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I'm trying to fit the lppl model to KLSE index to predict the most probable crash time. Many papers suggested tabuSearch to identify the initial value for non-linear parameters but none of them publish their code. I have tried to fit the mentioned index with the help of NLS And Log-Periodic Power Law (LPPL) in R. But the obtained error and p values are not significant. I believe that the initial values are not accurate. Can anyone help me on how to find the proper initial values?

library(tseries)

library(zoo)

ts<-get.hist.quote(instrument="^KLSE",start="2003-04-18",end="2008-01-30",quote="Close",provider="yahoo",origin="1970-01-01",compression="d",retclass="zoo")

df<-data.frame(ts)

df<-data.frame(Date=as.Date(rownames(df)),Y=df$Close)

df<-df[!is.na(df$Y),]

library(minpack.lm)

library(ggplot2)

df$days<-as.numeric(df$Date-df[1,]$Date)

f<-function(pars,xx){pars$a + (pars$tc - xx)^pars$m *(pars$b+ pars$c *     cos(pars$omega*log(pars$tc - xx) + pars$phi))}

resids<-function(p,observed,xx){df$Y-f(p,xx)}

nls.out <- nls.lm(par=list(a=600,b=-266,tc=3000, m=.5,omega=7.8,phi=-4,c=-14),fn = resids, observed = df$Y, xx = df$days, control= nls.lm.control (maxiter =1024, ftol=1e-6, maxfev=1e6))

par<-nls.out$par

nls.final<-nls(Y~(a+(tc-days)^m*(b+c*cos(omega*log(tc-days)+phi))),data=df,start=par,algorithm="plinear",control=nls.control(maxiter=10024,minFactor=1e-8))

summary(nls.final)
user3610659
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1 Answers1

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I would look at some of the newer research on this topic, there is a good trig modification that will practically guarantee a singular optimization. Additionally, you can use r's built in linear equation solver, to find the linearizable parameters, ergo you will only need to optimize in 3 dimensions. The link below should get you started. I would cite recent literature and personal experience to strongly advise against a tabu search.

https://www.ethz.ch/content/dam/ethz/special-interest/mtec/chair-of-entrepreneurial-risks-dam/documents/dissertation/master%20thesis/MAS_final_Tuncay.pdf

skatz
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