英文标题：
《Ross Recovery with Recurrent and Transient Processes》
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作者：
Hyungbin Park
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最新提交年份：
2015
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英文摘要：
Recently, Ross showed that it is possible to recover an objective measure from a risk-neutral measure. His model assumes that there is a finite-state Markov process X that drives the economy in discrete time. Many authors extended his model to a continuous-time setting with a Markov diffusion process X with state space R. Unfortunately, the continuous-time model fails to recover an objective measure from a risk-neutral measure. We determine under which information recovery is possible in the continuous-time model. It was proven that if X is recurrent under the objective measure, then recovery is possible. In this article, when X is transient under the objective measure, we investigate what information is sufficient to recover.
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中文摘要：
最近，罗斯证明了从风险中性度量中恢复客观度量是可能的。他的模型假设存在一个有限状态马尔可夫过程X，它在离散时间内驱动经济。许多作者将其模型推广到具有状态空间R的马尔可夫扩散过程X的连续时间环境。不幸的是，连续时间模型无法从风险中性度量中恢复客观度量。我们确定在连续时间模型中，在何种情况下信息恢复是可能的。事实证明，如果X在客观指标下反复出现，那么恢复是可能的。在本文中，当X在客观度量下是瞬态的，我们将研究哪些信息足以恢复。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Mathematical Finance 数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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