Changes In Yield Curve Research Articles

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

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.

Monetary policy shocks identified using the entire yield curve: an alternative approach

ABSTRACT Conventional monetary policy (MP) shocks are typically identified as unexpected changes in the short end of the yield curve. During the zero lower-bound period, central banks attempt to provide stimulus by operating on the long end of the yield curve with new tools, such as forward guidance and large-scale asset purchase. This implies that the identification of MP shocks should be done throughout the entire yield curve. This study proposes an alternative approach to analysing the effects of MP shocks, using factor-augmented vector autoregression (FAVAR) of monthly data. We therefore used information from large datasets and made changes over the entire yield curve as shocks to the VAR, which were identified using external high-frequency instruments. Furthermore, empirical analyses using economic and financial variables showed that contractionary yield curve changes decrease the inflation rate and increase risk aversion and uncertainty, which is consistent with conventional wisdom.

Scenario-based measurement of interest rate risks

PurposeThis paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.Design/methodology/approachThe authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.FindingsThe backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.Research limitations/implicationsThe paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.Practical implicationsThe authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.Originality/valueThe authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

Towards a Deeper Understanding of Yield Curve Movements

From a modeling perspective, daily movements in risk-free yield curves deserve further exploration. In this paper, I study daily yield curve changes for three major liquid sovereign yield curves, namely US, UK and Germany. I focus on two main themes: (1) characterizing the statistical distribution of the multivariate vector of daily yield curve changes, and (2) exploring efficiency of deep learning based dimensionality reduction approaches for the daily yield curve changes. Specifically, I am able to reject the null hypothesis that daily yield curve changes are normally distributed. I also show that a multivariate student-t distribution with 2 degrees of freedom or a gaussian mixture model are more accurate at capturing the leptokurtic distribution of daily yield curve changes. In addition, on the dimensionality reduction front, I show that a deep neural network auto-encoder outperforms a PCA based linear decomposition approach.

Libor Benchmark Reform: An Overview of Libor Changes and Its Impact on Yield Curves, Pricing and Risk

Libor is arguably the world's most important number, with more than USD 200 trillion of derivatives, loans, securities and mortgages referencing this rate in the US markets alone. The Libor benchmark rate is being replaced with alternative reference rates (ARRs) and there is no guarantee the rate will continue to be quoted beyond 2021. In this paper Libor benchmark rate reform is discussed in detail and we assess the impact this has on yield curve construction, interest rate pricing and risk. We highlight why Libor is important, review its history and how it has evolved, which leads to a discussion as to what is wrong with Libor benchmarks. We outline market terminology with regards to both interest rates and yield curve construction, before proceeding to assess on the impact of Libor reform, reviewing the new benchmarks, fall-back rates and yield curve changes. We review yield curve calibration and in doing so provide many charts and Excel workbook illustrations to demonstrate new features of ARR yield curves. We explain how to both bootstrap and globally calibrate curves to imply forward rates & discount factors. Moreover, we outline the interpolation, optimization and solving process, showing how to calibrate curves in such a way to capture the necessary risk metrics required to compute analytical risk and rebuild curves for ultra-fast performance. It is hoped this paper will serve as a useful Libor benchmark rate reform and yield curve primer.

Measuring euro area monetary policy

We map ECB policy communication into yield curve changes and study the information flow on policy dates. A byproduct is the publicly available Euro Area Monetary Policy Event-Study Database (EA-MPD), containing intraday asset price changes. We find that Policy Target, Forward Guidance and Quantitative Easing factors capture about all the variation in the yield curve, with different factors appearing in the windows covering the policy decision announcement and the press conference, and having time-varying variance shares. We study sovereign yields, exchange rates, stock prices, persistence of effects and response asymmetry. Our methodology can be implemented for any policy-related event.

A regression and comparative study of United States and South African yield curves using principal component analysis

Volatile markets and economic environments can significantly distort the shape and smoothness of yield curve movements. This study explores the influence of movements in United States interest rates on South African interest rates. This study aims to identify the main underlying movements present in the United States and South African yield curves and to further determine the dominant factors that are responsible for driving South African interest rate movements. The principal settings for the study were the United States and South African markets representing, respectively, a developed and developing market. Principal component analysis was used to discern the major drivers of developing and developed market interest rates. The findings show that the principal component analysis technique is able to effectively classify and quantify the movements of yield curves across both markets in terms of three main factors, namely level, slope and curvature shifts. During certain periods, South African yield curve changes were largely driven by variations in United States interest rates and the rand/dollar exchange rate. Results also demonstrated that a volatile market and economic environment can significantly distort the shape and smoothness of yield curve movements.

Dynamic Forces behind the Common Currency Risk Factors' Expected Moments

This paper examines the association between option-implied distributions of common currency risk factors (dollar ($RX$) and carry ($HML_{FX}$) ) and macroeconomic expectations in form of spread yield curve changes. When currencies are interpreted as an asset price, exchange rates are considered to equal the sum of discounted future macroeconomic fundamentals. The term structure of yield-spreads contains unobservable information about the same expected macroeconomic differentials that drive foreign exchange rates. Using data from G10 currencies, we find a consistent macro-risk based explanation on the common currency risk factors' option-implied moments. These findings are not only important for carry traders, but also contribute to the understanding of currency risk in the cross section.

