Nėra lietuvių kalba
Josep Maria Puigvert Gutiérrez
- 30 September 2013
- STATISTICS PAPER SERIES - No. 3Details
- Abstract
- Traditional literature on sampling techniques focuses mainly on statistical samples and covers non-random (non-statistical) samples only marginally. Nevertheless, there has been a recent revival of interest in non-statistical samples, given their widespread use in certain fields like government surveys and marketing research, or for audit purposes. This paper attempts to set up common rules for non-statistical samples in which only data on the largest institutions within each stratum are collected. This is done by focusing on the statistics compiled by the European System of Central Banks (ESCB) on the interest rates of monetary financial institutions (MFIs) in countries of the European Union. The paper concludes by proposing a way of establishing common rules for non-statistical samples based on a synthetic measurement of a mean of absolute errors.
- JEL Code
- C42 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Survey Methods
E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects
Annexes- 2 December 2013
- ANNEX
- 28 October 2011
- WORKING PAPER SERIES - No. 1391Details
- Abstract
- This paper analyses changes in short-term interest rate expectations and uncertainty during ECB Governing Council days. For this purpose, it first extends the estimation of risk-neutral probability density functions up to tick frequency. In particular, the non-parametric estimator of these densities, which is based on fitting implied volatility curves, is applied to estimate intraday expectations of threemonth EURIBOR three months ahead. The estimator proves to be robust to market microstructure noise and able to capture meaningful changes in expectations. Estimates of the noise impact on the statistical moments of the densities further enhance the interpretation. In addition, the paper assesses the impact of the ECB communication during Governing Council days. The results show that the whole density may react to the communication and that such repositioning of market participants
- JEL Code
- C14 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Semiparametric and Nonparametric Methods: General
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
E61 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Policy Objectives, Policy Designs and Consistency, Policy Coordination
- 23 December 2010
- WORKING PAPER SERIES - No. 1281Details
- Abstract
- This paper presents a set of probability density functions for Euribor outturns in three months’ time, estimated from the prices of options on Euribor futures. It is the first official and freely available dataset to span the complete history of Euribor futures options, thus comprising over ten years of daily data, from 13 January 1999 onwards. Time series of the statistical moments of these option-implied probability density functions are documented until April 2010. Particular attention is given to how these probability density functions, and their associated summary statistics, reacted to the unfolding financial crisis between 2007 and 2009. In doing so, it shows how option-implied probability density functions could be used to contribute to monetary policy and financial stability analysis.
- JEL Code
- C13 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Estimation: General
C14 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Semiparametric and Nonparametric Methods: General
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
G13 : Financial Economics→General Financial Markets→Contingent Pricing, Futures Pricing
- 14 October 2008
- WORKING PAPER SERIES - No. 948Details
- Abstract
- In this study we combine clustering techniques with a moving window algorithm in order to filter financial market data outliers. We apply the algorithm to a set of financial market data which consists of 25 series selected from a larger dataset using a cluster analysis technique taking into account the daily behaviour of the market; each of these series is an element of a cluster that represents a different segment of the market. We set up a framework of possible algorithm parameter combinations that detect most of the outliers by market segment. In addition, the algorithm parameters that have been found can also be used to detect outliers in other series with similar economic behaviour in the same cluster. Moreover, the crosschecking of the behaviour of different series within each cluster reduces the possibility of observations being misclassified as outliers.
- JEL Code
- C19 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Other
C49 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Other
G19 : Financial Economics→General Financial Markets→Other
- 26 May 2006
- WORKING PAPER SERIES - No. 627Details
- Abstract
- In this study we apply cluster analysis techniques, including a novel smoothing method, to detect some basic patterns and trends in the euro area banking sector in terms of the degree of homogeneity of countries. We find that in the period 1998-2004 the banking sectors in the euro area countries seem to have become somewhat more homogeneous, although the results are not unequivocal and considerable differences remain, leaving scope for further integration. In terms of clustering, the Western and Central European countries (like Germany, France, Belgium, and to some extent also the Netherlands, Austria and Italy) tend to cluster together, while Spain and Portugal and more recently also Greece usually are in the same distinct cluster. Ireland and Finland form separate clusters, but overall tend to be closer to the Western and Central European cluster.
- JEL Code
- C49 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Other
F36 : International Economics→International Finance→Financial Aspects of Economic Integration
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages