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In its simplest terms, imagine you were just using the yield curve as your single predictor of recessions. Suppose (horribly simplistically) that curve inversions tend to signal downturns in 12-18 months time. The curve 12-18 months ago is thus a relevant variable for whether the economy is going into recession or not today. It might also be the case that ...


3

One option is just to fill them in - interest rates don't usually jump around, so interpolating from surrounding data would be unsurprising. If you want to know what effect that is having, by all means mark those you're filling in and duplicate the analysis with a shift to all those filled in rates. If you have an index rather than a set of term rates, then ...


2

in method 1 you did not use the correct definition of the covariance. For two random variables $X$ and $Y$ we have that $$ Cov(X, Y) = E[XY] - E[X]E[Y]. $$ Also, we can use that the covariance is linear in both arguments (this is the bilinearity of the covariance). Therefore \begin{align} \gamma_X(h) &= Cov(X_{t+h}, X_t) = Cov(a+ bZ_{t + h} + cZ_{t + h -...


1

Are you dealing with overnight rates, such as Fed Funds? In such cases the same rate continues to be paid while the markets are closed. So for example if FF is X on Friday, it means you will earn X for three days: Saturday, Sunday and Monday. In this sense, there are no "blanks" in interest rate data, you earn some interest every day, whether a ...


1

Hmm... some notable implicit assumptions made en passant here ;-) How persistent are these autocorrelations (ACs)? Let's unpick a little. One obvious question is whether your AC process is strong enough to overcome transaction costs and slippage, if markets are almost-random. Then someone trying to trade that could easily just get their position sizes ...


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