A Continuous-Time Dynamics of Interest Cash Flows from a Bankks Loan Portfolio

A continuous-time deterministic model for analytical simulation of impact of the change in yield curve on bank’s interest income from a fixed rate loans portfolio is presented. It is considered both differential and integral presentations of equations for dynamics of principal and interest cash flows. The model allows taking into account the arbitrary movements in interest rates. Examples of calculation of total (from portfolio) and partial (across maturities) interest cash flows and corresponding interest rates on those cash flows are given. The sensitivity of total interest cash flows is evaluated for some changes in yield curve on loans that obeys the Nelson-Siegel model.

Yield curve changes effect on Euro area bond indexes: a partial durations approach

The dimension of the interest rate changes impact on bond prices depends on bond duration and convexity. The present paper uses a partial durations approach, combined with convexity measures and maturity segmentation, to estimate the impact of the Euro area yield curve shifts on the values of highest grade European government bond indexes.

Principal component analysis of yield curve movements

An important issue in interest rate modeling is the number and nature of the random factors driving the evolution of the yield curve. This paper uses principal component analysis to examine (1) the inherent dimension of historical yield curve changes indicated by the significance of eigenvalues of the covariance matrix, (2) the practical dimension determined by a variance threshold, (3) the shape of the yield curve change associated with the first principal component, and (4) the persistence of this shape over time. We find that although the first two components explain 93% of the sample variation within a 90% confidence interval, the remaining components make statistically significant contribution to the covariance matrix. Consequently, we can establish a practical limit on the dimension only if we are willing to designate a threshold error variance. Further, our results on the persistence of the shape of the yield curve shift associated with the first component depend upon this threshold. If all components are included, the hypothesis that the shape persists between two sample time periods is rejected. On the other hand, if all but the first six components are eliminated, the hypothesis is not rejected.

Liability-driven investment: multiple liabilities and the question of the number of moments

The selection of investments held in dedicated pension or insurance asset portfolios should be liability-driven. Techniques have been developed to hedge or immunize single liabilities from the effects of a variety of yield curve changes. In this paper, we extend these results to a more relevant practical problem, to immunize multiple liabilities occurring at different times in the future. This immunization approach can accommodate a variety of non-parallel yield curve behaviours. In a practical application, we demonstrate that our approach is effective in selecting index tracking portfolios in the UK Gilt (government bond) market.

A Multi-Factor Model for the Valuation and Risk Managment of Demand Deposits

How should we value and manage deposit accounts where deposits have a zero contractual maturity, but which, in practice, remain stable through time and are remunerated below market rates? Does the economic value of the deposit account differ from the face value and can we reliably measure it? To what extent is the economic value sensitive to yield curve changes? In this paper, we try to answer the above questions. The valuation is performed on yield curve, deposit rate and deposit balance data between December 1994 and June 2005 for a sample of Belgian bank retail savings deposits accounts. We find that the deposits premium component of Belgian savings deposits is economically and statistically significant, though sensitive to assumptions about servicing costs and outstanding balances average decay rates. We also find that deposit liability values depreciate significantly when market rates increase, thereby offsetting some of the value losses on the asset side. The hedging characteristics of deposit accounts depend primarily on the nature of the underlying interest rate shock (yield curve level versus slope shock) and on the average decay rate. We assess the reliability of the reported point estimates and also report corresponding duration estimates that results from a dynamic replicating portfolio model approach more commonly used by large international banks.

DEMAND FOR SHORT TERM GOVERNMENT DEBT*

SHORT-TERM government securities are by far the major instruments transacted in the money market. The various intermediaries or institutions which make up the demand sectors for these securities have differing uses or reasons for holding them or disposing of them during varying phases or cycles of economic activity. In this study, the demand for short-term government debt is analyzed for three demand sectors: Commercial banks, mutual savings banks, and life insurance companies. Within each of these demand sectors, the analysis of demand is broken into three categories of factors which influence demand. They are: equilibrium factors, disturbance factors, and factors which measure the term structure of yields of government securities of all maturities. The significant factors within each category are determined by least squares stepwise multiple regression procedures using temporal data. Each category of factors represents a stage of regression such that the dependent demand variable in the next category or regression stage is the residual demand that was unexplained by the significant factors in the previous stage. The order in which the regressions are performed is first stage (equilibrium demand), second stage (disturbance demand), and third stage (yield influence demand). Unique to this demand analysis is the development of series of variables which measure the monthly movements in the term structure of yields on government securities over a five year time period of study. These variables which are the parameters for two different functional forms of yield curves are the independent input variables in the third stage of regression in which the influences of yield curve changes on demand is tested. The test is structured in a Yield Curve Hypothesis Model which was devised for this study. Results of the study indicate that demand for short-term government securities came about mainly from their uses as highly liquid earning assets held in place of cash during periods of cash excesses; in place of other temporarily unobtainable, more profitable earning assets; and, as a buffer against sudden losses in other earning assets. Given these and some other minor uses of short-term government securities, further changes in demand took place due to changes in the term structure of yields. These changes significantly influenced demand in both the mutual savings bank and life insurance company demand sectors, but were not significant in the commercial bank demand sector. Within the commercial bank demand sector, whose members are primarily holders of short term government debt as opposed to long term government debt, two significant equilibrium variables accounted for 7.12 per cent of total variation explained. These variables are total assets and the Federal Reserve discount rate. Four disturbance variables contributed an additional 49.61 per cent to explained variation. They are free reserves, required reserve ratio, ratio of the bill rate to the discount rate, and monthly seasonal factors. Proxy variables which measured shapes and shifts in yield curves on governments were not statistically significant within this demand sector. Mutual savings banks and life insurance companies are demand sectors whose mem-