1 / 13

Non consolidated data: the role of inter-NFC loans

This presentation discusses the background and increasing user needs for non-consolidated data, the consolidation aspects in the German NFC sector, and the compilation, sources, and challenges of inter-NFC loans and trade credits. It also presents the new quarterisation and forecasting method of inter-NFC loans and concludes with an outlook for the implementation of the new method.

carolynf
Download Presentation

Non consolidated data: the role of inter-NFC loans

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Non consolidated data: the role of inter-NFC loans Taskforce on consolidated vs. non consolidated data – April 17-18, 2013 Felix Geiger, Deutsche Bundesbank

  2. Presentation Outline • Background: increasinguserneeds • Consolidationaspects in the German NFC sector • Compilationof inter-NFC loansandtradecredits • Data sourcesandchallenges • New quarterisationandforecastingmethodof inter-NFC loans: preliminaryresults • Conclusionandoutlook Felix Geiger

  3. Background: increasinguserneeds Financing and the role of inter-NFC loans Substitution of MFI loans an important mitigating factor for financial constraints restricting investment Inter-NFC loansgained relative importanceduringthe last decade, in particularduringthefinancialcrisis Felix Geiger

  4. Background: increasinguserneeds Debt definitions and intrasectoralliabilities Debt according to the MIP Scoreboard is non consolidated significantdifferencesbetweenconsolidatedand non consolidateddebt implicationsfor Alert Mechanism Report and In-Depth Reviews Felix Geiger

  5. Consolidationaspects in the German NFC sector Instruments, sourcesandfrequencyofprimarydata Felix Geiger

  6. Compilationof inter-NFC loansandtradevredits: datasources Inter-NCF loans in Germany • Make up a significant part of the difference between consolidated and non consolidated debt • Data sources: main aspects • Companies’ balance sheet statistics • annual information on total inter-NFC loans and trade credits (final coverage 100% with grossing-up method based on turnover) • timeliness of primary data: up to t+14 months (March 2013: data for 2011) • I.I.P. • quarterly information on assets and liabilities of enterprises • delineation of NFC counterparts to derive figures of non-domestic inter-NFC loans and trade credits • timeliness of primary data: up to t+3 months Necessity for quarterisation and forecasting (up to 7 quarters) Felix Geiger

  7. Compilationof inter-NFC loans: method Current compilation • Quarterly growth rate ≈ annual growth rate / 4 + random factor • Forecast: extrapolation of dynamics according to last year’s developments Major disadvantages • Compilation purely based on statistical techniques • No consideration of additional (quarterly) information • Danger of over-/underestimation of (recent) quarterly developments Main aim of new compilation Compile reliable data of high quality which reflect the development both during the year and during recent quarters according to economic fundamentals Increasinguserneeds Felix Geiger

  8. Compilationof inter-NFC loansandtradecredits: method New compilation method: key elements • Approach: related to factor analysis • Idea: replicate annual primary data using quarterly data Quarterisation and forecasting at once • Requirements for quarterly data: • always available on time • reliable and of high quality • strong and robust correlation from an economic viewpoint Main challenges • Preferably long time series ( use of commercial data) • Avoid one-way dependency ( consideration of several variables) ! Research based variable selection Felix Geiger

  9. Compilationof inter-NFC loansandtradecredits: method Basic pattern of new compilation: ΔAnnual inter-NFC loans = X% * Var1 + Y% * Var2 + … + Z% * Var3 Set weights (X, Y, Z) accordingly via grid search routine in order to maximize correlation Variables considered: • Flows: RoA (group), Δ RoA, sales, EBITDA, gross operating surplus… • Stocks: short / long term debt, Δ debt, liquid / total assets, trade credits... • Overall: • about 15 variables • years covered: 1999-2011 Felix Geiger

  10. Compilationof inter-NFC loansandtradecredits: quarterisation Felix Geiger

  11. Compilationof inter-NFC loansandtradecredits: forecasting Felix Geiger

  12. Conclusionandoutlook Background User needs increase  need for data of high quality and reliability Main issue for inter-NFC loans Insufficient primary sources require quarterisation and forecasting of inter-NFC loans Solution: the Bundesbank approach considers economic developments rather than statistical techniques only idea: replicate annual primary data using related quarterly data Outlook Implementation of new method envisaged for April 2013 ( revisions) Implementation of consolidation aspects for F3 and F5 with ESA 2010 Felix Geiger

  13. Thank you for your attention Taskforce on consolidated vs. non consolidated data – April 17-18, 2013 Felix Geiger, Deutsche Bundesbank

More Related