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I am trying to calculate the beta of two timeseries by setting up a state-space model, calculating its covariances via the EM algorithm and finally running the kalman filter/smoother. From what I have read, I understand that using the kalman smoother might more sense for what I am trying to achieve since I am in a post-processing environment and not really looking for any real-time predictions.

However, when comparing the two approaches with a a few examples, there are cases where the smoother does seem to improve the beta numbers whereby it feels similar to removing the noise of the kalman filter values via a spline interpolation (see the first picture below) and other cases where the 'interpolation' is quite broad and seems to be 'hiding' a few of the underlying dynamics of the kalman filter results (please see the second picture for example)

Any thoughts on which is the best approach?

EDIT:

adding the difference between the values of the smoother and the filter, as expected the difference goes towards zero in the most recent observations and is actually zero in the latest one:

... -0.03943203, -0.01329412, -0.011849 , -0.01031422, -0.01596532, -0.01822451, -0.00513093, 0.00208434, 0.00244347, -0.0020279 , -0.00991458, -0.0046458 , 0. ])

smoother better

filter better

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  • $\begingroup$ What is "the beta between time-series"? (I guess this relates e.g. beta in CAPM, but unclear). $\endgroup$ – Juho Kokkala Oct 18 '17 at 18:02
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They are not really different approaches in that they are solutions to different problems: one computes the sequence of filtering distributions $p(\beta_t|Y_{1:t})$, and the other the distributions based on all observations $p(\beta_t|Y_{1:T})$, for $t =1,...,T$.

The smoother doesn't "hide underlying dynamics" but rather adjusts its state estimate (with respect to the filter) to reflect the fact that new data has been observed; what "looked like" an increase in $\beta$ at time $t$ is now, on the basis of more accumulated evidence, believed to have been mostly observation noise and a much smaller move in $\beta$.

Which algorithm you should use depends on what you need it for. If you are really looking at this data purely retrospectively as you mention, then smoother is what you want. If you want to build a trading algorithm based on $\beta_t$ then you obviously need to use the filtered estimate in your backtesting because the smoothed one will not be available when you actually use the strategy (it depends on future data).

EDIT to answer additional questions from a comment:

  1. Are you suggesting taking the output of the filter, and then forecasting the beta by fitting some model to it? You are already specifying a model for beta in your state space model (this is often a random walk but does not need to be), and you can forecast directly from this model. A two-step approximation where you first filter with simple linear dynamics and then forecast the filter output with a more complicated model (something that could not have been cast to linear state space form, say) could "work" in some cases I guess, but it would be at the very least logically inconsistent. If you went that route it would definitely need to be done on the filter output and tested for accuracy (the most meaningful way to do so being choosing the model which optimizes something tangible like the P&L on a trading strategy based on your beta forecast, since you can't actually observe the "true" beta).

  2. Both of your beta estimates are implicitly conditional on the dynamics you have assumed for beta. If the dynamics of the filtered/smoothed estimates don't "make sense" then I would perhaps reconsider the state dynamics in the model. Additionally, you have to take into account uncertainty: the fact that the smoother beta increases monotonically for a period does not mean that the "true" beta necessarily did. It is simply your best guess based on the data that you have, which does not actually include beta. You should plot confidence bands so you can see what the smoother is really "saying" about beta. If the data is very noisy and doesn't contain a lot of information about beta, it would not make any sense for the smoother estimate to be "bumpy": that information is just not there.

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  • $\begingroup$ Hi Chris, Thanks a lot for your help. I agree that the smoother is the best one to use for my case however there are two points: 1. As expected, the latest values of the smoother will be almost identical to the filter, therefore, the dynamics of the filter (for example the volatility) could provide some input on the analysis of where is the beta going right now. 2. On the second graph, the smoother timeseries shows that the beta was almost constantly increasing for more than two years - this is hard to believe from a business perspective. Any further thoughts on these? $\endgroup$ – sen_saven Jul 7 '17 at 8:07
  • $\begingroup$ @sen_saven I edited my post to answer these additional questions $\endgroup$ – Chris Haug Jul 7 '17 at 12:31
  • $\begingroup$ +1 Very comprehensive answer, with important distinctions made! $\endgroup$ – Wayne Jul 7 '17 at 12:41
  • $\begingroup$ @ChrisHaug thanks again for your response 1. No I don't plan to remodel the beta by using as input the beta:). All I am telling is that: let's assume I go for the smoother approach and I am trying to tell what are the chances that what I am seeing right now is closer to the 'true beta'... if the filter output is not full of ups and downs which the smoother ended up ignoring in retrospect then I might feel a bit more confident about the number. 2. I have used EM for most of the parameters of my model. 3. Confidence interval bands are a good idea, just the approach I tried was always too wide... $\endgroup$ – sen_saven Jul 7 '17 at 19:49
  • $\begingroup$ ...so it gave little confidence:) In any case, thanks for the help - I am accepting your answer $\endgroup$ – sen_saven Jul 7 '17 at 19:50

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