The purpose of this thesis is to enrich the debate on the effects of the shift of policy interest rates into negative territory. We analyze how the implementation of negative interest rate policy (NIRP) impacts banks. As the literature on this topic is limited (but burgeoning), this thesis aims to contribute to it in two ways: (i) In the first part, we identify the effects of NIRP on banks' net interest margins and credit supply; (ii) the second part analyzes the different channels of banks' responses (including risk-taking incentives) to the introduction of negative interest rates. The first chapter shows that NIRP have reduced banks' net interest margins (NIM). It is also observed that the compression of NIM stems from the reluctance of banks to reduce or even charge a negative interest rate on savers' deposits. The results of the second chapter highlight that banks affected by NIRP adjusted their lending behavior by increasing the volume of credit and prioritizing loans with longer maturities. In addition, the third chapter assesses the influence of the NIRP-related reduction in net interest margins on banks' risk taking. Our results indicate that despite the reduction in NIM, banks did not have an incentive to take more risk. Finally, the fourth section, suggests that the decrease in interest income due to negative interest rates was only partially mitigated by an increase in non-interest income. Our results also highlight that banks' responses are not instantaneous and that they adjust them as negative interest rates persist over time. ; Cette thèse a pour but d'enrichir le débat sur les effets du passage des taux d'intérêt directeurs en territoire négatif. Nous analysons la manière dont l'implémentation des taux d'intérêt directeurs négatifs (TIDN) impacte les banques. La littérature sur ce sujet étant limitée (mais en pleine expansion), cette thèse vise à contribuer à celle-ci de deux manières : (i) Dans une première partie, nous identifions les effets de TIDN sur les marges nettes d'intérêt des banques ...
"This book sheds new light on a recently introduced monetary tool - negative interest rates policy (NIRP). It provides in-depth insight into this phenomenon, conducted by the central banks in several economies, for example, the Eurozone, Switzerland and Japan, and its possible impact on systemic risk. Although it has been introduced as a temporary policy instrument, it may remain widely used for a longer period and by a greater range of central banks than initially expected, thus the book explores its effects and implications on the banking sector and financial markets, with a particular focus on potentially adverse consequences. There is a strong accent on the uniqueness of negative policy rates in the context of financial stability concerns. The authors assess whether NIRP has any - or in principle a stronger - impact on systemic risk than conventional monetary policy. The book is targeted at presenting and evaluating the initial experiences of NIRP policy during normal, i.e. pre-COVID, times, rather than in periods in which pre-established macroeconomic relations are rapidly disrupted or, specifically, when the source of the disruption is not purely economic in nature, unlike in systemic crisis. The authors adopt both theoretical and practical approaches to explore the key issues and outline the policy implications for both monetary and macroprudential authorities, with respect to negative interest rate policy, thus the book will provide a useful guide for policymakers, academics, advanced students and researchers of financial economics and international finance"--
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence ; A maior herança da Grande Recessão (crise financeira de 2007/08 e crise das hipotecas subprime dos EUA de 2007/09) é definitivamente a queda da indústria bancária e a incapacidade dos países de reembolsar a sua dívida soberana e aumentar o seu PIB. As ligações são inegáveis e os Bancos Centrais foram responsáveis por uma resposta rápida para reverter essa queda a pique. Esta dissertação pretende analisar o efeito de taxas de juro baixas acrescido de uma política específica adotada pelo Banco Central Europeu (BCE), a saber, a Política de Taxas de Juros Negativos (Negative Interest Rate Policy - NIRP) na rentabilidade dos bancos em Portugal. Em essência, o principal objetivo desta dissertação é entender como a Política de Taxas de Juros Negativas moldaram o setor bancário em Portugal. Identificamos e analisámos os cinco principais canais pelos quais o NIRP impacta a rentabilidade dos bancos, nomeadamente o Canal de Taxa de Juros, o Canal de Crédito, o Canal de Carteira de Ativos, o Canal de Reflação e o Canal de Câmbio. Utilizámos modelos de Regressão Linear Múltipla combinados com uma Regressão Stepwise para identificar as variáveis mais significativas na explicação da rentabilidade e desempenho dos bancos. Este método é comumente usado em estudos similares. Considerámos múltiplas variáveis explicativas, incluindo taxas de juro diretoras do BCE (taxas de facilidade permanente de depósito e