Forecasting term structure of the Japanese bond yields in the presence of a liquidity trap

The Nelson–Siegel (NS) model is widely used in practice to fit the term structure of interest rates largely due to its high efficacy in the in-sample fit and out-of-sample forecasting of bond yields. In this paper, we compare forecasting performances of estimated yields from the Nelson–Siegel-based models and some simpler time series models, using the daily, weekly, and monthly data during a prolong period of liquidity trap in Japan. We find that the out-of-sample expanding window forecasts by NS-based models in general perform less satisfactory than the competitor models. However, the NS-based models can be useful in forecasting yields over longer horizons and can work well with GARCH-type structures in modeling the conditional volatility

MODELLING DEPENDENCE IN STI COMPONENT STOCKS: AN ANALYSIS WITH R-VINE COPULA MODELS

Bachelor'sBachelor of Social Sciences (Honours

Handling Noisy Labels via One-Step Abductive Multi-Target Learning: An Application to Helicobacter Pylori Segmentation

Learning from noisy labels is an important concern because of the lack of accurate ground-truth labels in plenty of real-world scenarios. In practice, various approaches for this concern first make some corrections corresponding to potentially noisy-labeled instances, and then update predictive model with information of the made corrections. However, in specific areas, such as medical histopathology whole slide image analysis (MHWSIA), it is often difficult or even impossible for experts to manually achieve the noisy-free ground-truth labels which leads to labels with complex noise. This situation raises two more difficult problems: 1) the methodology of approaches making corrections corresponding to potentially noisy-labeled instances has limitations due to the complex noise existing in labels; and 2) the appropriate evaluation strategy for validation/testing is unclear because of the great difficulty in collecting the noisy-free ground-truth labels. In this paper, we focus on alleviating these two problems. For the problem 1), we present one-step abductive multi-target learning (OSAMTL) that imposes a one-step logical reasoning upon machine learning via a multi-target learning procedure to constrain the predictions of the learning model to be subject to our prior knowledge about the true target. For the problem 2), we propose a logical assessment formula (LAF) that evaluates the logical rationality of the outputs of an approach by estimating the consistencies between the predictions of the learning model and the logical facts narrated from the results of the one-step logical reasoning of OSAMTL. Applying OSAMTL and LAF to the Helicobacter pylori (H. pylori) segmentation task in MHWSIA, we show that OSAMTL is able to enable the machine learning model achieving logically more rational predictions, which is beyond various state-of-the-art approaches in handling complex noisy labels.Comment: 71 pages, 39 figure