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Although Rf can be negative (but not too negative), Rm cannot be less than Rf as in your example. It is a non-equilibrium situation, no one would invest in risky securities if they have an expectation lower than risk-free securities. So Rm > Rf is a necessary assumption of the CAPM, whether rates are positive or negative. Also, algebra is algebra and the ...


The risk free rate can be viewed as the opportunity cost to hold an investment i.e. Every risky investment should at least pay out the risk free rate. This is why you subtract the Rf from the Rm When yields are negative you would have to add the Rf to Rm meaning you should expect to earn a much lower return [everything else held constant]: CAPM1= negative ...


This simply suggests the linear model is a poor fit in high frequency. But is this that surprising, even before you crunch the numbers? I argue not, for the following reasons: Even at low frequencies (i.e. monthly or annually), it is known that the classical CAPM (which is what you're running, albeit at a much higher frequency) does not fit well. It'd be ...


A high R-squared (1.0) means that you can explain the movements of one time series using the other. The lower your R-squared is, the worse your explanation is -- that includes the 'quality' of your beta. You can try to improve your R-squared score using different regression types. Beware of overfitting.

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