Interest rate (in)sensitivity of emerging market corporate debt : economic analysis based on 2002-2015 empirical evidence

Interest rate sensitivity assessment framework based on fixed income yield indexes is developed and applied to two types of emerging market corporate debt: investment grade and high yield exposures. Our research advances beyond the correlation analyses focused on co- movements in yields and/or spreads of risky and risk-free assets. We show that correlation- based analyses of interest rate sensitivity could appear rather inconclusive and, hence, we investigate the bottom line profit and loss of a hypothetical model portfolio of corporates. We consider historical data covering the period 2002 – 2015, which enable us to assess interest rate sensitivity of assets during the development, the apogee, and the aftermath of the global financial crisis. Based on empirical evidence, both for investment and speculative grades securities, we find that the emerging market corporates exhibit two different regimes of sensitivity to interest rate changes. We observe switching from a positive sensitivity under the normal market conditions to a negative one during distressed phases of business cycles. This research sheds light on how financial institutions may approach interest rate risk management, evidencing that even plain vanilla portfolios of emerging market corporates, which on average could appear rather insensitive to the interest rate risk in fact present a binary behavior of their interest rate sensitivities. Our findings allow banks and financial institutions for optimizing economic capital under Basel III regulatory capital rules.

Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.

Person tracking using motion detection and optical flow

Person tracking systems to date have either relied on motion detection or optical flow as a basis for person detection and tracking. As yet, systems have not been developed that utilise both these techniques. We propose a person tracking system that uses both, made possible by a novel hybrid optical flow-motion detection technique that we have developed. This provides the system with two methods of person detection, helping to avoid missed detections and the need to predict position, which can lead to errors in tracking and mistakes when handling occlusion situations. Our results show that our system is able to track people accurately, with an average error less than four pixels, and that our system outperforms the current CAVIAR benchmark system.