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There are a few ways to do this. For example, the FRBNY (google FRBNY and nowcasting) creates a weekly GDP number from monthly and weekly series. You can sift through that to see how they change the time steps. In the past I generated weekly unemployment data (which is a monthly series) from the pattern of weekly unemployment claims or something like ...


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First this is not a full answer, but it might help you. You probably hit $B$ quickly with $(1)$ than with $(2)$. Hint of previous assertion I might reformulate your question. I suppose your pricing condition is $$\left\langle X^{(2)}\right\rangle_t=\left\langle X^{(1)}\right\rangle_t $$ so you get : $$X^{(1)}_t = ...


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auto.arima has many unresolved issues. see: http://www.stat.pitt.edu/stoffer/tsa3/Rissues.htm


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You can do it manually. Let x be the data series. The code below considers all moving-average lag orders between 0 and max.q and prints out the BIC-minimizing lag order and the corresponding estimated model: m=list() # I will save estimated ARIMA(1,0,q) models here BIC=c() # I will save the corresponding BIC values here max.q=10 # the maximum MA order you ...


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Challenge with completely randomized yields is that it's hard to ensure the data is arbitrage-free. What you can do is either using the data from another market (say take the UK yield and pretend they are in USD) or use randomized resampling of SETS of data.



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