Testing Monotonicity of Pricing Kernels

Golubev, Yuri; Härdle, Wolfgang Karl; Timofeev, Roman

doi:10.2139/ssrn.2894329

Cited by 8 publications

(14 citation statements)

References 13 publications

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“…Examining options written on the German market index on three dates at the end of June 2000, 2002, Golubev, Haerdle, and Timofeev (2014 reject pricing kernel monotonicity. Their approach is similar to ours, in that they avoid the explicit estimation of subjective densities inferred from historical return data.…”

Section: Introductionmentioning

confidence: 94%

The Pricing Kernel Puzzle in Forward Looking Data

Cuesdeanu

Jackwerth

2016

SSRN Journal

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The pricing kernel puzzle concerns the locally increasing empirical pricing kernel, which is inconsistent with a risk-averse representative investor in a single period, single state variable setting. Some recent papers worry that the puzzle is caused simply by the mismatch of backward looking subjective and forward looking risk-neutral distributions of index returns. By using a novel test and forward looking information only, we generally confirm the existence of a u-shaped pricing kernel puzzle in the S&P 500 options data. The evidence is weaker for tests against an alternative with a risk-neutral investor and for longer horizons.

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Section: Introductionmentioning

confidence: 94%

The Pricing Kernel Puzzle in Forward Looking Data

Cuesdeanu

Jackwerth

2016

SSRN Journal

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“…This difference between the theoretical property of the pricing kernel and the observed failure of it is what we shall call the empirical pricing kernel paradox. A further confirmation of it is provided by Golubev et al (2008). In this paper a test for monotonicity of pricing kernels has been introduced, and applied to DAX data over several periods.…”

Section: Introductionmentioning

confidence: 83%

“…So summarizing, the empirical pricing kernel paradox has been observed across different times independently of the data sets, the markets, the models of stock prices and the employed estimation methods. It is further supported by Golubev et al (2008), where a statistical test for the monotonicity of pricing kernel has been developed. The application to DAX data over different periods lead to the rejection of the null hypothesis that the pricing kernel is nondecreasing, in almost every case.…”

Section: Introductionmentioning

confidence: 97%

A Microeconomic Explanation of the EPK Paradox

Härdle

Krätschmer²,

Moro

2009

SSRN Journal

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Supported by several recent investigations the empirical pricing kernel paradox might be considered as a stylized fact. In Chabi-Yo et al. (2008) simulation studies have been presented which suggest that this paradox might be caused by regime switching of stock prices in financial markets. Alternatively, we want to emphasize a microeconomic view. Based on an economic model with state dependent utilities for the financial investors we succeed in explaining the paradox by changes of the risk attitudes. Theoretically, the change behaviour is compressed by the pricing kernels. As a starting point for empirical insights we shall develop and investigate inverse problems in terms of data fits for estimated basic values of the pricing kernel.

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“…Golubev, Haerdle, and Timofeev (2014) present a formal test of monotonicity of the pricing kernel ( volatilities to obtain the risk-neutral distribution, which however loses some flexibility due to the rigid structure of the Heston (1993) model. For the physical distribution, they use a GARCH model, the Heston (1993) model, and the kernel density based on historical data.…”

Section: The German Dax 30 Index Marketmentioning

confidence: 99%

“…The complicated issue of formally testing for locally increasing segments of the estimated pricing kernel has been taken up in Golubev, Haerdle, and Timofeev (2014) Another test is Härdle, Grith, and Mihoci (2014), which uses the market model of Grith, Haerdle, and Krätschmer (2016). Here, the pricing kernel is parameterized as two decreasing segments with some breakpoint where the pricing kernel jumps up or down.…”

Section: Statistical Testsmentioning

confidence: 99%