4 research outputs found
Hedging Against the Interest-rate Risk by Measuring the Yield-curve Movement
By adopting the polynomial interpolation method, we propose an approach to
hedge against the interest-rate risk of the default-free bonds by measuring the
nonparallel movement of the yield-curve, such as the translation, the rotation
and the twist. The empirical analysis shows that our hedging strategies are
comparable to traditional duration-convexity strategy, or even better when we
have more suitable hedging instruments on hand. The article shows that this
strategy is flexible and robust to cope with the interest-rate risk and can
help fine-tune a position as time changes.Comment: 12 pages, 2 tables, 5 figure
Joint attribute chain prediction for zero‐shot learning
Zero‐shot learning (ZSL) aims to classify the objects without any training samples. Attributes are used to transfer knowledge from the training set to testing one in ZSL. Most ZSL methods based on Direct Attribute Prediction (DAP) assume that attributes are independent of each other. In this study, the authors explore the relationship between attributes and propose Joint Attribute Chain Prediction (JACP). Attribute chains are introduced to represent the relations. Conditional probabilities of attributes are estimated orderly along the chain to calculate the joint posteriors of the testing classes without independence assumptions. To reduce the estimation error, attribute relation clustering algorithm is presented to group the long chain into some unrelated small chains. When the max length of chains is one, JACP is essentially identical with DAP. Experiments on three data sets for zero‐shot problem demonstrate the classification accuracy and efficiency of the authors’ algorithm. The results show that mining attribute relations can greatly improve the performance of ZSL effectively