Journal of Risk and Financial Management

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Prof. Dr. Hai Lin

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Guest Editor

Wellington School of Business and Government, Victoria University of Wellington, Wellington 6012, New Zealand
Interests: fixed income securities; asset pricing; derivatives; market microstructure

Special Issue Information

Dear Colleagues,

The fixed-income market has developed quickly in the last few decades. Many fixed-income securities were innovated and have had a significant impact on society. For example, credit default swap (CDS) has attracted considerable attention during the Global Finance Crisis (GFC) period between 2007 and 2009. A lot of new questions arise due to the recent development in machine learning, big data, and other areas. This Special Issue will bring to bear the research that addresses emerging questions in the fixed-income market. Topics include but are not limited to: (1) Machine learning and big data analysis of fixed income markets; (2) The impact of fixed income market innovation on firm behaviour; (3) High-frequency trading or market microstructure of fixed income market; (4) Behaviour finance of fixed income market; (5) New modelling of fixed income securities.

Prof. Dr. Hai Lin
Guest Editor

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Keywords

fixed income market
big data
corporate finance
behaviour finance
market microstructure

Published Papers (1 paper)

Research

37 pages, 7688 KiB

Open AccessArticle

The Use of Principal Component Analysis (PCA) in Building Yield Curve Scenarios and Identifying Relative-Value Trading Opportunities on the Romanian Government Bond Market

by Andreea Oprea

J. Risk Financial Manag. 2022, 15(6), 247; https://doi.org/10.3390/jrfm15060247 - 31 May 2022

Cited by 4 | Viewed by 7082

Abstract

Based on previous research addressing the use of principal component analysis (PCA) in modeling the dynamics of sovereign yield curves, in this paper, we investigate certain characteristics of the Romanian government bond market. We perform PCA on data between March 2019 and March 2022, with emphasis on periods marked by extreme market stress, such as the outbreak of the COVID-19 pandemic in March 2020 or the Russian military invasion in Ukraine in February 2022. We find that on 25 March 2022, the first principal component explained 80.83% of the yield curve changes, the first two 91.92%, and the first three 96.87%, consistent with previous results from the literature, which state that the first three PCs generally explain around 95% of the variability in the term structure. In addition, we observe that principal components’ coefficients (factor loadings) at 2 years were lower than those at 10 years, suggesting that in case of market sell-offs, yields at 10 years increase more than those at 2 years, leading to yield curve steepenings. Interestingly, we observe that the explanatory power of the first PC increases significantly following extreme market events, when interest rates’ movements tend to become more synchronized, leading to higher correlations between tenors. We also employ PCA to check for relative-value (RV) trading signals and to assess the historical plausibility of yield curve shocks. We found that while both explanatory power and shape plausibility were characteristics of the yield curve dynamics during the outbreak of the COVID-19 pandemic, the magnitude of the market movement registered in mid-March 2020 was unlikely from a historical perspective. Finally, we use a forecasting model to derive the entire structure of the Romanian yield curve while also incorporating the trader’s view on a few benchmark yields. Full article

(This article belongs to the Special Issue Fixed Income Market)

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