volatility

I am doing a simulation of a GARCH model. The model itself is not too relevant, what I would like to ask you is about optimizing the simulation in R. More than anything if you see any room for vectorization, I have thought about it but I cannot see it. So far what I have is this: Let: # ht=cond.variance in t # zt= random number # et = error term # ret= return # Horizon= n periods ahead So this is the code: randhelp= function(horizon=horizon){ ret <- zt <- et <- rep(NA,horizon)#initialize ret and zt et for( j in 1:horizon){ zt[j]= rnorm(1,0,1) et[j] = zt[j]*sqrt(ht[j]) ret[j]=mu + et[j] ht[j+1]

We wrote a function get_timestamp() defined as CREATE OR REPLACE FUNCTION get_timestamp() RETURNS integer AS $$ SELECT (FLOOR(EXTRACT(EPOCH FROM clock_timestamp()) * 10) - 13885344000)::int; $$ LANGUAGE SQL; This was used on INSERT and UPDATE to enter or edit a value in a created and modified field in the database record. However, we found when adding or updating records consecutively it was returning the same value. On inspecting the function in pgAdmin III we noted that on running the SQL to build the function the key word IMMUTABLE had been injected after the LANGUAGE SQL statement. The

In my multithreaded asmx web service I had a class field _allData of my own type SystemData which consists of few List<T> and Dictionary<T> marked as volatile . The system data ( _allData ) is refreshed once in a while and I do it by creating another object called newData and fill it's data structures with new data. When it's done I just assign private static volatile SystemData _allData public static bool LoadAllSystemData() { SystemData newData = new SystemData(); /* fill newData with up-to-date data*/ ... _allData = newData. } This should work since the assignment is atomic and the threads