Dueker JBES 1997 MS-GARCH models

[TomDoan]

These are replication files for Dueker(1997), "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," J of Business & Economic Statistics, vol. 15, no 1, 26-34 using a reconstruction of the original data file. Dueker fits several different models with different types of switching. All of models allow the mean to switch, but two of these have "variances" switching, and two have the degrees of freedom in the conditional t density switching in different ways.

Switching GARCH (as opposed to ARCH) models require "collapsing" the history of the regimes in some fashion. Dueker waits one period longer for this than Gray(1996) does in "Modeling the conditional distribution of interest rates as a regime-switching process", J. of Financial Economics, vol 42, pp 27-62. This actually makes it easier to adapt the technique to different models than Gray's.

Switching GARCH models is one of the topics in the course materials: viewtopic.php?f=24&t=1185

dueker_swgarch.rpf

GARCH model with switching mean (only)

(4.66 KiB) Downloaded 195 times

dueker_swgarch_nf.rpf

GARCH model with switching variance

(5.53 KiB) Downloaded 216 times

dueker_swgarch_df.rpf

GARCH model with switching degrees of freedom in t error process

(5.26 KiB) Downloaded 148 times

dueker_swgarch_k.rpf

GARCH model with switching degrees of freedom

(5.29 KiB) Downloaded 154 times

dueker_swarch.rpf

SWARCH model

(4.65 KiB) Downloaded 178 times

sp500.xls

(Reconstructed) data file

(93 KiB) Downloaded 200 times

[lumengobobo46]

Dear Tom

I am trying to display the conditional variance according to the different regimes for the first programme (DUEKER-SWARCH). I use the followings:
set momo = gv(1)*h(i)
graph
# momo

i have an answer: missing operator or adjacent operator.

please can you help. I will also like to display the series momo.

Regards

[TomDoan]

1. Where are you putting this?
2. The "ARCH" factors are dependent upon the expanded states (8 of them with two regimes and two lags + current) and the GV scaling depends upon the contemporaneous regimes, so you wouldn't be using gv(1). Are you looking for eight time series, or did you want something else?

[lumengobobo46]

Hi tom

Thank you for your prompt response
1. I write the code at the end of the programme.
2. I want to obtain all the time series for conditional volatility (ARCH) related to the different regimes. they are more than 2000 observations.

Regards

[TomDoan]

Hi tom

Thank you for your prompt response
1. I write the code at the end of the programme.
2. I want to obtain all the time series for conditional volatility (ARCH) related to the different regimes. they are more than 2000 observations.

Regards

What I'm saying is that at each point in time, there are eight numbers, one for each combination of regimes for t, t-1 and t-2. At any given time period, many of those will have virtually zero probability, so the conditional volatility could be almost complete nonsense. I don't think those time series are especially useful---so the question is, what do you really want?

[lumengobobo46]

Hi Tom
I am currently trying to replicate a paper: "Rodrigues, J.C. (2007). Measuring financial contagion: A copula approach, journal of emepirical economics,14, 401-423." by using my own data. The main idea of the paper is to find dependence of conditional volatilities in high and low regime (states) of volatilities. i thought that if i can have time-series data of volatilities in the different regimes, after estimating a SWARCH model, then i could model a copula to find dependence between them. I am expecting the dependence to be different in the different regimes(represented by those time series i would like to derive from the estimated SWARCH model).

REgards