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Characterizing the Conditional Pricing Kernel: A New Approach
I propose a novel method to reliably estimate the conditional pricing kernel by incorporating conditioning variables. The VIX and the term spread are most informative variables for identifying state prices. The conditional kernel estimate exhibits significant time variation: the more favorable market expectations, the higher state prices in negative return states. During bad times, the equity premium implied by the conditional kernel is fully attributable to compensation for left-tail scenarios, in contrast to findings from the unconditional kernel. Lastly, the conditional kernel estimate yields superior out-of-sample option pricing performance compared to the unconditional kernel estimate.
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