通过贝叶斯模型平均的半参数GARCH
Semiparametric GARCH via Bayesian model averaging
摘要：
由于金融市场的动态结构会发生剧烈变化，因此能够提供一致准确的波动估计的模型不得对价格如何随时间变化做出强有力的假设。大多数波动率模型采用特定的参数函数形式，将观察到的价格变化与波动率预测（新闻影响函数）联系起来。我们提出了一类新的函数系数半参数波动率模型，其中新闻影响函数允许是任何光滑函数，并研究其估计波动率的能力相比，众所周知的参数建议，在模拟研究和实证研究与真实的金融数据。我们估计的新闻影响函数使用贝叶斯模型平均的方法，通过精心开发的马尔可夫链蒙特卡罗（MCMC）采样算法实现。使用模拟，我们表明，我们灵活的半参数模型是能够学习的形状的新闻影响函数从观察到的数据。当应用到实际的金融时间序列，我们的新模型表明，新闻影响函数的形状显着不同的不同的资产类型，但相同类型的资产是相似的。
英文摘要：
As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assumptions on how prices change over time. Most volatility models impose a particular parametric functional form that relates an observed price change to a volatility forecast (news impact function). We propose a new class of functional coefficient semiparametric volatility models where the news impact function is allowed to be any smooth function, and study its ability to estimate volatilities compared to the well known parametric proposals, in both a simulation study and an empirical study with real financial data. We estimate the news impact function using a Bayesian model averaging approach, implemented via a carefully developed Markov chain Monte Carlo (MCMC) sampling algorithm. Using simulations we show that our flexible semiparametric model is able to learn the shape of the news impact function from the observed data. When applied to real financial time series, our new model suggests that the news impact functions are significantly different in shapes for different asset types, but are similar for the assets of the same type.
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