search for: r_t

I would like to know if there is a way to test no correlaction in time series ? cov(r_t, r_t-1)=0 And r_t are homoscedastik and independent. Thanks [[alternative HTML version deleted]]

...nd v_t is the fundamental value of a stock. Now suppose I want a trading strategy to be: x_t+1 = c(v_t – p_t) where c is a costant. I know, from the paper where this equations come from (Farmer and Joshi, The price dynamics of common trading strategies, 2001) that the induced price dynamics is: r_t+1 = –a*r_t + a*e_t + theta_t+1 and p_t+1 = p_t +r_t+1 where r_t = p_t – p_t-1 , e_t = v_t – v_t-1 and a = c/lambda (lambda is another constant). How can I simulate the equations I have just presented? I have good confidence with R for statistical analysis, but not for simulation therefore I apo...

...+ w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) The investment function I would like to use for estimating my endogenous capacity utilization rate looks like (3) g_t = x[1] + x[2]*(u_t-un_t) + x[3]*r + v_t where g_t is the investment rate, r_t is the profit rate, u_t is the actual utilization rate and un_t is the 'normal' utilization rate which I take as endogenous (=time varying). x[i] are parameters. I'm particularly interested in this endogenous normal utilization rate. How can I specify a state space model which allows me...

Hi, I want to fit a mean and variance model jointly. For example I might want to fit an AR(2)-GARCH(1,1) model i.e. r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t where a_t = sigma_t*epsilon_t where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 i.e. R estimates a constant_term1, b, c, constant_term2, p, q TIA Aditya

...he following: r <- c(1:294)*0 rv <- c(1:294)*0 #RUN A LOOP WHERE YOU INPUT THE lx[(i-1)*7:i*7] INTO Z for (i in 1:294){ #CREATE A NEW VECTOR OF LENGTH 7 z <- NULL length(z)=7 dz <- NULL dz2 <- NULL #STORE THE VALUES IN z z <- lx[1+(i-1)*7:(i)*7] #THEN DIFFERENCE THOSE #THIS IS r_t,i,m dz=diff(z) #SUM THIS UP AND STORE IT IN r, THIS IS r_t r[i] <- sum(dz) #SUM UP THE SQUARES AND STORE IT IN rv, THIS IS RV_t dz2 <- dz^2 rv[i] <- sum(dz2) #END THE LOOP } However, the window seems to expand for some reason, so z ends up being a much longer vector than it should be a...

...ook "Analysis of Financial Time Series" by Ruey S. Tsay, and I was succesfull, but I had to use "for" loop, which is quite slow. The loop is necessary, since you need to calculate recursive sequence. Is there a faster way to do this in R, without using loops? The model is such: r_t = \mu + \alpha_2 r_{t-2} + a_t a_t = \sigma_t\varepsilon_t \sigma_t^2 = \beta_1a_{t-1}^2+\beta_2\sigma_{t-1}^2+ 1_{\{a_{t-1}>0\}}(\gamma_0+ \gamma_1a_{t-1}^2+\gamma_2\sigma^2_{t-1}) It is asummed that \varepsilon_t are iid and normal with zero mean and variance one. The data given is r_t, and...

Hey guys, i want to calculate the continuasly compounded returns for stock prices. Formula for CCR: R_t = ln(P_t/P_{t-1})*100 With R: First i have to modify the vectors, so that they have the same length and we start at the second observation. log(GOOG1[-1]/GOOG1[1:length(GOOG1)-1])*100 That does work with normal vectors. My Questions: 1) I want to use this for stock prices. so i use: lib...

Hey, I'm trying to implement a GARCH model with Johnson-Su innovations in order to simulate returns of financial asset. The model should look like this: r_t = alpha + lambda*sqrt(h_t) + sqrt(h_t)*epsilon_t h_t = alpha0 + alpha1*epsilon_(t-1)^2 + beta1 * h_(t-1). Alpha refers to a risk-free return, lambda to the risk-premium. I've implemented it like this: #specification of the model spec = ugarchspec(variance.model = list(model = "sGARCH&qu...

...download%2FRiskMetrics%2520-%2520Technical%2520Document.pdf&ei=RSJhUd7YJIbktQaQ-YCAAw&usg=AFQjCNGpCXUdLSVHQtYJMl7MccLGQtdkDw&sig2=HBxWDrRTMN7rVqWu-Yp1zQ&bvm=bv.44770516,d.Yms Especially page 238 is interesting: "According to this model, returns are generated as follows" r_t=sigma_t xi_t sigma^2_t is calculated by EWMA xi is distributed according to the generalized error distribution. So they do not assume the returns to follow a certain distribution, but they assume the returns condition on the volatility to follow a certain distribution, right? Now my question is...

Dear all R user, Please forgive me if my problem is too simple. Actually my problem is basically Statistical rather directly R related. Suppose I have return series ret with mean zero. And I want to fit a Garch(1,1) on this. my is r[t] = h[i]*z[t] h[t] = w + alpha*r[t-1]^2 + beta*h[t-1] I want to estimate the three parameters here; the R syntax is as follows: #

-- View this message in context: http://r.789695.n4.nabble.com/Generalized-DCC-GARCH-ML-estimation-tp2245125p2245125.html Sent from the R help mailing list archive at Nabble.com.

Hi, I am trying to write the Realized GARCH model with order (1,1) The model can be describe bellow: r_t = sqrt( h_t) * z_t logh_t = w + b*logh_(t-1) + r*logx_(t-1) logx_t = c + q*logh_t + t1*z_t +t2*(z_t ^2 -1) + u_t and z follow N(0,1) , u follow N(0, sigma.u^2) But I'm troubled with the simulation check for my code. After I simulate data from the model and estimate the data, I can't g...

...upperB = c(a = 10*abs(Mean), di = 1-S, mi = 1-S, do = 1-S, fr =1-S, b1 = 1-S, b2 = 1-S,b3 = 1-S, b4 = 1-S, dum = 1-S, alpha0 = 100*Var, alpha = 1-S, beta = 1-S) fitt<-maxLik(start=param, logLik=garch2,method="BHHH", x=dat2$r_csi,Di=dat2$Di,Mi=dat2$Mi,Do=dat2$Do,Fr=dat2$Fr,y=dat2$r_t,z=dat2$r_sp,d=dat2$f) Note that optim always breaks down: nlminb and the BFGS and BHHH algorithmus from the maxLik-package work fine. The estimated coefficients are similiar to those of the EVIEWS Estimation. So I guess, they are correct. Is my Implementation of the Dummy-Variabel in the VAria...

...: install.packages('sos', dep=T) require(sos) findFn('garch') Regards Liviu On Mon, Aug 23, 2010 at 5:59 AM, Aditya Damani wrote: > Hi, > > I want to fit a mean and variance model jointly. > > For example I might want to fit an AR(2)-GARCH(1,1) model i.e. > > r_t = constant_term1 + b*r_t-1 + c*r_t-2 + a_t > > where a_t = sigma_t*epsilon_t > > where sigma^2_t = constant_term2 + p*sigma^2_t-1 + q*a^2_t-1 > > i.e. R estimates a constant_term1, b, c, constant_term2, p, q > > TIA > Aditya > > _____________________________________...

I just switched to "Linux Mint KDE" and from what I have been reading, some Linux users are having some promising results with Playing Oblivion IV. So far I have the newest Wine installed and Oblivion with the Oblivion_v1.2.0416English patch... So a few problems! Under the Applications Tab under Wine, there is a windows folder that says "Brows C: Drive" When I click on it,