35 research outputs found
Non-Standard Errors
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants
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Non-standard errors
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants
Estimation of market prices of risks in the G.A.R.C.H. diffusion model
In this paper we propose an estimation procedure which uses joint
data on the underlying asset and option prices to extract market
prices of return and volatility risks in the context of the G.A.R.C.H.
diffusion model. The procedure is flexible and simple to implement.
Firstly, a quasi-closed form pricing formula for European options in
the G.A.R.C.H. diffusion model is derived. This result greatly eases the
computational burden for computing option prices, and well suited
for our model estimation. Then, based upon the joint data, we develop
an efficient importance sampling-based maximum likelihood (E.I.S.-
M.L.) estimation method for the objective and risk-neutral parameters
of the G.A.R.C.H. diffusion model and a particle filter algorithm for
latent state variable. Hence, this allows us to infer the market prices
of risks that link the objective measure and the risk-neutral measure.
Finally, we illustrate our approach using actual data on the Hang Seng
Index (H.S.I.) and index warrant prices. The results show that both
the return and volatility risks are priced by the market. Moreover,
an option pricing study demonstrates that the market price of the
volatility risk plays an important role in fitting option prices