Why is volatility important? Volatility is essential and is essential for valuing any asset within the financial market, from a single stock to the most complex derivative product. This is very important in portfolio management or in calculating the risk or the corresponding hedging strategy, but the problem is that it is not observable and it is heteroscedastic, it fluctuates over time, it should not be Do not assume a homoscedastic model, constant over time, because it will be a huge error when estimating. For this, a huge literature has put enormous effort into trying to predict future volatility as accurately as possible, which is why a large number of sophisticated models have been created since the crucial study by Engle (1982), in which he introduced the ARCH model. As financial variables are characterized by non-linear dependence, the ARCH model captures this dependence because it allows heteroskedasticity, precisely, it only depends on the last lag. However, when the sample is considerably large, the ARCH model does not have the ability to capture this dependence because it will require so many lags that the estimation will become too complex. Later, Bollerslev (1986) introduced the GARCH model. The main advantage is that it is able to capture volatility memory, this parameter is beta allowing large samples. The main problem is that we define a symmetrical response in volatility, that is to say that it is only a question of taking into account the magnitude of the shocks, rather than their sign, which means that for the same magnitude, negative and positive shocks will have the same effect. . This is called leverage and much empirical evidence has shown that it is present in financial variables, although it is not that significant for exchange rates...... . medium of paper ......e. Although this performance is quite good, the GARCH model faces two problems that will appear during estimation. (1) Symmetric response to volatility, known as leverage, which is quite well resolved with the use of other more sophisticated GARCH type models, in this case they suggest the TGARH, although they note that this effect is not too significant in the case of exchange rates. (2) Constant volatility, they introduce the GARCH component which makes it possible to introduce a model of volatility varying over time, a functionality unachievable with the estimation of a GARCH which only allows a mean reversion path. Ultimately the conclusion is that the GARCH model beats statistical exchange rates for the majority of exchange rates, this result is linked to the evidence obtained by Andersen and Bollerslev (1998) and Andersen et al.. (1999)