Fahd of Saudi Arabia

Investor heterogeneity and large-scale asset purchases

Retrieved on: 
화요일, 5월 28, 2024

We quantify both the direct and the portfolio re-balancing impact,

Key Points: 
    • We quantify both the direct and the portfolio re-balancing impact,
      emphasizing the role of investor heterogeneity.
    • By purchasing large quantities of assets, the central banks aim to affect asset prices
      throughout the economy.
    • In this paper, we set out to deal with these challenges and to quantify both the direct
      and indirect effects of large-scale asset purchases.
    • In this paper, we set out to deal with these challenges and to quantify both the direct
      and indirect effects of large-scale asset purchases.
    • In the first part of the paper, we assess the importance of investor heterogeneity for
      the direct effects of central bank purchases.
    • Large-scale asset purchases are isomorphic to
      negative changes in supply, as they effectively reduce the supply of bonds available to private
      investors in the market.
    • We weigh the estimates by the portfolio composition (number of
      securities held) of the respective asset types for the average fund.
    • ECB Working Paper Series No 2938

      5

      price of one security depends on the slope of the demand curve for that asset.

    • However, in our setting, supply changes are not
      necessarily randomly allocated, as purchases could be correlated with asset characteristics.
    • To address this challenge, we isolate a random component of purchases and, with that,
      estimate the price elasticity of each type of investor.
    • For each investor type, we regress
      the security holdings on that security?s yield, instrumenting the yield with ECB purchases.
    • Given the estimated investor elasticities, we construct a security-level measure of the
      price elasticity of the investor base.
    • To address this concern, we exploit quasi-exogenous cross-sectional variation in
      investor base composition across securities, measuring the share held by each investor type
      prior to the announcement and implementation of asset purchases.
    • With this measure, we can finally test how investor composition affects the direct impact of
      central bank purchases.
    • In
      this part of the analysis, we focus on mutual funds, one investor type we estimated to be
      elastic.
    • This insight can help assess the calibration and targeting of asset purchase programs by central banks based on their objective
      function.
    • This paper relates to several strands of literature on unconventional monetary policy, intermediary and demand-system asset pricing.
    • First, we relate to the literature on large-scale asset purchases in the Euro Area, studying announcement effects of purchases (Andrade et al.
    • We contribute to
      this literature by estimating spillover effects on assets that are not eligible for central bank
      purchases while emphasizing the role of investor heterogeneity and developing a novel identification methodology.
    • Our contribution to this literature is to analyze the role of investors?
      preferences for the propagation of large scale asset purchases.
    • Then the aggregate demand curve for the asset is
      X
      k

      ?k x k =

      X

      ?k (?k ? p?k )

      k

      where ?k is the share of investor k in the market.

    • P
      By adding market clearing (S = k ?k xk ) we can derive that the price of the asset takes
      the following form:
      P
      ?k ?k
      S
      p = Pk
      ?P
      .
    • We denote with Dt = [dt,k , dt,k? , ...dt,K ] the N xK matrix of
      demand of each investor type for each security.
    • 4

      Direct effects and investor heterogeneity

      In this section we study the following research question: do effects of asset purchases depend
      on investor composition?

    • In order to answer this question, we test how purchases affect yields and how these effects
      vary as investor composition changes.
    • Third, we measure investor composition by exploiting
      quasi-random variation in ex-ante investor composition.
    • In the next section we will investigate how this
      security-level heterogeneity in investor composition matters for the effects of asset purchases.
    • 4.2

      Demand elasticities of investors

      In this section we investigate how different investor types react to Large Scale Asset Purchases
      (LSAPs).

    • In section 4 we estimated heterogeneity in elasticity across investor types based on how
      price-elastic they were to ECB purchases.
    • These results are in line with Table 3,
      where we estimate heterogeneity in elasticity across investor types.
    • To measure investor composition base heterogeneity we construct

      ECB Working Paper Series No 2938

      38

      security-level Weighted elasticity as we did in section 4.

