Unscented Kalman filter for noisy multivariate financial time-series data

Kalman filter is one of the novel techniques useful for statistical estimation theory and now widely used in many practical applications. In this paper, we consider the process of applying Unscented Kalman Filtering algorithm to multivariate financial time series data to determine if the algorithm could be used to smooth the direction of KLCI stock price movements using five different measurement variance values. Financial data are characterized by non-linearity, noise, chaotic in nature, volatile and the biggest impediment is due to the colossal nature of the capacity of transmitted data from the trading market. Unscented Kalman filter employs the use of unscented transformation commonly referred to as sigma points from which estimates are recovered from. The filtered output precisely internments the covariance of noisy input data producing smoothed and less noisy estimates. © 2013 Springer-Verlag.