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Have a look at the following paper: Regime Shifts: Implications for Dynamic Strategies by Kritzman, Page and Turkington From the paper (p. 25): But why go through all the trouble? When dealing with regime shifts, we expect Markov-switching models to perform better than simple data partitions based on thresholds. For example, in Figure 1, if we had ...

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I think your time series is too short. Yours has lenght 10 and you estimate with parameter niterblkopt = 10. E.g. if you have a time series twice as long then it works: library(MSBVAR) a <- c(1.998513, 1.995302, 2.030693, 2.122130, 2.236770, 2.314639, 2.365214, 2.455784, 2.530696, 2.596537) b <- c(0.6421369, 0.6341437, 0.6494933, 0.6760939, 0....

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I don't understand how technical indicators are at all relevant to the question. State probabilities can be generated directly from the returns if the model is known. There is no need to guess at heuristic trading rules based on technical indicators. Let $r_t$ be the return at time $t$. Your model is $E\{r_t | s_t=i\} \sim N(\mu_i,\sigma^2_i), i=0,1$ \$P\{...

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HMM allows to get transition matrix that provides additional information itself about probabilities of switching. As HMM looks on complete state path it allows to identify, for example, short periods of low volatility in high volatility regime that were not a result of regime switching. If we apply some simple rule we have a larger number of switches and ...

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