    • Weighted Elasticity: Bn,t?1 =

      X

      ??n,k,t?1 ?k

      (8)

      k

      where ??n,k,t?1 is the share of the asset n held by investor k at t-1 and ?k are the elasticities
      of investor types estimated in section 4.

    • 7

      Conclusion

      In this paper, we examine the role of investor heterogeneity for both direct and indirect
      effects of central bank purchases.

    • Our findings show substantial heterogeneity in
      exposure to large-scale asset purchases across mutual funds.

AnaCredit plausibility checks, version 2.0

Retrieved on: 
화요일, 4월 2, 2024

AnaCredit plausibility

Key Points: 
    • AnaCredit plausibility
      checks
      Plausibility checks performed on
      AnaCredit datasets
      Version 2.0

      March 2024

      Contents
      1

      Introduction

      2

      2

      Plausibility checks

      3

      2.1

      Definitions

      3

      2.2

      Classification

      4

      3

      AnaCredit external plausibility checks

      7

      3.1

      Plausibility checks with other statistical reporting frameworks

      8

      3.2

      Plausibility checks with supervisory reporting frameworks

      AnaCredit plausibility checks ? Contents

      27

      1

      1

      Introduction
      This document sets out the AnaCredit plausibility checks.

    • AnaCredit plausibility checks ? Plausibility checks

      3

      are erroneous and require revision; second, where the AnaCredit data are correct
      but the BSI data have not been reported correctly; third, where methodological
      differences in the requirements of the two datasets justify the discrepancy.

    • Figure 1
      Types of AnaCredit plausibility check
      Structure

      Stability

      per OA

      Consistency within or across attributes

      Time consistency of aggregate metrics

      across OAs

      Consistency with data of other OAs

      Changes in relative position compared
      to other OAs

      Benchmark
      comparisons

      Consistency with statistical and/or
      supervisory reporting

      Consistency of ratios over time

      Internal
      plausibility

      External
      plausibility

      AnaCredit plausibility checks ? Plausibility checks

      4

      2.2.1

      Internal plausibility checks
      Internal plausibility checks are self-contained within the AnaCredit data set, i.e.

    • AnaCredit plausibility checks ? Plausibility checks

      5

      2.2.2

      External plausibility checks
      External plausibility checks assess the consistency of data reported under AnaCredit
      with other datasets.

    • AnaCredit plausibility checks ? Plausibility checks

      6

      3

      AnaCredit external plausibility checks
      The following subsections contain the details of the AnaCredit external plausibility
      checks.

    • List of external plausibility checks performed under AnaCredit
      Table 1 shows the external plausibility checks under AnaCredit.
    • Plausibility checks with other statistical reporting
      frameworks
      This section includes AnaCredit external plausibility checks against other statistical
      reporting frameworks.
    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      9

      3.1.1.2

      ?

      loans to other financial intermediaries, financial auxiliaries, captive financial
      institutions and money lenders (S.125+S.126+S.127) across all maturity
      breakdowns;

      ?

      loans to insurance corporations (S.128) across all maturity breakdowns;

      ?

      loans to pension funds (S.129) across all maturity breakdown.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      10

      instruments (loans), so the resulting aggregate is a good match to the BSI statistic.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      12

      loans.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      14

      If any of the input data necessary for this calculation are missing or inconsistent, the
      [relevant BSI balance] resolves to NULL for the instrument concerned.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      16

      the latter assuming the credit risk and the MFI being responsible for managing the
      loan.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      17

      Intra-company instrument flag
      BSI statistics also include intra-company loans, i.e.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      18

      Settled loans
      BSI statistics only include loans which have been settled, i.e.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      20

      resolves to NULL for the instrument concerned.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      25

      divided by the number of the main debtors.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      26

      3.2

      Plausibility checks with supervisory reporting frameworks
      This section includes AnaCredit external plausibility checks against supervisory
      reporting frameworks.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      27

      FINREP templates and compared with suitably computed AnaCredit equivalents for
      banks reporting the supervisory financial information under Regulation ECB/2015/13
      (FINREP solo).

