  Income and risk coexist, yet investors are often so focused on chasing high
returns that they overlook the potential risks that can lead to high losses.
Therefore, risk forecasting and risk control is the cornerstone of investment.
To address the challenge, we construct a multi-factor risk model on the basis
of the classical multi-factor modeling framework. For the common factors,
inspired by Barra Model's factor classification. we adjust the outliers and
missing values of factor exposure data, normalize and finally orthogonalize
them, before computing factor returns and making further analysis. Factor
return covariance matrix and idiosyncratic return variance matrix are essential
tools to express stock returns in the multi-factor risk model. Firstly, we
calculate the factor return covariance matrix with EWMA. To tackle the
time-series autocorrelation of factor returns, we apply Newey-West adjustment.
Then we estimate the idiosyncratic return variance matrix in a similar way and
make Newey-West adjustment again to solve the time-series autocorrelation
problem. Since the return of a single share is sensitive to missing values and
outliers, we introduce structural adjustment to improve the matrix.Eventually,
we obtain the return covariance matrix among stocks and compute the risk of
investment portfolio based on it. Furthermore, we search for optimal portfolio
with respect to minimizing risk or maximizing risk-adjusted return with our
model. They provide good Sharpe ratio and information ratio for considering
both absolute risk and active risk. Hence, the multi-factor risk model is
efficient.
