Съдържанието не е налично на български език.
Ken Nyholm
- 21 December 2021
- WORKING PAPER SERIES - No. 2631Details
- Abstract
- This paper explores how the need to transition to a low-carbon economy influences firm credit risk. It develops a novel dataset which augments data on firms’ green-house gas emissions over time with information on climate disclosure practices and forward-looking emission reduction targets, thereby providing a rich picture of firms’ climate-related transition risk alongside their strategies to manage such risks. It then assesses how such climate-related metrics influence two key measures of firms’ credit risk: credit ratings and the market-implied distance-to-default. High emissions tend to be associated with higher credit risk. But disclosing emissions and setting a forward-looking target to cut emissions are both associated with lower credit risk, with the effect of climate commitments tending to be stronger for more ambitious targets. After the Paris agreement, firms most exposed to climate transition risk also saw their ratings deteriorate whereas other comparable firms did not, with the effect larger for European than US firms, probably reflecting differential expectations around climate policy. These results have policy implications for corporate disclosures and strategies around climate change and the treatment of the climate-related transition risk faced by the financial sector.
- JEL Code
- E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill
Q51 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Environmental Economics→Valuation of Environmental Effects
Q56 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Environmental Economics→Environment and Development, Environment and Trade, Sustainability, Environmental Accounts and Accounting, Environmental Equity, Population Growth
C58 : Mathematical and Quantitative Methods→Econometric Modeling→Financial Econometrics
- 8 July 2019
- WORKING PAPER SERIES - No. 2293Details
- Abstract
- We trace the impact of the ECB’s asset purchase programme (APP) on the sovereign yield curve. Exploiting granular information on sectoral asset holdings and ECB asset purchases, we construct a novel measure of the “free-float of duration risk” borne by price-sensitive investors. We include this supply variable in an arbitrage-free term structure model in which central bank purchases reduce the free-float of duration risk and hence compress term premia of yields. We estimate the stock of current and expected future APP holdings to reduce the 10y term premium by 95 bps. This reduction is persistent, with a half-life of five years. The expected length of the reinvestment period after APP net purchases is found to have a significant impact on term premia.
- JEL Code
- C5 : Mathematical and Quantitative Methods→Econometric Modeling
E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
- 19 March 2018
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 2, 2018Details
- Abstract
- The liquidity of euro area sovereign bond markets is important for the transmission of the ECB’s monetary policy. In particular, a high degree of liquidity fosters the link between the ECB’s monetary policy decisions, the yield curve, financial asset prices in general, and the overall cost and flow of finance in the economy. The liquidity of sovereign bond markets needs to be monitored more closely since the implementation of the ECB’s public sector purchase programme (PSPP), under which a significant share of outstanding euro area sovereign bonds has been bought. Against this background, this box presents some of the market liquidity indicators that the ECB monitors regularly. Overall, the indicators suggest that liquidity conditions in sovereign bond markets have not deteriorated since the start of the PSPP (on 9 March 2015).
- JEL Code
- E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
G14 : Financial Economics→General Financial Markets→Information and Market Efficiency, Event Studies, Insider Trading
- 17 November 2016
- WORKING PAPER SERIES - No. 1980Details
- Abstract
- Spillovers between the US and euro area term structures of interest rates are examined. Implications for monetary policy are investigated using term-structure metrics that proxy conventional and unconventional instruments, i.e. the short rate, the 10 year term premium, and the 10 year risk-free rate. A new discrete-time arbitrage-free term structure model is used to extract these variables, at a daily frequency during the period covering 2005 to 2016. Relying on forecast error variance decompositions, following Diebold and Yilmaz (2009), it is found that transatlantic spillovers have increased by approximately 11%-points during the examined period, making it more dicult for central banks to directly assess the impact of their policies.
- JEL Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
- 23 September 2015
- WORKING PAPER SERIES - No. 1851Details
- Abstract
- A factor rotation scheme is applied to the well-known Dynamic Nelson-Siegel model facilitating direct parametrization of the short rate process. The model-implied term structure of term premia is derived in closed-form, and macroeconomic variables are included in a Taylor-rule- type fashion. Four empirical experiments are performed on US data covering the period from 1990 to 2014. It is found that macroeconomic variables impact the evolution of the short rate until 2002, after which their effects become insignificant in a statistical sense. The calculated term structure of term premia is robust to the tested parameterzations, and traces out the interest rate cycles present in the data.
