Annals of Financial Economics

In this paper we present two dynamic models of background risk. We first present a stochastic factor model with an additive background risk. Then, we present a dynamic model of simultaneous (correlated) multiplicative background risk and additive background risk. In so doing, we use a general utility function.

The purpose of this paper is to understand how the product market competition and the arbitrage risk affects managers’ decisions on capital structure. To find managers’ risk preference, we introduce the interaction between market competition and the arbitrage risk of the firm. Sampling from Taiwanese listed companies from 1986 to 2011, we identify both the market competition and the arbitrage risk affects managers’ debt decisions. In addition, we find that most managers in monopolistic firms increase debt to decrease the agency costs. However, some hold risk-averse motivation to enjoy their “quiet life”. These “quiet life” managers exist in those companies that are in non-competitive markets and that exists large idiosyncratic risk.

The authors present a rational learner agent, which considers the information coming from a behavioral counterpart during the allocation process. The learner agent adopts a herding behavior by conditioning her choice on the selection of the portfolio’s constituents. They use the concept of performance measure to define agents’ preferences: the higher the measure, the higher the expected utility of a given asset. The rational learner agent updates her information in a Bayesian manner similarly to the Black–Litterman model. Finally, the authors provide an empirical application including all the assets present in the NASDAQ and NYSE stock exchange from September 1977 to December 2014.

In this paper, we propose a strategy to extract the information on the market participants’ expectation of the future short rate from the cross-sectional zero coupon bond prices. In line with the current market practice of building different yield curves for different tenors, we construct multiple one-factor short rate processes to pin down the salient features of the yield curve at different tenors. We represent this information in the form of the Cox–Ingersoll–Ross model implied parameters, and show that this information can be used to forecast the future short rate. This approach of representing the information on the market participants’ consensus in the form of implied model parameters and using these implied parameters for forecasting purposes resembles the approach of representing the market expectation of the underlying asset volatility reflected by stock option prices in the form of implied volatility, and using it to forecast the realized volatility. We illustrate the implementation of this method using historical US STRIPS prices and effective Federal Funds rate.

This study proposes a new way of solving standard dynamic problem based on Simulated Method of Moments (SMM) approach. It uses a newly introduced model of stock returns involving latent state variables and the regime-switching fundamentals and estimates three key preference parameters namely the Coefficient of Relative Risk Aversion, the Elasticity of Intertemporal Substitution and the subjective discount factor by suitably applying SMM and without directly using noisy consumption data. The estimates we found here seem to be relatively better than prevalent studies and very close to the true values of the parameters.