Forecasting macro-economy based on the term structure of credit spreads: Evidence from China

Abstract

This paper establishes an original methodology to forecast macro-economy based on the term structure of credit spreads. It combines the traditional Svensson model with genetic algorithms to obtain the interest rate term structures of government bonds and corporate bonds, and calculates credit spreads as their differences. The principal component analysis is used to derive three factors of the term structure of credit spreads: level, slope and curvature. Based on these three factors and several macroeconomic variables including the consumer price index, exchange rate, and the growth rate of industrial and broad money, VAR models are developed and tested to forecast macroeconomic variables. Based on monthly transaction data from Shanghai Stock Exchange in China covering the period from are taken as out-of-sample data to test the models' robustness by comparing the estimates of the forecasting model with the actual values. The empirical results confirm that our VAR models can predict the changes of China's macro-economy well, which indicates that the term structure of credit spreads contains information of future changes of macroeconomic variables. We believe this result has significant implications for macro-economy policy-makers.

Interest Rate Model With Investor Attitude and Text Mining

“…For other literature on estimation of yield curve models, Ren et al [19], Qing and Huahua [20], Liu et al [21], and Maciel et al [22] deal with estimation of yield curve models for government bonds. In addition, Maciel et al [23] and Rong-xi et al [24] utilize yield curve models for forecasting of interest rates.…”

“…for ''two-factor flattening'', where X j,t (τ )(j = 1, 2) and X 3,t (τ ) are defined as the equations ( 23) and (24). With Monte Carlo filter, 7 we estimate three factors that simultaneously explain time series of the yield curve and key word groups' frequencies obtained in 2).…”

“…X 1,t (τ ), X 2,t (τ ) and X 3,t (τ ) are defined on ( 23) and (24). In two-factor steepening model, X 2,t (τ ) ≡ 0.…”