By Myron Scholes and Joseph Williams; Estimating betas from nonsynchronous data. Scholes, Myron & Williams, Joseph, “Estimating betas from nonsynchronous data,” Journal of Financial Economics, Elsevier, vol. 5(3), pages Scholes, M. and Williams, J. () Estimating Betas from Nonsynchronous Data. Journal of Financial Economics, 5,
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Estimating betas from nonsynchronous data
What you ought to be doing is maximum likelihood estimation MLE. There’s really no proper convention here. Please note that corrections may take a couple of weeks to filter through the various RePEc services. How do you estimate the volatility of a sample when points are irregularly spaced? This allows to link your profile to this item. More about this item Statistics Access and download statistics Corrections All material on this site has been provided by the respective publishers and authors.
Estimating Beta from unevenly spaced price history Ask Question. As the access to this document is restricted, you may want to search for a different version of it. Sign up using Facebook. RePEc uses bibliographic data supplied by the respective publishers.
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Whenever you don’t have synchronous data, you’ll have a probability distribution for the missing price conditional on all other data points in its future and in its past. We have no references for this item.
I have a certain non-stock asset that has 1 transaction every 1 to 8 months. There are a lot of different options that might be better in some cases than others. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. You’ll have to assume a parameterized family of joint stochastic processes and estimate the parameters given the price observations.
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Estimating betas from nonsynchronous data
First, what you ought to be regressing are returns, not prices. Sign up or log in Sign up using Google. Right now, I am blindly guessing it through the following steps: Estimating betas betax nonsynchronous data. You can help correct errors and omissions.
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Also, how much effort you put in might depend on what you’re trying to do and what your boss wants. Hence nonaynchronous distribution you’ll be using to maximise the nonsynchrojous of the observed price will be wider than otherwise.
Second, by interpolating you’re underestimating the variance of the asset price in the interval between index price observations. This sounds like the same problem faced when doing model fitting on tick and order book data – do you have any handy references to the conversion from simple regression to using proper MLE when transitioning to asynchronous event data?
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