R语言中的马尔科夫机制转换(Markov regime switching)模型

library(SIT)load.packages('quantmod')	# find regimes	load.packages('RHmm', repos ='http://R-Forge.R-project.org')	y=returns	ResFit = HMMFit(y, nStates=2)	VitPath = viterbi(ResFit, y)

	matplot(fb$Gamma, type='l', main='Smoothed Probabilities', ylab='Probability')		legend(x='topright', c('State1','State2'),  fill=1:2, bty='n')

	fm2 = fit(mod, verbose = FALSE)

	probs = posterior(fm2)	layout(1:2)	plot(probs$state, type='s', main='Implied States', xlab='', ylab='State')	matplot(probs[,-1], type='l', main='Probabilities', ylab='Probability')		legend(x='topright', c('State1','State2'),  fill=1:2, bty='n')

	#*****************************************************************	# Add some data and see if the model is able to identify the regimes	#****************************************************************** 	bear2  = rnorm( 100, -0.01, 0.20 )	bull3 = rnorm( 100, 0.10, 0.10 )	bear3  = rnorm( 100, -0.01, 0.25 )	true.states = c(true.states, rep(2,100),rep(1,100),rep(2,100))	y = c( bull1, bear,  bull2, bear2, bull3, bear3 )

	plota(data, type='h', x.highlight=T)		plota.legend('Returns + Detected Regimes')

#*****************************************************************# Load historical prices#****************************************************************** data = env()getSymbols('SPY', src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)price = Cl(data$SPY)	open = Op(data$SPY)ret = diff(log(price))	ret = log(price) - log(open)atr = ATR(HLC(data$SPY))[,'atr']fm2 = fit(mod, verbose = FALSE)

print(summary(fm2))

Initial state probabilties model pr1 pr2 pr3 pr4 0 0 1 0Transition matrix toS1 toS2 toS3 toS4 fromS1 9.821940e-01 1.629595e-02 1.510069e-03 8.514403e-45 fromS2 1.167011e-02 9.790209e-01 8.775478e-68 9.308946e-03 fromS3 3.266616e-03 8.586650e-47 9.967334e-01 1.350529e-69 fromS4 3.608394e-65 1.047516e-02 1.922545e-130 9.895248e-01Response parameters Resp 1 : gaussian Resp 2 : gaussian Re1.(Intercept) Re1.sd Re2.(Intercept) Re2.sd St1 2.897594e-04 0.006285514 1.1647547 0.1181514 St2 -6.980187e-05 0.008186433 1.6554049 0.1871963 St3 2.134584e-04 0.005694483 0.4537498 0.1564576 St4 -4.459161e-04 0.015419207 2.7558362 0.7297283 	Re1.(Intercept)	Re1.sd	Re2.(Intercept)	Re2.sdSt1	0.000289759401378951	0.00628551404616354	1.16475474419891	0.118151350440916St2	-6.98018749098021e-05	0.00818643307634358	1.65540488736983	0.187196307284941St3	0.000213458358141314	0.00569448330115608	0.453749781945066	0.156457606460757St4	-0.00044591612667264	0.0154192070819596	2.75583620018895	0.72972830143278

probs = posterior(fm2)print(head(probs))

rownames(x)	state	S1	S2	S3	S41	3	0	0	1	02	3	0	0	1	03	3	0	0	1	04	3	0	0	1	05	3	0	0	1	06	3	0	0	1	0

layout(1:3)plota(temp, type='l', col='darkred')	plota.legend('Market Regimes', 'darkred')

layout(1:4)