de facilidade permanente de cedência marginal de liquidez), taxas de juros do mercado monetário interbancário, variáveis específico do setor financeiro (por exemplo, rácio custo / rendimento, rácio Crédito / Depósito) e variáveis macroeconómicas (Crescimento real do PIB, taxa de desemprego). Recorremos a dados publicamente disponíveis, para 35 bancos diferentes, de 2010 a 2017, fornecidos pela Associação Portuguesa de Bancos (APB), pelo Banco de Portugal (BdP), pelo BCE e pelo Instituto Europeu para os Mercados Monetários (EMMI). Durante este período, os bancos portugueses fizeram algumas mudanças nas suas estratégias de negócio, aumentando o foco nas comissões e comissões de serviço e maiores retornos da gestão de carteiras. Depois de executar os modelos e analisar os resultados, podemos concluir que quando o BCE decidiu utilizar o NIRP, como forma de recuperar a economia europeia, os canais que mais afetaram a rentabilidade do banco português foram o Canal de Taxa de Juro, o Canal de Crédito e o Canal de Carteira de Ativos. ; The aftermath of the Great Recession (financial crisis of 2007/08 and U.S. subprime mortgage crisis of 2007/09) and the Euro Zone Sovereign Debt Crisis is definitely the fall of the Banking industry and the countries incapability of repaying their debts. The world economy suffered a major setback and Governments and Central Banks had to provide actions to regain the financial strength they once had. A quick response was demanded in order to reverse this tsunami of downfalls that jeopardized the economical actors. This paper intends to analyse the effects of negative interest rates plus a specific policy adopted by the European Central Bank (ECB), namely the Negative Interest Rates Policy (NIRP), on banks' profitability in Portugal. We identified and analysed the five main channels by which NIRP impacts on banks' profitability, namely the Interest Rate Channel, the Credit Channel, the Portfolio Channel, the Reflation Channel and the Exchange Rate Channel. We used Multiple Linear Regression models combined with a Stepwise Regression to identify the most significant variables in explaining bank's profitability and performance. This method is commonly used in similar related studies. We considered multiple explanatory variables, including ECB key interest rates (deposit and facility rates), Interbank Money Market Interest Rates, Bank Specific covariates (e.g., Cost-to-Income ratio, Loan-to-Deposit ratio) and macroeconomic variables (e.g., real GDP Growth, unemployment rate). We use publicly available data for 35 different banks from 2010 to 2017 provided by Portuguese Banking Association (Associação Portuguesa de Bancos, APB), Bank of Portugal (Banco de Portugal, BdP), ECB and European Money Markets Institute (EMMI). During this period Portuguese banks made some changes in their business strategies, increasing the focus on servicing fees and commissions and higher returns from portfolio management. After executing the models and analysing the results, we can conclude that when ECB decided to use NIRP, as a mean to recover the European economy, the channels that most affected Portuguese bank's profitability, were the Interest Rate Channel, the Credit Channel and the Portfolio Channel.
The recently wide-spreading disappointment at 'Abenomics,' following the meager outcomes of its first arrow, quantitative easing (QE), drew attention to the third arrow, a comprehensive growth strategy focusing on structural reforms in Japan. In other words, although the Abe Cabinet has steered its economic policies toward monetary expansion since April 2013, which further developed into a Negative Interest Rates Policy (NIRP) on February 2016, their impacts on the real sectors, particularly on private investment and consumption, have been utterly trivial. Furthermore, the second arrow of Abenomics, fiscal stimulus, has reached an impasse due to a huge fiscal deficit mainly resulting from low fertility rates and population ageing.
In the face of the global financial crisis, central banks have used unconventional monetary policy instruments. Firstly, they implemented the interest rate policy, lowering base interest rates to a very low (almost zero) level. However, in the following years they did not undertake normalizing activities. The macroeconomic environment required further initiatives. For the first time in history, central banks have adopted Negative Interest Rate Policy (NIRP). The main aim of the study is to explore the risk accompanying the negative interest rate policy, aiming at identifying channels and consequences of its impact on the economy. The study verifies the research hypothesis stating that the risk of negative interest rates, so far unrecognized in Theory of Interest Rate, is a consequence of low effectiveness of monetary policy normalization and may adopt systemic nature, by influencing – through different channels – the financial stability and growth dynamics of the modern world economy.
Die Inhalte der verlinkten Blogs und Blog Beiträge unterliegen in vielen Fällen keiner redaktionellen Kontrolle.
