting need of trimming strategies displayed by classical local GMM estimators when the density function of the conditioning variables may take arbitrarily small values. In addition, the fact that SMD may allow fixed bandwidth asymptotics is helpful regarding the curse of dimensionality. In contrast with the true unknown value for a well-specified model, the estimated pseudo-true value, albeit defined in a time-invariant (unconditional) way, may actually depend on the choice of the state variables that define fundamental factors and their scaling weights. Therefore, we may not want to be overly parsimonious about the set of explanatory variables. Finally, following Antoine and Lavergne (2014), we show how SMD can be further robustified to deal with weaker identification contexts. Since SMD can be seen as a local extension of the method of jackknife GMM (Newey and Windmeijer, 2009), we characterize the Gaussian asymptotic distribution of the estimator of the pseudo-true value using classical U-statistic theorems.


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