This thesis focus on a yield curve modelling. It estimates unobserved "global" yield curve factors which drives changes in individual real yield curves. Yield curves of USD, GBP, JPY and EUR are considered and global factors are able to explain substantial part of their variances. The method is built on the Nelson-Siegel model which is implemented in a state-space form to be able to extract the unobserved yield factors. The estimated global yield factors are further used for explaining the evolution of the Czech yield curve. Their impact to the Czech yield curve is estimated in a time-varying regression which results show that the impact of the global factors is stronger during the years of the interventions of the Czech National Bank and thus suggests that the interventions help to transmit the global low interest rates to the Czech economy.

The thesis is focused on finding the most appropriate method for constructing the yield curve which will meet the criteria of Solvency II and also the selected evaluation criteria. An overview of advantages of each method is obtained by comparing these methods. Yield curves are constructed using the Czech interest rate swap data from 2007 to 2013. The selection of the evaluated methods respects their public availability and their practical application in life insurance or central banks. This thesis is divided into two parts. The first part describes the theoretical background which is necessary to understand the examined issues. In the second part the analysis of selected methods was carried out with detailed evaluation.

Financial institutions are exposed to several different types of risk. One of the risks that can have a significant impact is the interest rate risk in the bank book (IRRBB). In 2018, the European Banking Authority (EBA) released a regulation on IRRBB to ensure that institutions make adequate risk calculations. This article proposes an IRRBB model that follows EBA's regulations. Among other things, this framework contains a deterministic stress test of the risk-free yield curve, in addition to this, two different types of stochastic stress tests of the yield curve were made. The results show that the deterministic stress tests give the highest risk, but that the outcomes are considered less likely to occur compared to the outcomes generated by the stochastic models. It is also demonstrated that EBA's proposal for a stress model could be better adapted to the low interest rate environment that we experience now. Furthermore, a discussion is held on the need for a more standardized framework to clarify, both for the institutions themselves and the supervisory authorities, the risks that institutes are exposed to. / Finansiella institutioner är exponerade mot flera olika typer av risker. En av de risker som kan ha en stor påverkan är ränterisk i bankboken (IRRBB). 2018 släppte European Banking Authority (EBA) ett regelverk gällande IRRBB som ska se till att institutioner gör tillräckliga riskberäkningar. Detta papper föreslår en IRRBB modell som följer EBAs regelverk. Detta regelverk innehåller bland annat ett deterministiskt stresstest av den riskfria avkastningskurvan, utöver detta så gjordes två olika typer av stokastiska stresstest av avkastningskurvan. Resultatet visar att de deterministiska stresstesten ger högst riskutslag men att utfallen anses vara mindre sannolika att inträffa jämfört med utfallen som de stokastiska modellera genererade. Det påvisas även att EBAs förslag på stressmodell skulle kunna anpassas bättre mot den lågräntemiljö som vi för tillfället befinner oss i. Vidare förs en diskussion gällande ett behov av ett mer standardiserat ramverk för att tydliggöra, både för institutioner själva och samt övervakande myndigheter, vilka risker institutioner utsätts för.

This thesis investigates the modelling and forecasting of multivariate volatility and dependence in financial time series. The first paper proposes a new model for forecasting changes in the term structure (TS) of interest rates. Using the level, slope and curvature factors of the dynamic Nelson-Siegel model, we build a time-varying copula model for the factor dynamics allowing for departure from the normality assumption typically adopted in TS models. To induce relative immunity to structural breaks, we model and forecast the factor changes and not the factor levels. Using US Treasury yields for the period 1986:3-2010:12, our in-sample analysis indicates model stability and we show statistically significant gains due to allowing for a time-varying dependence structure which permits joint extreme factor movements. Our out-of-sample analysis indicates the model's superior ability to forecast the conditional mean in terms of root mean square error reductions and directional forecast accuracy. The forecast gains are stronger during the recent financial crisis. We also conduct out-of-sample model evaluation based on conditional density forecasts. The second paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences from multivariate GARCH models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations. The third paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting. The key idea is to rotate the returns and then fit them using a BEKK model for the conditional covariance with the identity matrix as the covariance target. The extension to DCC type models is given, enriching this class. We focus primarily on diagonal BEKK and DCC models, and a related parameterisation which imposes common persistence on all elements of the conditional covariance matrix. Inference for these models is computationally attractive, and the asymptotics is standard. The techniques are illustrated using recent data on the S&P 500 ETF and some DJIA stocks, including comparisons to the related orthogonal GARCH models.