Warnung zur Verfügbarkeit
Eine dauerhafte Verfügbarkeit ist nicht garantiert und liegt vollumfänglich in den Händen der Blogbetreiber:innen. Bitte erstellen Sie sich selbständig eine Kopie falls Sie einen Blog Beitrag zitieren möchten.
The so-called zero-lower-bound (ZLB) plays a prominent role in modern (and even older) macroeconomic theories. It is often introduced in a paper or at conference as a fact of life -- an unavoidable property of the physical environment, like gravity. But is it correct to view it in this way? Or is the ZLB better thought of as legal constraint--something that can potentially be circumvented by policy?
The Financial Services Regulatory Relief Act of 2006 allows the U.S. Federal Reserve (the Fed) to pay interest on reserve accounts that private banks hold at the Fed. Specifically, the Act states that:
Balances maintained at a Federal Reserve bank by or on behalf of a depository institution may receive earnings to be paid by the Federal Reserve bank at least once each calendar quarter, at a rate or rates not to exceed the general level of short-term interest rates. The effective date of this authority was advanced to October 1, 2008, by the Emergency Economic Stabilization Act of 2008.
It is not clear (to me, at least) whether the Act grants the Fed the authority to pay a negative interest rate on reserves. Note that if the interest-on-reserves (IOR) rate is set to a negative number, then banks would in effect be paying the Fed a "service fee" for the privilege of holding reserve balances with the Fed. But if the Fed is not legally permitted to use negative interest rate policy (NIRP), then the ZLB is obviously a legal constraint.
This legal constraint, however, may not be binding if the ZLB is also an economic constraint. In fact, the traditional explanation for the ZLB is the existence of physical currency bearing zero interest. The idea that arbitrage will effectively keep interest rates from falling below zero is deeply ingrained in the minds of economists. For example, Corriea, et. al. (2012) write:
Arbitrage between money and bonds requires nominal interest to be positive. This "zero bound" constraint gives rise to a macroeconomic situation known as a liquidity trap. It presents a difficult challenge for stabilization policy. However, we know from recent experience that the ZLB appears not to be an economic constraint. Several central banks today have set their deposit rates into negative territory:
There is currently over $10 trillion of government debt in the world yielding a negative nominal interest rate; see here. As of this writing, even long bonds like the German 10-year Bund are in negative territory.
Well, alright, so the ZLB is evidently not an economic constraint. But surely there is some limit to how low nominal interest rates can fall? This lower limit is called the effective lower bound (ELB). And economic theory is clear: if we're at the ELB in a recession, then monetary policy has done about as much as it can be expected to do.
But what exactly is the ELB? Is it -1%, -2%, -5%, or perhaps even lower? Economists like Miles Kimball believe it to sufficiently negative to warrant NIRP as an effective policy tool; see here (see also the discussion by Ken Rogoff in chapter 10 of his book). These arguments, however, did not seem to gain much traction. For example, in the present discussions concerning the Fed's new long-run monetary policy framework, the possibility of NIRP is not even mentioned. But perhaps it should be if the ELB is in fact significantly below zero. In what follows, I want to make my own (related) argument for why the ELB is probably a lot lower than most people think.
Suppose the Fed was to set the IOR to -10% (in a deep demand-driven recession, this would presumably be accompanied with a promise to raise the IOR at some point in the future). The traditional economic argument suggests that any security dominated in rate of return by cash would in this case be driven out of circulation.
The first thing we could imagine happening is banks attempting to convert their digital reserves into vault cash. Banks are presently holding over $1.6 trillion in reserves with the Fed. The largest denomination Federal Reserve note is $100. This is what $1 trillion in $100 bills apparently looks like:
That's about the size of a football field. Banks would not convert all of their reserves into cash--even if it was costless to do so--because they'd need about $20-30 billion or so to make interbank payments. Of course, managing all that cash would be far from costless. But there is a simpler reason for why banks would not make the conversion. The Fed could simply charge banks a 10% service fee on their vault cash.
Alright, well what effect is the -10% IOR rate going to have on the deposit rate (or fees) that banks offer (or charge) their depositors? Banks are not likely to pass the full cost on to their depositors, especially if they view the NIRP to be temporary, because they'll want to maintain their customer relationships.
But let us take the extreme case and suppose that NIRP is perceived to be permanent. Then surely deposit rates will decline (or bank fees will rise) significantly. Deposit rates may even decline to the point where depositors start withdrawing their money from the banking system. Banks may well let this source of funding go if they could borrow more cheaply from the Fed (banks would need to borrow reserves to honor the withdrawal requests of their customers). Of course, the Fed lending rate is also a policy variable and could, in principle, be lowered to negative territory as well.
