The term structure of economic management rate under the parameter analysis of the estimation model based on common differential equation

To reflect the country's economic growth, inflation and the implementation of monetary policies. Based on the monthly data of national debt yield from January 2015 to December 2019, these data are divided into 1 year to 30 years according to the maturity period, and the principal component analysis of the term structure of interest rate from 2012 to 2017 shows that the factors affecting the change of term structure of interest rate include level factor, skew factor and curve factor. The variance contribution rates of these factors to the variation of interest rate term structure curve are 82.2002%, 16.9948% and 0.6283% respectively. The horizontal factor represents the position of the term structure of interest rate, the skew factor represents the degree of skew of the term structure of interest rate, and the curve factor determines the interest rate.

Shape Evolution of the Interest Rate Term Structure

This paper adopts a novel approach to studying the evolution of interest rate term structure over the U.S. business cycles and to predicting recessions. Applying an effective algorithm, I classify the Treasury yield curve into distinct shapes and find the less frequent shapes intrinsically linked to the recessions in the post-WWII data. In forecasting recessions, the median-short yield spread trumps the long-short spread for horizons up to 17 months ahead and the yield curve shape is nearly impressive as the median-short spread. Overall, the yield curve shape is an informative but more succinct indicator than the spreads in studying the term structure. Key words: Business cycle, recession forecast, U.S. Treasury yield curve, yield spreads.

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.

Interest Rate Term Structure Decomposition at the Instrument Level

The paper examines term structure decomposition at the instrument level – decomposing term structures for issues as well as the portfolio. Three different implementations are stipulated: axiomatic structural approaches, a sequential approach, and a base structure approach. The three different implementations are evaluated against a portfolio of risk-free government bonds. The goodness-of-fit and smoothness properties of instrument-level term structure decomposition are also considered. The conclusion points to remaining gaps in theory regarding instrument-level term structure decomposition, and considers areas of application – typically bond valuation.

Interest Rate Term Structure Decomposition: An Axiomatic Structural Approach

The paper examines an axiomatic structural approach to term structure decomposition. From this perspective, term structure decomposition is modelled as an non-parsimonious optimization problem, with the structure delineated by constraints related to the likely attributes thereof, rather than by a linear combination of splines or functions. The motivation for the model lies in its perceived flexibility or power. Also, the model is seen as a likely candidate to implement issue-level term structure decomposition. Consequently, issue-level term structure decomposition is also briefly introduced. The power of the model is tested on a simulated and market sample. Even though it may go against notions of structure smoothness, the relationship or correlation between structure smoothness, goodness of fit, and systematic/ unsystematic risk is also touched on.