Fintech’s future predicted in Team8 report

發表人  研究員    發表日期  2024-05-24    點閱次數  586

A Team8 report, An Unconventional Look At The Future Of Fintech, charts the industry’s path by predicting what remains and changes while taking a few big swings at potential mega-trends. Team8 creates and invests in companies focusing on cybersecurity, data and AI, fintech, and digital health. 

One constant is convenience. Any service that simplifies getting consumers what they want begins with an attractive proposition.

Another truism is you can’t fight City Hall. While some fintechs have been founded to exploit a regulatory loophole, Team8 partner Liran Amrany said a more sustainable strategy is to design companies with a compliance-first focus, acknowledging that regulation’s purpose is to foster trust.

Incumbents aren’t going anywhere. This isn’t the early 2010s when bold startups made their hay by saying they would get rid of the banks. Incumbents in highly regulated sectors like finance are stickier, harder to displace, and significantly more profitable. They enjoy economies of scale and have generated consumer trust.

The report states that the conditions of the last decade from which we are now emerging were anomalous. Amrany said it’s wiser to work with them. Yes, there is a chance a startup could achieve massive scale, but it’s much likelier they’ll get acquired, their service gets replicated by a much better-funded big bank, or their innovation spawns an entire as-a-service sector.

Sorry, folks, but fraud will remain omnipresent. Consider fraud a business where the practitioners want to maximize their ROI. They are early technology adapters, so companies must be vigilant.

Of course, that brings opportunities for fintechs in fraud detection and prevention. Amrany said one of those could be addressing Generative AI’s impact on fraud, which he said is just beginning. Generative AI’s ability to lower the entry bar for scammers and to assist with better fraud tactics at scale could impact entrepreneurs’ abilities to build companies with manageable fraud risk.



Top 10 fintech companies leading the way in 2024

發表人  研究員    發表日期  2024-04-29    點閱次數  800

Fintech is the fusion of two of the largest and wealthiest sectors of the economy: finance and technology. As a result of this fusion, a highly valuable class of companies has emerged, leading innovation and the fintech industry. In this article, we present the top 10 fintech companies of 2024.

Financial technology, or fintech, is a dynamic sector reshaping the way companies and consumers conduct financial transactions. It is a fusion of finance and technology, where innovation is the driving force behind the creation of new tools and services that streamline and enhance financial activities.

The fintech market is burgeoning, with a projected value of $851 billion by 2030, growing at a compound annual growth rate (CAGR) of 18.5% from 2024 to 2030. This growth is fueled by the adoption of advanced technologies like artificial intelligence, blockchain, and application programming interfaces (APIs) that are revolutionising traditional financial services.

Fintech’s reach extends across various segments, including digital payments, lending, insurance, and wealth management, making it an indispensable part of the modern financial landscape Within this continuously flourishing industry, a class of top-tier companies has emerged as the frontiers of the fintech sector.

As of 2024, these are the ten largest privately held fintech companies worldwide that are expected to dominate the fintech sector in the years to come, according to Forbes Advisor:

Ant Group

Ant Group is the company behind Alipay, China’s largest digital payment platform. It is a testament to the global impact of fintech, having been founded in 2004 and headquartered in Hangzhou. Ant Group has revolutionsed financial transactions by providing digital wallet and payment app services to over one billion users worldwide. These services allow users to conduct transactions with a simple QR code scan, eliminating the need for physical cash or cards.

Despite facing regulatory challenges and a halted IPO in 2020, Ant Group’s valuation was recalibrated to $78.5 billion in 2023, highlighting its status as a fintech titan with a significant footprint in the digital economy.

Stripe, Inc.

Stripe, Inc., headquartered in both San Francisco and Dublin, Ireland, is a fintech company that has significantly simplified payment processing for businesses. Founded in 2009, Stripe has become a go-to platform for merchants seeking instant account approval and a seamless transaction experience.