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      28

      Figure 3
      Calculation flow ? schematic overview of the comparison with FINREP solo

      By stacking the FINREP solo benchmark side-by-side with its AnaCredit equivalent,
      the deviation between the values can be quantified.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      29

      3.2.2.1

      FINREP solo benchmark value
      As mentioned, comparing AnaCredit with supervisory financial information helps
      ensure accounting information on loan portfolios that must be reported to AnaCredit
      is reported properly.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      30

      Table 3
      The formula for the benchmark DP_FNRP_F1800_ALL_00 from data points from the
      reporting templates of the EBA reporting framework.

    • The composition of FINREP solo reporters thus defined serves as a basis for
      determining (i) which AnaCredit observed agents correspond to which FINREP solo
      reporters, and (ii) the extent to which the perimeter of a FINREP solo reporter can be
      reconstructed from AnaCredit (given that some observed agents may have been

      AnaCredit plausibility checks ? AnaCredit external plausibility checks

      32

      derogated from reporting to AnaCredit).

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      34

      For a given FINREP solo reporter, the result of the calculation described in this
      section (i.e.

    • AnaCredit plausibility checks ? AnaCredit external plausibility checks

      35

      ? European Central Bank, 2024
      Postal address
      Telephone
      Website

      60640 Frankfurt am Main, Germany
      +49 69 1344 0
      www.ecb.europa.eu

      All rights reserved.

East Africa's Digital Dawn: ClickPesa Debt Fund and Pendulum enter into funding and technology partnership for Tanzania's Microfinance Evolution

Retrieved on: 
화요일, 11월 21, 2023

BERLIN, Nov. 21, 2023 /PRNewswire/ -- ClickPesa and Pendulum have teamed up in a technology partnership, leveraging the possibilities of decentralized finance (DeFi).

Key Points: 
  • BERLIN, Nov. 21, 2023 /PRNewswire/ -- ClickPesa and Pendulum have teamed up in a technology partnership, leveraging the possibilities of decentralized finance (DeFi).
  • Through the fusion of ClickPesa's proficiency in digital financial solutions and extensive networks in East Africa, and Pendulum's cutting-edge Web3 technology based on Polkadot, this partnership is set to equip Microfinance Institutions (MFIs) with essential tools and resources to access funding through the ClickPesa Debt Fund.
  • "We are thrilled to announce this strategic partnership with Pendulum," said Richard Lema, COO of ClickPesa, which runs the ClickPesa Debt Fund.
  • "Pendulum is excited to collaborate with the ClickPesa Debt Fund in their mission to support SMEs and women-owned businesses in Tanzania through MFIs," said Daniel Kisluk, CMO & Co-Founder at Pendulum.

East Africa's Digital Dawn: ClickPesa Debt Fund and Pendulum enter into funding and technology partnership for Tanzania's Microfinance Evolution

Retrieved on: 
화요일, 11월 21, 2023

BERLIN, Nov. 21, 2023 /PRNewswire/ -- ClickPesa and Pendulum have teamed up in a technology partnership, leveraging the possibilities of decentralized finance (DeFi).

Key Points: 
  • BERLIN, Nov. 21, 2023 /PRNewswire/ -- ClickPesa and Pendulum have teamed up in a technology partnership, leveraging the possibilities of decentralized finance (DeFi).
  • Through the fusion of ClickPesa's proficiency in digital financial solutions and extensive networks in East Africa, and Pendulum's cutting-edge Web3 technology based on Polkadot, this partnership is set to equip Microfinance Institutions (MFIs) with essential tools and resources to access funding through the ClickPesa Debt Fund.
  • "We are thrilled to announce this strategic partnership with Pendulum," said Richard Lema, COO of ClickPesa, which runs the ClickPesa Debt Fund.
  • "Pendulum is excited to collaborate with the ClickPesa Debt Fund in their mission to support SMEs and women-owned businesses in Tanzania through MFIs," said Daniel Kisluk, CMO & Co-Founder at Pendulum.

