臺灣博碩士論文加值系統

We take a similar form of pricing kernel which developed by Christoffersen et al (2013) to extend the multiple volatility components model. By that way, we can obtain a more elaborate model which also explains some puzzles in the market. Apart from that, a surprise result is we don't need to estimate full parameters in model. Instead of that, we estimate the scaling factor which plays an important role when changing of measure. Empirical tests demonstrate the well ability of generalized model when reconcile time series properties of stock returns with the option prices. Furthermore, we also use the in-sample and out-sample for testing the predictability of the generalized model. The result shows the pricing kernel more or less enhancing the predictability than before.

1. Introduction p.1
2. Model p.3
2.1 Multiple Volatility Components Model p.3
2.2 Implications of the Variance-Dependent Pricing Kernel p.6
2.3 Option Valuation p.8
3. Empirical Test p.10
3.1 Data p.10
3.2 Estimation p.12
4. Conclusion p.18
 Appendix p.19
 References p.27

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