State Prices of Conditional Quantiles: New Evidence on Time Variation in the Pricing Kernel. Metaxoglou, K. & Smith, A. *Journal of Applied Econometrics*, 32(1):192–217, 2017.

Paper abstract bibtex

Paper abstract bibtex

We develop a set of statistics to represent the option‐implied stochastic discount factor and we apply them to S&P 500 returns between 1990 and 2012. Our statistics, which we call state prices of conditional quantiles (SPOCQ), estimate the market's willingness to pay for insurance against outcomes in various quantiles of the return distribution. By estimating state prices at conditional quantiles, we separate variation in the shape of the pricing kernel from variation in the probability of a particular event. Thus, without imposing strong assumptions about the distribution of returns, we obtain a novel view of pricing‐kernel dynamics. We document six features of SPOCQ for the S&P 500. Most notably, and in contrast to recent studies, we find that the price of downside risk decreases when volatility increases. Under a standard asset pricing model, this result implies that most changes in volatility stem from fluctuations in idiosyncratic risk. Consistent with this interpretation, no known systematic risk factors such as consumer sentiment, liquidity or macroeconomic risk can account for the negative relationship between the price of downside risk and volatility.

@article{metaxoglou2017state, title={State Prices of Conditional Quantiles: New Evidence on Time Variation in the Pricing Kernel}, author={Metaxoglou, Konstantinos and Smith, Aaron}, journal={Journal of Applied Econometrics}, volume={32}, number={1}, pages={192--217}, year={2017}, url={https://files.asmith.ucdavis.edu/2017_JAppEmet_MS_SPOCQ.pdf}, abstract={We develop a set of statistics to represent the option‐implied stochastic discount factor and we apply them to S\&P 500 returns between 1990 and 2012. Our statistics, which we call state prices of conditional quantiles (SPOCQ), estimate the market's willingness to pay for insurance against outcomes in various quantiles of the return distribution. By estimating state prices at conditional quantiles, we separate variation in the shape of the pricing kernel from variation in the probability of a particular event. Thus, without imposing strong assumptions about the distribution of returns, we obtain a novel view of pricing‐kernel dynamics. We document six features of SPOCQ for the S\&P 500. Most notably, and in contrast to recent studies, we find that the price of downside risk decreases when volatility increases. Under a standard asset pricing model, this result implies that most changes in volatility stem from fluctuations in idiosyncratic risk. Consistent with this interpretation, no known systematic risk factors such as consumer sentiment, liquidity or macroeconomic risk can account for the negative relationship between the price of downside risk and volatility.}, keywords={finance} }

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K.","Smith, A."],"bibdata":{"bibtype":"article","type":"article","title":"State Prices of Conditional Quantiles: New Evidence on Time Variation in the Pricing Kernel","author":[{"propositions":[],"lastnames":["Metaxoglou"],"firstnames":["Konstantinos"],"suffixes":[]},{"propositions":[],"lastnames":["Smith"],"firstnames":["Aaron"],"suffixes":[]}],"journal":"Journal of Applied Econometrics","volume":"32","number":"1","pages":"192–217","year":"2017","url":"https://files.asmith.ucdavis.edu/2017_JAppEmet_MS_SPOCQ.pdf","abstract":"We develop a set of statistics to represent the option‐implied stochastic discount factor and we apply them to S&P 500 returns between 1990 and 2012. Our statistics, which we call state prices of conditional quantiles (SPOCQ), estimate the market's willingness to pay for insurance against outcomes in various quantiles of the return distribution. By estimating state prices at conditional quantiles, we separate variation in the shape of the pricing kernel from variation in the probability of a particular event. Thus, without imposing strong assumptions about the distribution of returns, we obtain a novel view of pricing‐kernel dynamics. We document six features of SPOCQ for the S&P 500. Most notably, and in contrast to recent studies, we find that the price of downside risk decreases when volatility increases. Under a standard asset pricing model, this result implies that most changes in volatility stem from fluctuations in idiosyncratic risk. Consistent with this interpretation, no known systematic risk factors such as consumer sentiment, liquidity or macroeconomic risk can account for the negative relationship between the price of downside risk and volatility.","keywords":"finance","bibtex":"@article{metaxoglou2017state,\r\n title={State Prices of Conditional Quantiles: New Evidence on Time Variation in the Pricing Kernel},\r\n author={Metaxoglou, Konstantinos and Smith, Aaron},\r\n journal={Journal of Applied Econometrics},\r\n volume={32},\r\n number={1},\r\n pages={192--217},\r\n year={2017},\r\n\turl={https://files.asmith.ucdavis.edu/2017_JAppEmet_MS_SPOCQ.pdf},\r\n\tabstract={We develop a set of statistics to represent the option‐implied stochastic discount factor and we apply them to S\\&P 500 returns between 1990 and 2012. Our statistics, which we call state prices of conditional quantiles (SPOCQ), estimate the market's willingness to pay for insurance against outcomes in various quantiles of the return distribution. By estimating state prices at conditional quantiles, we separate variation in the shape of the pricing kernel from variation in the probability of a particular event. Thus, without imposing strong assumptions about the distribution of returns, we obtain a novel view of pricing‐kernel dynamics. We document six features of SPOCQ for the S\\&P 500. Most notably, and in contrast to recent studies, we find that the price of downside risk decreases when volatility increases. Under a standard asset pricing model, this result implies that most changes in volatility stem from fluctuations in idiosyncratic risk. Consistent with this interpretation, no known systematic risk factors such as consumer sentiment, liquidity or macroeconomic risk can account for the negative relationship between the price of downside risk and volatility.},\r\n\tkeywords={finance}\r\n}\r\n\r\n\r\n\r\n\r\n","author_short":["Metaxoglou, K.","Smith, A."],"key":"metaxoglou2017state","id":"metaxoglou2017state","bibbaseid":"metaxoglou-smith-statepricesofconditionalquantilesnewevidenceontimevariationinthepricingkernel-2017","role":"author","urls":{"Paper":"https://files.asmith.ucdavis.edu/2017_JAppEmet_MS_SPOCQ.pdf"},"keyword":["finance"],"metadata":{"authorlinks":{"smith, a":"https://asmith.ucdavis.edu/"}}},"bibtype":"article","biburl":"https://www.dropbox.com/s/1yaa9wha4jhlrgu/pubs.bib?dl=1","creationDate":"2020-03-23T21:37:30.534Z","downloads":2,"keywords":["finance"],"search_terms":["state","prices","conditional","quantiles","new","evidence","time","variation","pricing","kernel","metaxoglou","smith"],"title":"State Prices of Conditional Quantiles: New Evidence on Time Variation in the Pricing Kernel","year":2017,"dataSources":["m3trFABzuGWj6pcfa","4PiJhMSXhsNKJ9rxM"]}