In 2022, Stripe processed an impressive $817 billion in transactions, serving a diverse clientele that includes industry giants such as Amazon, Google, and Shopify. Despite a valuation decrease from $91 billion in 2021 to $50 billion in 2023, Stripe remains the most valuable fintech entity in the United States, showcasing resilience and adaptability in a fluctuating market.


Revolut has emerged as Europe’s most valuable fintech company, with its headquarters in London, U.K. Since its inception in 2015, Revolut has redefined mobile banking, offering services that include international money transfers, a range of currency exchange options, and commission-free stock trading. Its app also supports cryptocurrency transactions, broadening its appeal.

With a valuation of $33 billion as of its last funding round in July 2021, Revolut has expanded its services globally, reaching markets beyond Europe, such as the United States and Japan. Despite concerns about its valuation in secondary markets, Revolut’s innovative financial solutions continue to disrupt the traditional banking sector.

Chime Financial, Inc.

Chime Financial, Inc., is a San Francisco-based fintech company that has established itself as a leading digital banking provider. Founded in 2012, Chime offers consumer-centric banking services that are technology-driven, designed to lower fees, and automate savings. Chime has a valuation of $25 billion, making it a compelling alternative to traditional banking institutions. Its primary focus is on providing a streamlined user experience and promoting customer financial health.

Chime’s innovative approach to banking includes features like early paycheck access and no overdraft fees, which have resonated with a growing customer base seeking more control over their finances.


Rapyd stands out as a fintech innovator specialising in global payment services. Founded in 2016 and headquartered in London, U.K., Rapyd has built a platform that facilitates cross-border payments through various methods, including bank transfers, cards, digital wallets, and cash. With a presence in over 190 countries, Rapyd aims to reduce the cost and complexity of financial transactions.

In early 2022, Rapyd’s valuation soared to $15 billion, a six-fold increase within a year, making it Israel’s most valuable private fintech company. Rapyd’s growth trajectory is marked by strategic acquisitions in Asia and Europe, enhancing its global payment network.


Plaid operates as a critical intermediary in the fintech ecosystem, securely connecting financial accounts to apps and services. Launched in 2013 and based in San Francisco, Plaid’s technology enables consumers to link their bank accounts to financial applications like Betterment, Chime, and Venmo. It supports connections with 12,000 financial institutions, facilitating a seamless and secure data-sharing process.

Plaid’s last funding round in 2021 valued the company at $13.4 billion. Although it once considered merging with Visa, Plaid continues to thrive independently, leveraging investor capital to expand its operations and solidify its position as a linchpin of financial technology integration.

Brex, Inc.

Brex, Inc., headquartered in San Francisco, has made a name for itself in the fintech sector by offering financial services tailored for growing businesses. Founded in 2017, Brex provides an array of products, including credit cards, cash management accounts, and spend management tools, all designed to help companies scale efficiently.

With a valuation of $12.3 billion, Brex stands out for its innovative approach to business finance, combining seamless integration with robust financial controls. The company’s rapid growth and substantial valuation reflect its success in addressing the unique financial needs of emerging businesses in a competitive market landscape.


GoodLeap, headquartered in Roseville, California, has established itself as a significant player in the sustainable home solutions market. Founded in 2003, GoodLeap provides a platform that facilitates financing for residential solar energy and other home efficiency solutions. With a valuation of $12 billion, GoodLeap is at the forefront of promoting eco-friendly investments, making it easier for homeowners to adopt sustainable technologies.

The company’s innovative financing model has not only contributed to the growth of clean energy but also reflects a broader trend in fintech companies supporting environmentally conscious consumer choices and contributing to a greener economy.


Bolt, based in San Francisco and founded in 2014, has redefined e-commerce with its one-click checkout software solution. By streamlining the checkout process, Bolt has significantly reduced cart abandonment rates, enhancing the online shopping experience. Customers can effortlessly complete purchases with a single account across Bolt’s network. At its last funding round, Bolt achieved an $11 billion valuation, a testament to its innovative approach to e-commerce transactions.