But how realistic is it to imagine all or most bank deposits converted to cash? While this might be the case for small value accounts, it seems unlikely that the business sector would be able to manage its payments needs without the aid of the banking system. Even money market funds need to work through the banking system. I suppose one could imagine a new product created by (say) Vanguard in which they create a cash fund with equity shares redeemable for cash that is collected and stored in rented Las Vegas vault. But the moment the activity is intermediated, it becomes taxable. If the Fed is not permitted to tax (oops, charge a service fee) such entities, the fiscal authority could, in principle, implement a surcharge that is set automatically off the IOR rate in some manner.
I think in this way one can see how the ELB might easily be well below -5% (or more). This is probably low enough to allow us to disregard the ELB as a binding economic constraint. The relevant constraint is always a legal one. And laws can be changed if it is deemed to serve the public interest.
Keep in mind that in a large class of economic models, ranging from Keynes (1936) to New Keynesian, there is potentially much to be gained by eliminating the ZLB. If these models are wrong, then let's get rid of them. But if they're roughly correct, why don't we take their policy prescriptions seriously? Let's stop talking about the ZLB as if it's a force of nature. It is a policy choice. And if it's a bad policy choice, it should be changed.
The recent decision by the Governor of the Bank of England, Mark Carney, to support monetary policies that effectively deliver negative real rates of return on U.K. gilts, coupled with Janet Yellen's consideration of negative interest rate policy (NIRP), suggest that the "powers-that-be" in the West are cautiously revisiting the requisite monetary and fiscal economic policies to be applied against three distinct economic scenarios: recession, deflation, and stagflation. Key factors to be evaluated in a discussion of this shift include the broader economic malaise that appears to have set into the Eurozone, the fears regarding a "deflationary trap in China", the possible end of the Japanese recovery under Abe-nomics, and the less than stellar recovery by the U.S. from the events of 2008 and 2009. This article will explore the implications of recession, deflation, and stagflation as they relate specifically to the United Arab Emirates (U.A.E) and in general to the entire Gulf Cooperation Council (GCC). The three scenarios can be termed "the Good, the Bad - and the Ugly". This article will comment on pressures that arise from such scenarios, but is in no way a commentary on actual policies adopted by the Saudi or U.A.E central banks, nor, to the extent that these central banks are not independent of their governments, on each government's policy of the day.
Payment of compensation for different types of losses are continuously experiencing in Sri Lanka. One of the typical aspects is the compulsory land acquisition. Government acquires the lands from private landowners for providing infrastructure in terms of public interest and proceeds to pay the monetary compensation recovering the losses while the National Involuntary Resettlement Policy (NIRP) intends to address involuntary resettlement because of land acquisition. However, distrusting is arising on satisfaction of the victims of this process and no evidence how far the social sustainability concept is addressed. Therefore, this paper aims to analyze the determinants of satisfaction while focusing on the factors representing the social sustainability concept in compensation procedure of compulsory land acquisition of Sri Lanka. For acquiring data, questionnaire survey was conducted with 30 re-settlers who were the victims of government land acquisition for a reservoir project and an interview was carried out with prominent community leaders on 09 factors. Data were analyzed using descriptive statistics and a content analysis. The findings reveal that social sustainability aspects of neighborhood environment, education, market availability, electricity, and public health services were successful to some extent, while there were significant inconsistencies amongst assessed components within individual cases. Hence, the study reveals that pure monetary compensation process was unsuccessful since it represents the inadequacy to cover all losses of victims. Thus, for a total loss a resettling strategy is essential along with high concentration on the social sustainability aspects. Keywords: Compulsory Land Acquisition, Involuntary Resettlement Policy, Payment of Compensation, Social Sustainability Concept, Satisfaction
