Non-linear Expectation Theory and Risk Measurement Based on Model Ambiguity
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| Title | Non-linear Expectation Theory and Risk Measurement Based on Model Ambiguity
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| Author | Gong Xiaolin (Caelyn Gong), Yang Shuzhen, Hu Jinyan and Zhang Ning |
| Organization | Shandong University |
| Email | gcaelyn@gmail.com； |
| Key Words | Uncertainty; Non-linear Expectation Theory; Model Ambiguity; Risk Measurement |
| Abstract | With the approach of multiple-change-point detection for auto-regressive conditional heteroskedastic processes, the paper first demonstrates that economic and financial data has uncertain characteristics of probability and statistics, and thus illustrates the limited applicability of current Probability Theory in a realistic, dynamic economic environment. Then, we analyze how Non-linear Expectation Theory incorporates all kinds of uncertainty, such as volatility uncertainty and mean uncertainty, in risk modelling and measure risk with infinite amount of possible uncertain distributions. Meanwhile the analysis shows that this recent progress in stochastic analysis and calculus might bring fundamental change to risk management theory and practice. And empirical evidence of the effectiveness of risk measurement based on model ambiguity is provided. Thus, the paper is to contribute to the cutting edge research on uncertainty analysis and risk management and to provide important technical support for managing and maintaining financial stability. |
| Serial Number | WP944 |
| Time | 2015-11-03 |
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