There are eight categories of data released daily for the eight major currencies or countries thatare closely followed in Forex market. The currencies are as follow: USD, EUR, GBP, JPY, CHF,CAD, AUD, NZD? As the general rule because US dollars is in more than 90% of trades, US economic news tend tohave the most impact on the market.

? Asset holders are interested in the volatility of returns over the holding period, not over somehistorical period. This forward-looking view of risk means that it is important to be able toestimate and forecast the risk associated with holding a particular asset. To model and forecastvolatility I need to develop a model to forecast conditional heteroskedasticity.? The unconditional variance (the long-run forecast of the variance) would be unimportant if Iplan to buy the asset at t and sell it at t+1.? Conditional forecast means I have all the information up to today and I want to forecasttomorrow.? To build the model I use Internet search queries for the “keywords in News” related to eachcurrency. These queries can be obtained from google trends.

? I use ARCH model since most of the models related to volatility estimation of the financialmarkets are ARCH and GARCH models.? ARCH and GARCH are based on stationary data. So I need to make sure that my news Googletrends are all stationary.? I also do the LM test for ARCH model to check whether conditional heteroskedasticity exists.? Y t+1 =e t+1 x t. Where Y t+1 is the variable of interest that is volatility, e t+1 is the white-noise disturbanceterm with the variance of sigma squared and x t is an independent variable that can be observedat period t which is news Google trend. I will have 8 independent variables. Here the realizationsof the x t sequence are not equal, so the variance of Y t+1 conditional on the observable value of x t .

? Since I can observe x t at time period t, I can form the variance of Y t+1 conditionally on the realizedvalue of x t .? One simple strategy is to forecast the conditional variance as an AR(q) process using squares ofthe estimated residuals.e t 2 = a 0 + a 1 e 2 t-1 + a 2 e 2 t-1 +…..+ a q e 2 t-q + v tIs an auto regressive process which is the ARCH (Autoregressive Conditional Heteroskedastic)model, v t is the white-noise process.

I can use this equation to forecast conditional variance att+1 asE t e 2 t+1 = a 0 + a 1 e 2 t + a 2 e 2 t-1 +…..+ a q e 2 t+1-q? This model is the linear specification which is not the most convenient.

? The conditional heteroskedasticity in { e t } will result in {y t } being Heteroskedastic itself. So theARCH model is able to capture periods of tranquility and volatility in the {y t } series.