PurposeThis study aims to examine to what extent the Japanese financial markets are affected by the four periods of unconventional monetary policies (UMP) implemented by the Bank of Japan from 2013 to 2020.Design/methodology/approachUsing the daily 10-year term spread as a proxy for monetary easing policy, this study uses four sub-sample periods from 2013 to 2020 to look into the effectiveness of UMP on the Japanese financial markets.FindingsOur result shows that not all of the Bank of Japan's unconventional monetary policies are equally effective in influencing the Japanese financial markets. In particular, the QQE policy implemented from April 2013 to October 2014 effectively influenced the stock market, banking sector and foreign exchange market. However, the financial market impact of monetary policy is muted during the QQE expansion period. Likewise, the QQE with a negative interest rate policy influences only the banking sector. Finally, the QQE with its yield curve control policy effectively impacts the financial markets.Research limitations/implicationsThis research can be expanded by studying the international spillover effect of the Bank of Japan's UMP on the financial markets in Asian countries.Practical implicationsThe findings of this study enable investors to understand the causal relationship between the Bank of Japan's UMP and the financial market indicators, thereby helping them to position their portfolio investments. From the policy perspective, the finding is useful to inform the Bank of Japan on which policy is relatively effective in affecting the financial markets. In light of the empirical finding, the Bank of Japan should continue to pursue the QQE YCCP or revert to the initial QQE policy, as the two policies are relatively more effective than the QQE expansion and QQE NIRP in affecting the Japanese financial markets.Social implicationsThe empirical finding highlights the importance of controlling for the impact of different QQE policies in the model. Future research may consider conducting sub-sample analysis to cater to the different QQE policy regimes. This approach provides a clearer picture and valid inferences on the financial market impact of each QQE policy.Originality/valueThis study provides a comprehensive analysis of the impact of Bank of Japan's QQE on the Japanese financial markets. For the market participants, the findings of this study suggest that investors should closely gauge the development of the unconventional monetary policies of the Bank of Japan because the monetary easing policy influences the decision-making process of commercial banks, pension funds, mutual funds, retail investors and other stakeholders in the financial markets. The policy twist will have future ramifications for their loan, investment and retirement fund portfolios.
Publisher's version (útgefin grein) ; Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n=20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p[BI]= 4.4 × 10-10; p [SSBI] = 1.2 × 10 -4), diabetes (p[BI] = 1.7 × 10 -8; p [SSBI] = 2.8 × 10 -3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10 -24), and MRI-defined white matter hyperintensity burden (p [BI]=1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI. ; CHAP: R01-AG-11101, R01-AG-030146, NIRP-14-302587. SMART: This study was supported by a grant from the Netherlands Organization for Scientific Research–Medical Sciences (project no. 904-65–095). LBC: The authors thank the LBC1936 participants and the members of the LBC1936 research team who collected and collated the phenotypic and genotypic data. The LBC1936 is supported by Age UK (Disconnected Mind Programme grant). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1). The brain imaging was performed in the Brain Research Imaging Centre (https://www.ed.ac.uk/clinical-sciences/edinburgh-imaging), a center in the SINAPSE Collaboration (sinapse.ac.uk) supported by the Scottish Funding Council and Chief Scientist Office. Funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC) and the UK Medical Research Council is acknowledged. Genotyping was supported by a grant from the BBSRC (ref. BB/F019394/1). PROSPER: The PROSPER study was supported by an investigator-initiated grant obtained from Bristol-Myers Squibb. Prof. Dr. J.W. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Support for genotyping was provided by the seventh framework program of the European commission (grant 223004) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810). SCES and SiMES: National Medical Research Council Singapore Centre Grant NMRC/CG/013/2013. C.-Y.C. is supported by the National Medical Research Council, Singapore (CSA/033/2012), Singapore Translational Research Award (STaR) 2013. Dr. Kamran Ikram received additional funding from the Singapore Ministry of Health's National Medical Research Council (NMRC/CSA/038/2013). SHIP: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs, as well as the Social Ministry of the Federal State of Mecklenburg–West Pomerania, and the network "Greifswald Approach to Individualized Medicine (GANI_MED)" funded by the Federal Ministry of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. Whole-body MRI was supported by a joint grant from Siemens Healthineers, Erlangen, Germany, and the Federal State of Mecklenburg–West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. OATS (Older Australian Twins Study): OATS was supported by an Australian National Health and Medical Research Council (NHRMC)/Australian Research Council (ARC) Strategic Award (ID401162) and by a NHMRC grant (ID1045325). OATS was facilitated via