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

vlambda
2020-05-07

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

原文链接：http://tecdat.cn/?p=12187

金融分析师通常关心检测市场何时“发生变化”：几个月或至几年内市场的典型行为可以立即转变为非常不同的行为。投资者希望及时发现这些变化，以便可以相应地调整其策略，但是这样做可能很困难。

RHmm从CRAN不再可用，因此我想使用其他软件包复制功能实现马尔科夫机制转换(Markov regime switching)模型从而对典型的市场行为进行预测，并且增加模型中对参数的线性约束功能。


  
    
    
   
    
     
     
     
     
     
     
     
     
     
    
   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)

DimObs = 1

 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)

使用logLik在迭代69处收敛：125.6168

 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 )

DimObs = 1

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

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


#*****************************************************************# 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)

使用logLik在迭代30处收敛：18358.98

print(summary(fm2))

Initial state probabilties model pr1 pr2 pr3 pr4 0 0 1 0
Transition 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-01
Response 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')