Global Microinsurance Market 2023-2031: Set for a More Inclusive and Economically Resilient Future - ResearchAndMarkets.com

Retrieved on: 
수요일, 8월 30, 2023

As the global financial ecosystem evolves, microinsurance emerges as a pivotal tool for bridging gaps and ensuring financial inclusion.

Key Points: 
  • As the global financial ecosystem evolves, microinsurance emerges as a pivotal tool for bridging gaps and ensuring financial inclusion.
  • Microinsurance market revenue showcases a consistent upward trajectory, reflecting the growing recognition and adoption of microinsurance products across the world.
  • Asia-Pacific shines as a leader in the microinsurance market, driven by the region's large population and significant untapped market potential.
  • With innovation, partnerships, and regulatory support, microinsurance sets the stage for a more inclusive and economically resilient future.

Global financial inclusion initiative expands to collect over 1 million unique data points in 2023

Retrieved on: 
화요일, 6월 6, 2023

LONDON, June 6, 2023 /PRNewswire/ -- 60 Decibels, a global impact measurement company, is excited to announce the 2023 Microfinance Index (MFI Index), gathering over 1 million unique data points. The Microfinance Index is a groundbreaking financial inclusion initiative that provides high-quality, comparable impact data for the microfinance industry, driven entirely by client voices.

Key Points: 
  • The Microfinance Index is a groundbreaking financial inclusion initiative that provides high-quality, comparable impact data for the microfinance industry, driven entirely by client voices.
  • The Index is designed to complement and integrate with existing frameworks and standards in microfinance, impact investing, and international development.
  • The 2023 MFI Index provides data aligned to IRIS+ metrics and the five dimensions of impact guidance established by the Impact Management Project.
  • To learn more and sign up to receive the 2023 Microfinance Index, please visit https://60decibels.com/mfi-index/

Global financial inclusion initiative expands to collect over 1 million unique data points in 2023

Retrieved on: 
화요일, 6월 6, 2023

LONDON, June 6, 2023 /PRNewswire/ -- 60 Decibels, a global impact measurement company, is excited to announce the 2023 Microfinance Index (MFI Index), gathering over 1 million unique data points. The Microfinance Index is a groundbreaking financial inclusion initiative that provides high-quality, comparable impact data for the microfinance industry, driven entirely by client voices.

Key Points: 
  • The Microfinance Index is a groundbreaking financial inclusion initiative that provides high-quality, comparable impact data for the microfinance industry, driven entirely by client voices.
  • The Index is designed to complement and integrate with existing frameworks and standards in microfinance, impact investing, and international development.
  • The 2023 MFI Index provides data aligned to IRIS+ metrics and the five dimensions of impact guidance established by the Impact Management Project.
  • To learn more and sign up to receive the 2023 Microfinance Index, please visit https://60decibels.com/mfi-index/

Micro Lending Market Size, Share & Trends Analysis Report 2022: An $86.82 Billion Market by 2030 - Growing demand through Peer to Peer Lending Platforms - ResearchAndMarkets.com

Retrieved on: 
금요일, 12월 16, 2022

The "Micro Lending Market Size, Share & Trends Analysis Report by Service Provider (Banks, Micro Finance Institutes (MFIs)), by End-user (Solo Entrepreneurs & Individuals, Micro, Small & Medium Enterprises), by Region, and Segment Forecasts, 2022-2030" report has been added to ResearchAndMarkets.com's offering.

Key Points: 
  • The "Micro Lending Market Size, Share & Trends Analysis Report by Service Provider (Banks, Micro Finance Institutes (MFIs)), by End-user (Solo Entrepreneurs & Individuals, Micro, Small & Medium Enterprises), by Region, and Segment Forecasts, 2022-2030" report has been added to ResearchAndMarkets.com's offering.
  • Furthermore, the growing demand for micro lending by individuals through peer-to-peer lending platforms is another major factor driving the market's growth.
  • Furthermore, the initiatives undertaken by the government to offer support to various financial enterprises and grants to NGOs bode well for the growth of the micro lending industry.
  • Hence, such digital trends in the market are expected to create growth opportunities for the industry over the forecast period.