However, recent challenges have led to workforce reductions, suggesting that Bolt’s actual market value might be lower than its peak valuation, reflecting the volatile nature of fintech valuations in a rapidly evolving industry.


Checkout.com, a London-based fintech company, has carved out a significant niche in the online payment processing industry. Founded in 2012, Checkout.com has developed a robust platform that supports businesses with comprehensive, secure, and reliable online payment solutions. With a valuation of $11 billion, Checkout.com has distinguished itself through its ability to handle a wide array of payment methods, catering to a global clientele.

The company’s success is indicative of the fintech industry’s shift towards providing more integrated and user-friendly payment experiences, which are essential in today’s digital-first economy. Checkout.com’s growth trajectory underscores the increasing demand for fintech solutions that can adapt to diverse market needs.


How AI Boosts Fintech: 7 Promising AI-Powered Industries To Follow

發表人  研究員    發表日期  2023-11-02    點閱次數  1464

When Willie Sutton, once one of America’s most wanted fugitives, was asked why he robbed banks, his response was remarkably simple, “Because that’s where the money is.”

This is the same answer that could be given to those who inquire about the growing tendency towards regulation in the fintech sector, and who believe that increasing legislation could damage innovation in the field. That’s where the money is, therefore, the stakes are high, and more regulation will be there. This will most likely happen sooner than later, as Michael Hsu, Acting Comptroller of the Currency, said recently. Therefore, we can expect compliance to be at the forefront of the conversation, and to become a priority for venture capitalists, CFOs, and other stakeholders alike.



發表人  研究員    發表日期  2023-10-20    點閱次數  1996

FinTech Magazine runs through our Top 10 most ethical banks of 2023

FinTech Magazine takes a look at the Top 10 most ethical banks of 2023, looking at the ESG initiatives they employ to put them in our Top 10 list

Economic social governance (ESG) is becoming one of the most important considerations for financial institutions and banks alike. 

Below, FinTech Magazine runs through our Top 10 most ethical banks of 2023. 


THOUGHT LEADERSBuilding Trust in AI with ID Verification

發表人  研究員    發表日期  2023-10-01    點閱次數  1757

Generative AI has captured interest across businesses globally. In fact, 60% of organizations with reported AI adoption are now using generative AI. Today’s leaders are racing to determine how to incorporate AI tools into their tech stacks to remain competitive and relevant – and AI developers are creating more tools than ever before. But, with rapid adoption and the nature of the technology, many security and ethical concerns are not fully being considered as businesses rush to incorporate the latest and greatest technology. As a result, trust is waning.


Top 10 fintech startups based in the US 2023

發表人  研究員    發表日期  2023-09-28    點閱次數  2133

Startups are a strength to any economy, bringing new skills and ideas to industries like fintech.

e have collated a list of the Top 10 fintech startups based in the US, limited to firms who are Series A stage or earlier and ordered by amount raised

The US startup scene has ridden recent economic volatility, geopolitical instability, a downturn in the fundraising environment and even a global pandemic to be where it is today – so the early-stage and growth-stage fintechs who are still thriving deserve all the more credit for where they find themselves today.



Top 10 open banking platform providers in fintech 2023

發表人  研究員    發表日期  2023-08-11    點閱次數  1884

Open banking has made a new generation of financial tools and use cases possible.https://fintechmagazine.com/articles/top-10-open-banking-platform-providers-in-fintech-2023

These are some of the most influential companies creating technology solutions and APIs to help power the open banking revolution evident in fintech today


140+ Blockchain and Crypto Words: The Ultimate A-Z Glossary

發表人  研究員    發表日期  2023-07-12    點閱次數  2104

The most comprehensive dictionary online of blockchain and cryptocurrency-related buzzwords, from HODL to NFT, these are the terms you need to know


AI in Finance? Use Cases, Benefits, and Challenges

發表人  研究員    發表日期  2023-07-01    點閱次數  1450


AI in finance? If you’re unfamiliar with this combination, chances are you are missing out on a lot. The main goals of financial institutions  – banks, hedge funds, and insurance companies – are minimizing risks, reducing costs, and providing high-end customer services to clients using AI.