This doctoral thesis is a collection of four papers on the analysis of the term structure of interest rates with a focus at the intersection of macroeconomics and finance. "Risk in Macroeconomic Fundamentals and Bond Return Predictability" documents that factors related to risks underlying the macroeconomy such as expectations, uncertainty and downside (upside) macroeconomic risks are able to explain variation in bond risk premia. The information provided is found to be, to a large extent, unrelated to that contained in forward rates and current macroeconomic conditions. "Out-of-sample bond excess returns predictability" provides evidence that macroeconomic variables, risks in macroeconomic outcomes as well as the combination of these different sources of information are able to generate statistical as well as economic bond excess returns predictability in an out-of-sample setting. Results suggest that this finding is not driven by revisions in macroeconomic data. The term spread (yield curve slope) is largely used as an indicator of future economic activity. "Re-examining the predictive power of the yield curve with quantile regression" provides new evidence on the predictive ability of the term spread by studying the whole conditional distribution of GDP growth. "Modeling and forecasting the yield curve by extended Nelson-Siegel class of models: a quantile regression approach" deals with yield curve prediction. More flexible Nelson-Siegel models are found to provide better fitting to the data, even when penalizing for additional model complexity. For the forecasting exercise, quantile-based models are found to overcome all competitors. / <p>Diss. Stockholm :  Stockholm School of Economics, 2014. Introduction together with 4 papers.</p>

Submitted by Andre Akamine (andre_akamine@yahoo.com.br) on 2014-02-25T19:51:22Z No. of bitstreams: 1 dissertacao-andre_akamine -versao final.pdf: 1090370 bytes, checksum: 2cc992eb83cbdbf42457a77eeb02dffa (MD5) / Approved for entry into archive by Vera Lúcia Mourão (vera.mourao@fgv.br) on 2014-02-25T20:48:28Z (GMT) No. of bitstreams: 1 dissertacao-andre_akamine -versao final.pdf: 1090370 bytes, checksum: 2cc992eb83cbdbf42457a77eeb02dffa (MD5) / Made available in DSpace on 2014-02-26T12:20:22Z (GMT). No. of bitstreams: 1 dissertacao-andre_akamine -versao final.pdf: 1090370 bytes, checksum: 2cc992eb83cbdbf42457a77eeb02dffa (MD5) Previous issue date: 2014-01-29 / Com o objetivo de analisar o impacto na Estrutura a Termos de Volatilidade (ETV) das taxas de juros utilizando dois diferentes modelos na estimação da Estrutura a Termo das Taxas de Juros (ETTJ) e a suposição em relação a estrutura heterocedástica dos erros (MQO e MQG ponderado pela duration), a técnica procede em estimar a ETV utilizando-se da volatilidade histórica por desvio padrão e pelo modelo auto-regressivo Exponentially Weighted Moving Average (EWMA). Por meio do teste de backtesting proposto por Kupiec para o VaR paramétrico obtido com as volatilidades das ETV´s estimadas, concluí-se que há uma grande diferença na aderência que dependem da combinação dos modelos utilizados para as ETV´s. Além disso, há diferenças estatisticamente significantes entre as ETV´s estimadas em todo os pontos da curva, particularmente maiores no curto prazo (até 1 ano) e nos prazos mais longos (acima de 10 anos). / For the purpose of analyzing the impact in Volatility Term Structure (VTS) of interest rate using two different models in the estimation of the Term Structure of Interest Rates (TSIR) and the assumption regarding the heterocedastic structure of errors (OLS and GLS weighted by duration), the technique proceeds in estimating the VTS using the historical volatility by the standard deviation and autoregressive model Exponentially Weighted Moving Average (EWMA). Through the backtesting test proposed by Kupiec for parametric VaR obtained with the volatilities of VTS’s estimate, conclude that there is a big difference in adherence that depend on the combination of the models used for VTS’s. In addition, there is statistically significant differences between the VTS’s estimated around the points of the curve, specially higher in the short term (less than 1 year) and long term (over 10 years).

This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates. <p><p>The first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models.<p><p>The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons.<p><p>The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

The work deals with the theoretical basis and the practical approach for determining the risk-free rate of return. The aim of the work is to form recommendations which should analysts follow in determining the risk-free rate of return in the Czech Republic. The first part focuses on theoretical basis of risk-free rate of return and market interest rates. Further, the criteria of risk-free investments are defined in this chapter. The second and third part focuses on determination of the risk-free rate of return using yield to maturity of government bond and yield curve which was derived with using the Nelson-Siegel model. The table of forward rates at the end of each month from January 1999 to April 2010 is attached.