Hi,
I am currently working on paper that uses an identical model from a paper titled "Is gold a safe haven? International Evidence", by Baur & McDermott 2010. Here is the description of the model:
In this section, we present the econometric models to analyze the safe haven property of gold. We assume that the gold price is dependent on changes in the stock market. Moreover, we assume that the relationship is not constant but is influenced by specific extreme, market conditions. Eqs. (1a), (1b), (1c) present the principal regression model to analyze the safe haven property of gold:
rGold = a + þstock + e
þ = c0 + c1Dstockq10 + c2stockq5 + c3stockq1
h = pi + ae^2 +þ bht
Eq. (1a) models the relation of gold and stock returns. The parameters to estimate are a and bt. The error term is given by et. The parameter bt is modelled as a dynamic process given by Eq. (1b). The parameters to estimate in Eq. (1b) are c0, c1, c2 and c3. The dummy variables denoted as D(...) capture extreme stock market movements and are equal to one if the stock market exceeds a certain threshold given by the 10%, 5% and 1% quantile of the return distribution. If one of the parameters c1, c2 or c3 is significantly different from zero, there is evidence of a non-linear relationship between gold and the stock market. If the parameters in Eq. (1b) are non-positive (including c0), gold acts as a weak safe haven for the market under study. If the parameters are negative and statistically different from zero, gold functions as a strong safe haven. Gold is a hedge for the market under study if the parameter c0 is zero (weak hedge) or negative (strong hedge) and the sum of the parameters c1 to c3 are not jointly positive exceeding the value of c0. Finally, Eq. (1c) presents a GARCH(1,1) model which is used to account for heteroscedasticity in the data. Eqs. (1a), (1b), (1c) are jointly estimated with Maximum Likelihood. Eq. (1b) focuses on extreme negative returns to model potential non-linearities of the gold–stock index return relationship. If the relationship is, in fact, non-linear, this implies that investors act differently in extreme market conditions compared to normal conditions.
I am having a lot of trouble programming this in STATA. Specifically when attempting to run an arch command with these interactions. I may be getting the code completely wrong but was wondering if anyone may be of help. This is the code I have so far:
arch r_SGOL r_TSX60 c.r_TSX60#(q25_tsx60_dummy q10_tsx60_dummy q5_tsx60_dummy q1_tsx60_dummy), ///
arch(1) garch(1)
I have already created the dummy variables, but I am not sure whether they should be interactions or not.
Thank you.
I am currently working on paper that uses an identical model from a paper titled "Is gold a safe haven? International Evidence", by Baur & McDermott 2010. Here is the description of the model:
In this section, we present the econometric models to analyze the safe haven property of gold. We assume that the gold price is dependent on changes in the stock market. Moreover, we assume that the relationship is not constant but is influenced by specific extreme, market conditions. Eqs. (1a), (1b), (1c) present the principal regression model to analyze the safe haven property of gold:
rGold = a + þstock + e
þ = c0 + c1Dstockq10 + c2stockq5 + c3stockq1
h = pi + ae^2 +þ bht
Eq. (1a) models the relation of gold and stock returns. The parameters to estimate are a and bt. The error term is given by et. The parameter bt is modelled as a dynamic process given by Eq. (1b). The parameters to estimate in Eq. (1b) are c0, c1, c2 and c3. The dummy variables denoted as D(...) capture extreme stock market movements and are equal to one if the stock market exceeds a certain threshold given by the 10%, 5% and 1% quantile of the return distribution. If one of the parameters c1, c2 or c3 is significantly different from zero, there is evidence of a non-linear relationship between gold and the stock market. If the parameters in Eq. (1b) are non-positive (including c0), gold acts as a weak safe haven for the market under study. If the parameters are negative and statistically different from zero, gold functions as a strong safe haven. Gold is a hedge for the market under study if the parameter c0 is zero (weak hedge) or negative (strong hedge) and the sum of the parameters c1 to c3 are not jointly positive exceeding the value of c0. Finally, Eq. (1c) presents a GARCH(1,1) model which is used to account for heteroscedasticity in the data. Eqs. (1a), (1b), (1c) are jointly estimated with Maximum Likelihood. Eq. (1b) focuses on extreme negative returns to model potential non-linearities of the gold–stock index return relationship. If the relationship is, in fact, non-linear, this implies that investors act differently in extreme market conditions compared to normal conditions.
I am having a lot of trouble programming this in STATA. Specifically when attempting to run an arch command with these interactions. I may be getting the code completely wrong but was wondering if anyone may be of help. This is the code I have so far:
arch r_SGOL r_TSX60 c.r_TSX60#(q25_tsx60_dummy q10_tsx60_dummy q5_tsx60_dummy q1_tsx60_dummy), ///
arch(1) garch(1)
I have already created the dummy variables, but I am not sure whether they should be interactions or not.
Thank you.