A model-based assessment of the macroeconomic impact of the ECB’s monetary policy tightening since December 2021

發表人  研究員    發表日期  2023-05-23    點閱次數  3356

The monetary policy normalisation that started in December 2021 has taken the ECB’s policy stance from a highly accommodative position into restrictive territory.

In December 2021 the ECB announced that it would begin normalising its policy stance by slowing the pace of net asset purchases, with net purchases under the pandemic emergency purchase programme (PEPP) and the asset purchase programme (APP) eventually ending in March 2022 and June 2022 respectively.
[1] The ECB’s interest rate guidance was revised in June 2022, and its key policy rates were increased by a total of 350 basis points between July 2022 and March 2023, rapidly tightening policy and ultimately taking rates into restrictive territory. While the speed and magnitude of this tightening is high from a historical perspective, monetary policy is transmitted to the economy with lags, implying that the full impact of the tightening will unfold over the next few years. This box uses a variety of empirical macroeconomic modelling frameworks to illustrate the impact on economic activity and inflation in the euro area.

Uncertainty about the impact of monetary policy on the economy can be addressed by drawing on a suite of models. This box presents details of a stylised exercise analysing the impact of policy tightening so far and illustrates the analytical challenges that surround such an assessment. There are two main challenges in assessing the impact of policy tightening. First, financial and macroeconomic variables are driven by a host of factors on both the demand side and the supply side. These factors need to be disentangled from the impact of monetary policy itself, calling for a model-based identification approach. And second, there is uncertainty regarding the transmission channels and lags of monetary policy, and it is therefore necessary to consider alternative methodologies with different transmission mechanisms in the interests of robustness. For these reasons, this assessment uses a suite of models: two structural DSGE models (NAWM II and MMR) and one large‑scale semi‑structural model (ECB-BASE).[2] This approach is in line with the conclusions of the ECB’s recent monetary policy strategy review, which emphasised the importance of robustness in carrying out model-based analyses within the Eurosystem.[3]

The assessment is carried out in two steps: first, by estimating the impact that monetary policy has on the yield curve, and second, by translating the impact on the yield curve into macroeconomic effects using macro models. The first step is to identify monetary policy-induced changes in short and long-term interest rates. The impact on short-term rates is calibrated on the basis of the upward shift observed in the forward curve for the euro short-term rate (€STR) at short to medium maturities since December 2021, which reflects both actual increases in policy rates and the anticipation of future increases. The impact on long-term rates is derived from the upward pressure on yields that is exerted by revisions to expected APP and PEPP holdings. In a second step, the policy-related effects on interest rates and the Eurosystem’s balance sheet are translated into macroeconomic effects using the suite of macro models, either directly or indirectly via the impact that balance sheet expectations have on long‑term rates.[4] In the DSGE models, the conditioning on the short-term interest rate is done through monetary policy shocks, which are partially anticipated in MMR and unexpected in NAWM II. In the ECB‑BASE model, short and long-term interest rates are assumed to be exogenous and the counterfactual is imposed as an alternative path relative to the baseline (i.e. the interest rate path expected in December 2021). In practice, market-based The results show that the policy tightening can be expected to exert substantial downward pressure on real activity and inflation over the period 2023-25. Since December 2021, short-term interest rates have increased by around 270 basis points on average over the projection horizon 2022‑25. Expectations for long-term interest rates, which account for anticipation, have increased by around 230 basis points over the same horizon (a significant percentage of which can be attributed to changes in APP and PEPP expectations, as Table A shows).[5] Short-term interest rate expectations began shifting upwards even before the first policy rate increase in July 2022 (Chart A), which shows the importance of accounting for policy expectations. The associated upward shift in the yield curve has an effect, in turn, on broader financing conditions and exerts a tangible impact on the economy. Averaging results across the three models, this assessment suggests that policy normalisation has exerted significant downward pressure on inflation and real GDP growth across the whole of the projection horizon (Chart B). Most of the impact on inflation is expected to be seen in the period from 2023 onward, with that impact peaking in 2024. The tightening of policy is estimated to have lowered inflation by around 50 basis points in 2022, while the downward impact on inflation is expected to average around 2 percentage points over the period 2023-25, with estimates differing substantially across the three models. The transmission to economic activity is faster, with the impact on GDP growth expected to peak in 2023 and a downward impact of 2 percentage points on average over the period 2022-25.[6] [7]financial assumptions also change as an endogenous reaction to other drivers, such as energy prices. In order to compute the impact of monetary policy, this exercise quantifies the macroeconomic impact of policy had it not followed the historical regularities captured by market‑based financial assumptions. This counterfactual is computed using policy shocks. Sensitivity to these assumptions is explored in more detail later in the box, particularly as regards the role of the expectation formation process.

