Dynamic Asset Allocation with Ambiguous Return Predictability
We study an investors optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investors aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. Unlike in the Bayesian framework, model uncertainty induces a hedging demand, which may cause the investor to decrease his stock allocations sharply and then increase with his prior probability of IID returns. Adopting suboptimal investment strategies by ignoring model uncertainty can lead to sizable welfare costs.
generalized recursive ambiguity utility ambiguity aversion model uncertainty learning portfolio choice robustness return predictability
Hui Chen Nengjiu Ju Jianjun Miao
MIT Sloan School of Management, 50 Memorial Drive, Cambridge, MA 02142 Department of Finance, the Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Department of Economics, Boston University, 270 Bay State Road, Boston, MA 02215
国际会议
广州
英文
1-42
2009-07-07（万方平台首次上网日期，不代表论文的发表时间）