Financial Correlation plays a major part in financial markets
which is a statistical tool to measure the relationship between changes of two
or more financial variables over time. Linear correlation, Pearson product-moment correlation coefficient, copula
correlations and many others are various statistical measures to find the
degree of financial correlation. It is vital to understand if there are any
correlations between stocks for example, in a portfolio, as this gives a strong
indication about the various risks associated in the portfolio and volatility.
The goal of financial correlation is to combine assets with low
correlation, for example, stocks in a portfolio to lower the portfolio
volatility and risk. Hence, it is vital to understand how assets are correlated
in a portfolio in order to understand how to balance the portfolio with various
assets and equity by combining them with correct proportions. This is because
the instability of the portfolio is decreased by setting low correlation or
negatively correlated investments in a portfolio.
For example, by combining asset classes such as stocks/equity or
bonds that have low correlation decreases the overall volatility and
instability and therefore allows an investor to invest more. Thus, in order for an investor to earn a high
investment return on a particular portfolio, the latter will have to take
certain amount of risk. Hence, understanding thoroughly how assets are
correlated can lead to huge rewards provided the portfolio of assets is
combined with non-correlated assets which will lower the overall volatility of
Moreover, the GARCH model is also a useful model to adopt to
understand any volatility in assets and portfolios. The model builds on
advances in the understanding and modelling of volatility in the last ten years.
The GARCH model is advantageous as it takes into consideration any surplus
kurtosis (that is fat tail behaviour) and volatility clustering, two essential characteristics
of financial time series. It is beneficial as it also provides exact forecasts of
covariances and variances of asset returns as the model is able to model time-varying
conditional variances. Thus, the GARCH model is very beneficial as it can be
applied to a wide range of fields such as risk management, option pricing,
portfolio management and asset allocation, foreign exchange and the term
structure of interest rates.