I am trying to fit kappa for a ticker. I am using 5 days of data to illustrate how this can be done, which isn't that much data but I think is sufficient to show my problem. This data however appears to have a non exponential function for the (A, k) part.
I am following the method described in this post: How does one calibrate lambda in a Avellaneda-Stoikov market making problem?
Once I have my data, I plotted the mean lambda for each bucket of spread. But I found that the curve seems to not be exponential. Here I show the curve from empirical data vs a few choices of kappa:
The y-axis is in log scale and the empirical curve is not linear in particular not linear at the area less than 0.02.
I also fitted with volume-clock (advance the clock by x when x shares are traded)
I am not sure how to deal with this in practice; are there some adjustments that I need to make to the fitting process? Do I need to make k dynamic in production trading depending on if I anticipate large vs small trades next?
The reason I ask is because I believe either the large trades are hurting my PNL when kappa is too large or I can't get volume from small trade when kappa is too small.