similar to: Fitting a GARCH model in R

Hi, I was trying to repeat the estimation of threshold GARCH models from the book "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

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta

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

Ok, I try it again with plain text, with a simple R code example and just sending it to the r list and you move it to sig finance if it is necessary. I try to be as detailed as possible. I want to fit a distribution to my financial data using a volatility model to estimate the VaR. So in case of a normal distribution, this would be very easy, I assume the returns to follow a normal distribution

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]]

Dear R users, suppose we have a random walk such as: v_t+1 = v_t + e_t+1 where e_t is a normal IID noise pocess with mean = m and standard deviation = sd and 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

I am trying to make the following Kalman filter equations work and therefore produce their graphs. v_t=y_t - a_t a_t+1=a_t+K_t*v_t F_t=P_t+sigma.squared.epsilon P_t+1=P_t*(1-K_t)+sigma.squared.eta K_t=P_t/F_t Given: a_1=0,P_1=10^7,sigma.squared.epsilon=15099, sigma.squared.eta=1469.1 I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a. Can somebody tell me

I'm trying to use the following command. arima (x, order = c(p,d,q), seasonal =list(order=c(P,D,Q), period=s) How can I write an estimated seasonal ARIMA model from the outputs. To be specifically, which sign to use? I know R uses a different signs from S plus. Is it correct that the model is: (1-ar1*B-ar2*B^2-...)(1-sar1*B^s-sar2*B^2s-....)(1-B)^d(1-B^s)^D

Dear r-users! I have another question regarding the dlm package and I would be very happy if someone could give me a hint! I am using the dlm package to get estimates for an endogenous rate of capacity utilization over time. The general form of a state space model is (1) b_t = G * b_t-1 + 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

Dear all, I am trying to write an R function that can estimate Transfer functions *with additive noise* i.e. Y_t = \delta^-1(B)\omega(B)X_{t-b} + N_t where B is the backward shift operator, b is the delay and N_t is a noisy component that can be modelled as an ARMA process. The parameters to both the impulse response function and the ARMA noisy component need to be estimated simultaneously. I

Hi all, My problem is as follows: I want to run a loop which calculates two values and stores them in vectors r and rv, respectively. They're calculated from some vector x with length a multiple of 7. x <- c(1:2058) I need to difference the values but it would be incorrect to difference it all in x, it has to be broken up first. I've tried the following: r <- c(1:294)*0 rv

Hi all, I'm new with R (and S), and relatively new to statistics (I'm a computer scientist), so I ask sorry in advance if my question is silly. My problem is this: I have a (sample of a) discrete time stochastic process {X_t} and I want to estimate Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} } where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for me to compute

Dear R People: Suppose I want to estimate the parameters of the following AR model: (1 - phi_1 B - phi_2 B^2 - phi_9 B^9) x_t = a_t and I want to use the arima0 command from the ts library. How would I use the order subcommand, please? R Version 1.4.1 for Windows. Thanks! Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston -

This has ABSOLUTELY nothing to do with R but I was hoping that someone might know because there are obviously a lot of very bright people on this list. Suppose I had a time series of data and at each point in time t, I was calculating x bar + plus minus sigma where x bar was based on a moving window of size n and so was sigma. So, if I was at time t , then x bar t plus minus sigma_t would be

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

I am new in R. I can simulate Arma, using Arima.sim However, I want to simulate an Arima Model. Say (1-B)Zt=5+(1-B)at. I do not know how to deal with 5 in this model. Can any one could help me? Thank you very much! Regards, -- View this message in context: http://www.nabble.com/simulate-arima-model-tp23239027p23239027.html Sent from the R help mailing list archive at Nabble.com.

  Hello, fellow R users,   I recently need to estimate a dynamic linear model in the following form:   For the measurement equation:   Y_t = F_t * a_t + v_t   where Y_t is the observation. It is a 1 by q row vector for each t. F_t is my forecasting variable. It is a 1 by p row vector. a_t is my state variable. It is a p by q MATRIX of parameters with each column of the matrix being regression

  Hi, All,   This is the first time I seriously use this package. However, I am confused that the result is quite unstable. Maybe I wrote something wrong in the code? So could anybody give me some hint? Many thanks.   My test model is really simple. Y_t = X_t * a_t + noise(V)，(no Intercept here) a_t = a_{t-1} + noise(W)   I first run the following code: (I shall provide data at the end of the

Hello, I am using {plm} to estimate panel models. I want to estimate a model that includes fixed effects for time and individual, but has a random individual effect for the coefficient on the independent variable. That is, I would like to estimate the model: Y_it = a_i + a_t + B_i * X_it + e_it Where i denotes individuals, t denotes time, X is my independent variable, and B (beta) is the

This is a time series\optimization rather than an R question : Suppose I have an ARMA(1,1) with restrictions such that the coefficient on the lagged epsilon_term is related to the coefficient on The lagged z term as below. z_t =[A + beta]*z_t-1 + epsilon_t - A*epsilon_t-1 So, if I don't have a facility for optimizing with this restriction, is it legal to set A to something and then Optimize