Sources: Bloomberg, Refinitiv and ECB calculations.
Notes: The impact on short-term interest rates is calculated as the average difference between the short-term interest rates expected in the December 2021 and March 2023 MPE projections. The short-term interest rate curve is based on monetary policy-dated €STR forward contracts. The impact on ten-year yields is computed on the basis of changes to balance sheet expectations in the Survey of Monetary Analysts. The estimated impact on ten-year yields in the period from October 2021 (in order to account for anticipation) to May 2023 is around 65 basis points, while the average impact on expected ten-year yields over the period from 2022 to 2025 is 55 basis points. The impact is computed as the average across two models: a term-structure model (see Eser et al., op. cit.) and a BVAR model (see Rostagno et al., op. cit.).

Chart A

Impact on the monetary policy-dated €STR forward curve

(percentages per annum)

Sources: Bloomberg and ECB calculations.
Notes: This chart shows, for each Governing Council monetary policy meeting with updated economic projections, the €STR forward curve on the first available day of the maintenance period that follows the meeting. The purple line represents realised values for the deposit facility rate (DFR), with data being adjusted for the DFR space by applying a spread of 8 basis points. The cut-off dates for the data used for the various lines are based on the following final cut-off dates for projections: 23 November 2021 (December 2021), 28 February 2022 (March 2022), 17 May 2022 (June 2022), 22 August 2022 (September 2022), 25 November 2022 (December 2022) and 15 February 2023 (March 2023).

Source: ECB calculations based on the NAWM II model (see Coenen et al., op. cit.), the MMR model (see Mazelis et al., op. cit.) and the ECB-BASE model (see Angelini et al., op. cit.).
Notes: This chart reports the results of a simulation involving changes to short-term rate expectations between December 2021 and March 2023 and changes to expectations regarding the ECB’s balance sheet between October 2021 and May 2023. The reported values refer to year-on-year growth rates. “Mean” denotes the average across the three models.

The impact estimates are surrounded by significant uncertainty, reflecting differences in transmission channels across models, with the structural models displaying a stronger impact. The structural models are specifically designed for the purpose of deriving conditional correlations between identified monetary policy impulses and macroeconomic aggregates, while semi-structural models seek to achieve a satisfactory combination of identification and empirical fit. This can result in monetary policy tightening having a more limited impact, as the estimated impact based on such models probably conflates the effect of a “pure” monetary policy impulse with that of other non-policy drivers. In practice, there is a trade-off between the scale of the model and the number of drivers that can be identified, as abstracting from many of the cross-equation restrictions required for full structural identification allows a richer model structure (e.g. as regards consumption). In the DSGE models used for the simulations, consumption is closely linked to expected future short-term rates via the Euler equation. On the other hand, the richer modelling of consumption in the ECB-BASE model includes individual income risk and differing propensities to consume out of different income sources.[8] This implies that the dynamics of consumption are less dependent on expected short-term interest rates but better capture the observed persistence in consumption.