- JEL Code
- G1 : Financial Economics→General Financial Markets
E4 : Macroeconomics and Monetary Economics→Money and Interest Rates
C5 : Mathematical and Quantitative Methods→Econometric Modeling
- 8 June 2010
- WORKING PAPER SERIES - No. 1205Details
- Abstract
- In this paper we compare the in-sample fit and out-of-sample forecasting performance of no-arbitrage quadratic and essentially affine term structure models, as well as the dynamic Nelson-Siegel model. In total eleven model variants are evaluated, comprising five quadratic, four affine and two Nelson-Siegel models. Recursive re-estimation and out-of-sample one-, six- and twelve-months ahead forecasts are generated and evaluated using monthly US data for yields observed at maturities of 1, 6, 12, 24, 60 and 120 months. Our results indicate that quadratic models provide the best in-sample fit, while the best out-of-sample performance is generated by three-factor affine models and the dynamic Nelson-Siegel model variants. However, statistical tests fail to identify one single-best forecasting model class.
- JEL Code
- C14 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Semiparametric and Nonparametric Methods: General
C15 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Statistical Simulation Methods: General
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
- 27 February 2008
- WORKING PAPER SERIES - No. 874Details
- Abstract
- We test whether the Nelson and Siegel (1987) yield curve model is arbitrage-free in a statistical sense. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities, as shown by Bjork and Christensen (1999). 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, we find that the no-arbitrage parameters are not statistically different from those obtained from the NS model, at a 95 percent confidence level. We therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness. To corroborate this result, we show that the Nelson-Siegel model performs as well as its no-arbitrage counterpart in an out-of-sample fore-casting experiment.
- JEL Code
- C14 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Semiparametric and Nonparametric Methods: General
C15 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Statistical Simulation Methods: General
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
- 18 July 2007
- OCCASIONAL PAPER SERIES - No. 64Details
- Abstract
- This report summarises the findings of the task force. It is organised as follows. Section 2 starts with a discussion of the relevance of credit risk for central banks. It is followed by a short introduction to credit risk models, parameters and systems in Section 3, focusing on models used by members of the task force. Section 4 presents the results of the simulation exercise undertaken by the task force. The lessons from these simulations as well as other conclusions are discussed in Section 5.
- JEL Code
- E : Macroeconomics and Monetary Economics
- 26 June 2006
- WORKING PAPER SERIES - No. 641Details
- Abstract
- This paper develops a new methodology for simulating fixed-income return distributions. It is shown that a traditional factor risk model, when augmented with reference returns, is capable of generating visually consistent return distributions for a broad range of fixed income instruments such as government and nongovernment instruments in the US dollar and Japanese yen bond markets. The reference returns result from a regime-switching Nelson-Siegel yield curve model following Bernadell, Coche and Nyholm (2005). Empirical results are encouraging: simulated distributions exhibit most characteristics observed in the fixed income markets such as non-normal right-skewed distributions for short maturity instrument while instruments with longer maturity are closer to being normally distributed.
- JEL Code
- C15 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Statistical Simulation Methods: General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
G15 : Financial Economics→General Financial Markets→International Financial Markets
- 26 May 2006
- WORKING PAPER SERIES - No. 624Details
- Abstract
- This paper studies the implications of introducing an explicit policy objective to the management of foreign reserves at a central bank. A dynamic model is developed which links together reserves management and the exchange rate by foreign exchange interventions. The exchange rate is modelled as a mean-reverting autoregressive process incorporating a linear response to interventions. The premise is that it is the objective of the central bank to prevent undervaluation of its currency. Given this objective, the model is formulated in a one- and a multi-period setting and solved to find the optimal asset allocation. The results show that asset allocation can significantly help in achieving the desired policy objective.
- JEL Code
- G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
F31 : International Economics→International Finance→Foreign Exchange
- 25 April 2005
- WORKING PAPER SERIES - No. 472Details
- Abstract
- This paper presents a new framework allowing strategic investors to generate yield curve projections contingent on expectations about future macroeconomic scenarios. By consistently linking the shape and location of yield curves to the state of the economy our method generates predictions for the full yield-curve distribution under different assumptions on the future state of the economy. On the technical side, our model represents a regimeswitching expansion of Diebold and Li (2003) and hence rests on the Nelson-Siegel functional form set in state-space form. We allow transition probabilities in the regimeswitching set-up to depend on observed macroeconomic variables and thus create a link between the macro economy and the shape and location of yield curves and their time-series evolution. The model is successfully applied to US yield curve data covering the period from 1953 to 2004 and encouraging out-of-sample results are obtained, in particular at forecasting horizons longer than 24 months.
- JEL Code
- C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy