基于贝叶斯模型平均技术的资产波动率预测方法研究及其应用

项目名称： 基于贝叶斯模型平均技术的资产波动率预测方法研究及其应用

项目编号： No.71271221

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 李勇

作者单位： 中国人民大学

项目金额： 56万元

中文摘要： 在实证金融研究中, 金融资产收益波动性预测研究是金融计量研究领域的一个热点研究问题。近三十年来，研究者提出了大量的波动模型如ARCH,GARCH，SVM,用以拟合金融资产收益波动率。现在，这些波动模型已经被广泛应用于资产管理、风险管理、期权定价等金融领域中。然而，伴随着模型的发展，一个重要的后果是引入了模型不确定性，从而导致了模型风险，造成了许多重大的损失。本项目基于贝叶斯模型平均技术，研究如何减少或者分散金融资产收益波动率建模的模型不确定性风险。本项目的开展，在期货对冲、风险管理、基金投资等领域都会有很大的应用价值。特别是股指期货推出后，我国金融市场由单边走向双边，这项研究在宏观上国家管理层监管金融市场，在微观上投资者制定投资策略均有重要的参考价值。

中文关键词： 潜在变量模型；假设检验；模型不确定性；期权定价；随机波动模型

英文摘要： In empirical finance, forecasting asset volatility is one of the important topics for researchers. In recent thirty years, many volatility models such as ARCH, GARCHY, SV models have been proposed to fit the asset volatility. Now, these models have widely used in asset management, risk management, option pricing, etc. However, with the development of the volatility models, a serious consequence, model uncertainty which leads to model risk, is aroused in practice. It is well-documented that this so-called model risk has caused some huge loss in finance industry. In this project, based on the Bayesian model averaging techniques, we devote to how to reduce the model risk on forecasting the asset volatility. This project has some important applications in the fields such as, future hedging, risk management, fund investment,etc. Especially, when the future trades are implemented in China so that the Chinese financial market has become a two-side market from an one-side market, this project also has some referenced implications on monitoring the financial market for the government from macroeconomical viewpoint and making investment strategies for microeconomical investors.

英文关键词： latent variable models；hypothesis testing；model uncertainty；option pricing；stochastic volatity models