Over the last few months, GBP/JPY has been moving in a consolidation pattern that is increasingly becoming more narrow, with the eventuality of a possible breakout in either direction (up or down).
After a weekly peak on 172.53, the rate fell to 171.441 even if the long-term uptrend is still ongoing and could lead to new highs. In case there is a further increase, we have to take in consideration a strong resistance set on 174.790, while maintaining a support of 169.330 by March.
The GBPJPY is moving between the resistance and support composed by the triangle above. Recently the Bank of England (BOE) has reported some bad news due to the inflation problem which has remained a huge source of concern. Based on the last inflation data, the BOE is not able to tighten the monetary policy that would see her in front of a real risk of fall of the price levels. Recently we heard talk about the possibility that the Governor of the Bank of England, Mark Carney, can decide to rise up the interest rate with the risk of massive monetary policy actions. If we look at the picture regarding the long-term trend, we can observe how the bullish trend started from a value of 116.83 in 2011, has still growing possibility.
Below we highlight some forecasting techniques based on models of continuous-time stochastic calculus.In particular, the two successive forecasts represent the trend of the price of the exchange rate GBP / JPY assuming that prices follow a geometric Brownian motion. You can learn with respect to the Brownian motion from the following: http://en.wikipedia.org/wiki/Brownian_motion.
In the first scenario, we have taken the time series with time frame equal to 4hours and the historic data start from the 8th October 2013. From the chart, four out of five paths follow a bullish trend due to the fact that, if we analyse the time series with time frame of four hours, we can observe a positive drift, where drift means a positive slope.
In the second scenario we applied the same methodology to a time series starting on the 7th April 2014. The difference with respects to the figure above is evident, ie there is ‘more’ a majority of positive trends, in fact the drift is much lower, close to zero. Looking the first graph in the first page, as a matter of fact there is a parallel shift in the last part of the upward trend.
It is important to focus on a basic concept: these simulations are mere representations of reality’ and even if we measure it 10000, this is not to say that among those 10,000 there will be ‘necessarily the future value of our money and is not easy or possible to correctly guess the future without errors. However we can obtain the probability that the price can be lower or higher of the initial spot price. With a code developed on C++, we can find the following probabilities:
1) The time series with forecast in two days onward and with time frame 1 hour has the probability that the future price will be lower than the actual spot price equal to 50,41%. This value has been obtained with a simulation of more than 10,000 paths.
2) In the other case, despite the graph seems to prefer a bullish trend, actually the probability that the price will be lower than the spot price is equal to 48.08% meaning that on 100 random possibilities, we will observe 48 cases where the price will be lower than the initial price.