I have several questions. I will split the text up in one high-level description of the goal of my exercise, a detailed description of my potential solution and finally my actual questions. Please remark on my overall approach or idea if you see a more efficient way of reaching my goal.

High-level: I want to predict a financial time-series X, currently on a daily level. I am looking to know whether X is more likely to go up or down tomorrow. I have access to X down to 1m ticks. X does not seem to contain any clear autoregressive component, but does exhibit some persistence in squared returns. I have therefore been able to, with limited success, predict the volatility of X using an ARMA+GARCH approach.

Detailed: There are multiple other assets Y that are very similar to X, and I believe that I could make use of the cross-correlations between X and Y to predict X. More specifically, I want to investigate the cross-correlation between X and Y where Y is lagged to Z lags. I have access to data for all Y in the same resolution as X.

My analysis becomes troublesome as the number of Y is large, and I need an efficient way of determining which Y are potentially interesting. I want to build a function in R for this. I am thinking of having the function output the following, small y indicates a financial asset contained in Y:

  • Acf of X and y

  • Pacf of X and y

  • Correlation matrix

  • CCF

  • Simple linear regression of y~lag(X) and X~lag(y)


  • Do you agree with the approach or would you recommend another method that may provide better results?

  • Would you include anything else in the function?

  • Would you drop anything from the function?

  • What type of output would you recommend if I want the function to correlate X to multiple Y, and lags up to Z of Y, at the same time?


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