access to the Australian Twin Registry, which is supported by the NHMRC Enabling Grant 310667. The OATS genotyping was partly supported by a Commonwealth Scientific and Industrial Research Organisation Flagship Collaboration Fund Grant. NOMAS: The Northern Manhattan Study is funded by the NIH grant "Stroke Incidence and Risk Factors in a Tri-Ethnic Region" (NINDS R01NS 29993). TASCOG: NHMRC and Heart Foundation. AGES: The study was funded by the National Institute on Aging (NIA) (N01-AG-12100), Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament), with contributions from the Intramural Research Programs at the NIA, the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute of Neurological Disorders and Stroke (NINDS) (Z01 HL004607-08 CE). ERF: The ERF study as a part of European Special Populations Research Network (EUROSPAN) was supported by European Commission FP6 STRP grant no. 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme "Quality of Life and Management of the Living Resources" of 5th Framework Programme (no. QLG2-CT-2002-01254). High-throughput analysis of the ERF data was supported by a joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). Exome sequencing analysis in ERF was supported by the ZonMw grant (project 91111025). Najaf Amin is supported by the Netherlands Brain Foundation (project no. F2013[1]-28). ARIC: The Atherosclerosis Risk in Communities study was performed as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HSN268201100006C, HSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL70825, R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and NIH contract HHSN268200625226C. Infrastructure was partly supported by grant no. UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. This project was also supported by NIH R01 grant NS087541 to M.F. FHS: This work was supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (contracts no. N01-HC-25195 and no. HHSN268201500001I), and its contract with Affymetrix, Inc. for genotyping services (contract no. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This study was also supported by grants from the NIA (R01s AG033040, AG033193, AG054076, AG049607, AG008122, and U01-AG049505) and the NINDS (R01-NS017950, UH2 NS100605). Dr. DeCarli is supported by the Alzheimer's Disease Center (P30 AG 010129). ASPS: The research reported in this article was funded by the Austrian Science Fund (FWF) grant nos. P20545-P05, P13180, and P20545-B05, by the Austrian National Bank Anniversary Fund, P15435, and the Austrian Ministry of Science under the aegis of the EU Joint Programme–Neurodegenerative Disease Research (JPND) (jpnd.eu). LLS: The Leiden Longevity Study has received funding from the European Union's Seventh Framework Programme (FP7/2007–2011) under grant agreement no. 259679. This study was supported by a grant from the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology, and the Netherlands Consortium for Healthy Ageing (grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO), UnileverColworth, and by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007). CHS: This CHS research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC15103, and HHSN268200960009C and grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, R01HL085251, and R01HL130114 from the NHLBI with additional contribution from NINDS. Additional support was provided through R01AG023629 from the NIA. A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Rotterdam Study: The generation and management of GWAS genotype data for the Rotterdam Study is supported by the Netherlands Organisation of Scientific Research (NWO) Investments (no. 175.010.2005.011, 911-03-012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/NWO project no. 050-060-810. The Rotterdam Study is funded by Erasmus MC Medical Center and Erasmus MC University, Rotterdam, Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. M.A.I. is supported by an NWO Veni grant (916.13.054). The 3-City Study: The 3-City Study is conducted under a partnership agreement among the Institut National de la Santé et de la Recherche Médicale (INSERM), the University of Bordeaux, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, Mutuelle Générale de l'Education Nationale (MGEN), Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Programme "Cohortes et collections de données biologiques." C.T. and S.D. have received investigator-initiated research funding from the French National Research Agency (ANR) and from the Fondation Leducq. S.D. is supported by a starting grant from the European Research Council (SEGWAY), a grant from the Joint Programme of Neurodegenerative Disease research (BRIDGET), from the European Union's Horizon 2020 research and innovation programme under grant agreements No 643417 & No 640643, and by the Initiative of Excellence of Bordeaux University. Part of the computations were performed at the Bordeaux Bioinformatics Center (CBiB), University of Bordeaux. This work was supported by the National Foundation for Alzheimer's Disease and Related Disorders, the Institut Pasteur de Lille, the Labex DISTALZ, and the Centre National de Génotypage. ADGC: The Alzheimer Disease