Abstract
Volatility is an important parameter for financial risk management and it is applied in many issues such as option pricing, portfolio optimization, VaR methodology and hedging; thus the forecasting of volatility or variance can be regarded as a problem of financial modelling. The objective of this paper is to forecast FTSE 100 Stock Prices of top 100 companies listed on London Stock Exchange by using the Exponential Weighted Moving Average (EWMA) Model. The data for this model are directly obtained from the UK FTSE 100 Index. In this research paper, we have examined the daily returns of FTSE 100 Stock Prices of top 100 companies listed on London Stock Exchange from the thirtieth day of June 2009 to the first day of December 2014 and equally forecasted the daily returns from the first day of December 2014 to the fifth day of February 2015 with the Exponential Weighted Moving Average (EWMA) Model. We found that there is a very high possibility that the stock prices will start to fall as from 5th February 2015 downwards.
Highlights
It is well established that to estimate the volatility of a stock price empirically, the stock price is usually observed at fixed intervals of time
According to theefficient-market hypothesis, stock price movements which is quite unpredictable are controlled by the random walk hypothesis, implying that the best forecasting on tomorrow’s price is today’s price value.Some researchers identified a large number of statistical models and financial variables that are useful to predict the future price of stock market
Exponential Weighted Moving Average (EWMA) is basically a special form of an Autoregressive Conditional Heteroskedasticity (ARCH)() model, with such characteristics which include the fact that the ARCH order is equal to the sample data size and the weights are exponentially declining at rate λ throughout time
Summary
It is well established that to estimate the volatility of a stock price empirically, the stock price is usually observed at fixed intervals of time. A model can be defined as “a simplified description of reality that is at least potentially useful in decision-making” [1] It is the imitation of real world events which gives us the opportunity to study events in order to make accurate decisions about the future before the actual events take place. There is ability to study and compare different future possible alternatives or actions so as to choose the least expensive method that best suits the requirements of a user and thereby avoid potential costs associated with trialling in real life Another benefit is the setting up experimental conditions allows us to gain control on the model so as to reduce the variance of the results output without upsetting the mean values. This opportunity is not available to the real world system
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