Genetics Consortium is supported by NIH. NIH-NIA supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; NACC, U01 AG016976; NCRAD, U24 AG021886; NIA LOAD, U24 AG026395, U24 AG026390; Banner Sun Health Research Institute, P30 AG019610; Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, R01AG33193; Columbia University, P50 AG008702, R37 AG015473; Duke University, P30 AG028377, AG05128; Emory University, AG025688; Group Health Research Institute, UO1 AG06781, UO1 HG004610; Indiana University, P30 AG10133; Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, MO1RR00096, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, 1R01AG035137; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG30146; TGen, R01 NS059873; University of Alabama at Birmingham, P50 AG016582, UL1RR02777; University of Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573, P50, P50 AG016575, P50 AG016576, P50 AG016577; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383, AG05144; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991. The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS grant NS39764, NIMH MH60451, and by GlaxoSmithKline. Genotyping of the TGEN2 cohort was supported by Kronos Science. The TGen series was also funded by NIA grant AG041232, the Banner Alzheimer's Foundation, The Johnnie B. Byrd Sr. Alzheimer's Institute, the Medical Research Council, and the state of Arizona and also includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council [MRC], local NHS trusts, and Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council), South West Dementia Brain Bank (funding via numerous sources including the Higher Education Funding Council for England [HEFCE], Alzheimer's Research Trust [ART], BRACE, as well as North Bristol NHS Trust Research and Innovation Department and DeNDRoN), The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, and Universitat de Barcelona). ADNI: Funding for ADNI is through the Northern California Institute for Research and Education by grants from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., Alzheimer's Association, Alzheimer's Drug Discovery Foundation, the Dana Foundation, and the National Institute of Biomedical Imaging and Bioengineering and NIA grants U01 AG024904, RC2 AG036535, and K01 AG030514. Support was also provided by the Alzheimer's Association (LAF, IIRG-08-89720; MAP-V, IIRG-05-14147) and the US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program. SiGN: Stroke Genetic Network (SiGN) was supported in part by award nos. U01NS069208 and R01NS100178 from NINDS. Genetics of Early-Onset Stroke (GEOS) Study was supported by the NIH Genes, Environment and Health Initiative (GEI) grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488); and the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to Johns Hopkins University (contract no. HHSN268200782096C). Assistance with data cleaning was provided by the GENEVA Coordinating Center (U01 HG 004446; PI Bruce S. Weir). Study recruitment and assembly of datasets were supported by a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention, and by grants from NINDS and the NIH Office of Research on Women's Health (R01 NS45012, U01 NS069208-01). METASTROKE: ASGC: Australian population control data were derived from the Hunter Community Study. This research was funded by grants from the Australian National and Medical Health Research Council (NHMRC Project Grant ID: 569257), the Australian National Heart Foundation (NHF Project Grant ID: G 04S 1623), the University of Newcastle, the Gladys M Brawn Fellowship scheme, and the Vincent Fairfax Family Foundation in Australia. E.G.H. was supported by a Fellowship from the NHF and National Stroke Foundation of Australia (ID: 100071). J.M. was supported by an Australian Postgraduate Award. BRAINS: Bio-Repository of DNA in Stroke (BRAINS) is partly funded by a Senior Fellowship from the Department of Health (UK) to P.S., the Henry Smith Charity, and the UK-India Education Research Institutive (UKIERI) from the British Council. GEOS: Genetics of Early Onset Stroke (GEOS) Study, Baltimore, was supported by GEI Grant U01 HG004436, as part of the GENEVA consortium under GEI, with additional support provided by the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488), and the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs. Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to the Johns Hopkins University (contract no. HHSN268200782096C). Assistance with data cleaning was provided by the GENEVA Coordinating Center (U01 HG 004446; PI Bruce S. Weir). Study recruitment and assembly of datasets were supported by a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention, and by grants from NINDS and the NIH Office of Research on Women's Health (R01 NS45012, U01 NS069208-01). HPS: Heart Protection Study (HPS) (ISRCTN48489393) was supported by the UK MRC, British Heart Foundation, Merck and Co. (manufacturers of simvastatin), and Roche Vitamins Ltd. (manufacturers of vitamins). Genotyping was supported by a grant to Oxford University and CNG from Merck and Co. J.C.H. acknowledges support from the British Heart Foundation (FS/14/55/30806). ISGS: Ischemic Stroke Genetics Study (ISGS)/Siblings With Ischemic Stroke Study (SWISS) was supported in part by the Intramural Research Program of the NIA, NIH project Z01 AG-000954-06. ISGS/SWISS used samples and clinical data from the NIH-NINDS Human Genetics Resource Center DNA and Cell Line Repository (ccr.coriell.org/ninds), human subjects protocol nos. 2003-081 and 2004-147. ISGS/SWISS used stroke-free participants from the Baltimore Longitudinal Study of Aging (BLSA) as controls. The inclusion of BLSA samples was supported in part by the Intramural Research Program of the NIA, NIH project Z01 AG-000015-50, human subjects protocol no. 2003-078. The ISGS study was funded by NIH-NINDS Grant R01 NS-42733 (J.F.M.). The SWISS study was funded by NIH-NINDS Grant R01 NS-39987 (J.F.M.). This study used the high-performance computational capabilities of the Biowulf Linux cluster at the NIH (biowulf.nih.gov). MGH-GASROS: MGH Genes Affecting Stroke Risk and Outcome Study (MGH-GASROS) was supported by NINDS (U01 NS069208), the American Heart Association/Bugher Foundation Centers for Stroke Prevention Research 0775010N, the NIH and NHLBI's STAMPEED genomics research program (R01 HL087676), and a grant from the National Center for Research Resources. The Broad Institute Center for Genotyping and Analysis is supported by grant U54 RR020278 from the National Center for Research resources. Milan: Milano–Besta Stroke Register Collection and genotyping of the Milan cases within CEDIR were supported by the Italian Ministry of Health (grant nos.: RC 2007/LR6, RC 2008/LR6; RC 2009/LR8; RC 2010/LR8; GR-2011-02347041), FP6 LSHM-CT-2007-037273 for the PROCARDIS control samples. WTCCC2: Wellcome Trust Case-Control Consortium 2 (WTCCC2) was principally funded by the Wellcome Trust, as part of the Wellcome Trust Case Control Consortium 2 project (085475/B/08/Z and 085475/Z/08/Z and WT084724MA). The Stroke Association provided additional support for collection of some of the St George's, London cases. The Oxford cases were collected as part of the Oxford Vascular Study, which is funded by the MRC, Stroke Association, Dunhill Medical Trust, National Institute of Health Research (NIHR), and the NIHR Biomedical Research Centre, Oxford. The Edinburgh Stroke Study was supported by the Wellcome Trust (clinician scientist award to C.L.M.S.) and the Binks Trust. Sample processing occurred in the Genetics Core Laboratory of the Wellcome Trust Clinical Research Facility, Western General Hospital, Edinburgh. Much of the neuroimaging occurred in the Scottish Funding Council Brain Imaging Research Centre (https://www.ed.ac.uk/clinical-sciences/edinburgh-imaging), Division of Clinical Neurosciences, University of Edinburgh, a core area of the Wellcome Trust Clinical Research Facility, and part of the SINAPSE (Scottish Imaging Network: A Platform for Scientific Excellence) collaboration (sinapse.ac.uk), funded by the Scottish Funding Council and the Chief Scientist Office. Collection of the Munich cases and data analysis was supported by the Vascular Dementia Research Foundation. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements no. 666881, SVDs@target (to M.D.) and no. 667375, CoSTREAM (to M.D.); the DFG as part of the Munich Cluster for Systems Neurology (EXC 1010 SyNergy) and the CRC 1123 (B3) (to M.D.); the Corona Foundation (to M.D.); the Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain) (to M.D.); the e:Med program (e:AtheroSysMed) (to M.D.) and the FP7/2007-2103 European Union project CVgenes@target (grant agreement no. Health-F2-2013-601456) (to M.D.). M.F. and A.H. acknowledge support from the BHF Centre of Research Excellence in Oxford and the Wellcome Trust core award (090532/Z/09/Z). VISP: The GWAS component of the Vitamin Intervention for Stroke Prevention (VISP) study was supported by the US National Human Genome Research Institute (NHGRI), grant U01 HG005160 (PI Michèle Sale and Bradford Worrall), as part of the Genomics and Randomized Trials Network (GARNET). Genotyping services were provided by the Johns Hopkins University Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the NIH to Johns Hopkins University. Assistance with data cleaning was provided by the GARNET Coordinating Center (U01 HG005157; PI Bruce S. Weir). Study recruitment and collection of datasets for the VISP clinical trial were supported by an investigator-initiated research grant (R01 NS34447; PI James Toole) from the US Public Health Service, NINDS, Bethesda, MD. Control data obtained through the database of genotypes and phenotypes (dbGAP) maintained and supported by the United States National Center for Biotechnology Information, US National Library of Medicine. WHI: Funding support for WHI-GARNET was provided through the NHGRI GARNET (grant no. U01 HG005152). Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GARNET Coordinating Center (U01 HG005157). Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the GEI (U01 HG004424). R.L. is a senior clinical investigator of FWO Flanders. F.W.A. is supported by a Dekker scholarship-Junior Staff Member 2014T001–Netherlands Heart Foundation and UCL Hospitals NIHR Biomedical Research Centre. ; Peer Reviewed