The larger impact of monetary policy in structural models also reflects stronger expectation channels. In particular, while structural models are forward‑looking, semi-structural models typically involve more backward-looking expectations, resulting in slower propagation of shocks.[9] Similarly, in DSGE models, an endogenous fall in inflation expectations in response to a rate rise leads to a further increase in real rates, thereby creating a reinforcing loop – a channel that is not present in semi-structural models, as these do not directly incorporate expectations of future inflation. This role played by expectations can be illustrated using sensitivity analysis. If it is assumed that agents do not anticipate policy decisions, the impact that the normalisation of policy has on inflation is halved in the MMR model (pale red bars in Chart C), bringing its estimates closer to those derived from the ECB-BASE model. Likewise, in the case of the NAWM II model, if the forward-looking expectations mechanism is modified to incorporate an adaptive learning scheme that makes households and firms’ expectations more backward‑looking, the impact that monetary policy has on inflation is mitigated (pale yellow bars in Chart C). Conversely, using more reactive expectations and strengthening the impact that asset prices have on the valuation of wealth in the ECB-BASE model (pale blue bars in Chart C) brings its responses closer to those produced by the two DSGE models under a tempered expectations channel.[10]

Source: ECB calculations based on the NAWM II, MMR and ECB-BASE models.
Notes: The reported values refer to year-on-year growth rates. “Mean” denotes the average across all three models using the standard expectations channel in each model, and is therefore equivalent to the mean in Chart B.

This model-based assessment can serve as a useful cross-check, but is no substitute for a data-dependent approach to the setting of policy and the monitoring of transmission over time. First, the current situation is characterised by exceptionally high levels of uncertainty about economic relations. The pandemic, the large energy shock, the fiscal responses to those two events and the unprecedented pace of the tightening of monetary policy are all likely to affect economic decisions and structures in ways that go beyond the historical regularities captured by available models. This uncertainty is compounded by the fact that macroeconomic outcomes reflect shocks from many different sources beyond monetary policy, and those shocks will propagate differently across the various models. Second, these estimates do not capture the prevention of any adverse non‑linear dynamics that might have materialised in the absence of monetary policy tightening, such as a risk of destabilising inflation expectations. Finally, the results point to considerable lags in the transmission of monetary policy to the economy. For all those reasons, while this model-based assessment can serve as a complementary cross-check, it is necessary to monitor indicators such as financial and credit variables, as well as leading indicators of activity and prices, to establish a timely and comprehensive medium-term inflation outlook.

  1. Furthermore, in December 2022 the ECB announced its intention to reduce the size of the APP portfolio by not reinvesting some of the principal payments from maturing securities. The APP portfolio will decline by €15 billion per month on average until the end of June 2023, and the Governing Council expects to discontinue all reinvestments thereafter.

  2. For details of the NAWM II model, see Coenen, G., Karadi, P., Schmidt, S. and Warne, A., “The New Area-Wide Model II: an extended version of the ECB’s micro-founded model for forecasting and policy analysis with a financial sector”, Working Paper Series, No 2200, ECB, November 2018 (revised December 2019); for information on the MMR model, see Mazelis, F., Motto, R. and Ristiniemi, A., “Monetary policy strategies for the euro area: optimal rules in the presence of the ELB”, Working Paper Series, No 2797, ECB, March 2023; for details of the ECB-BASE model, see Angelini, E., Bokan, N., Christoffel, K., Ciccarelli, M. and Zimic, S., “Introducing ECB-BASE: The blueprint of the new ECB semi-structural model for the euro area”, Working Paper Series, No 2315, ECB, September 2019. NAWM II is a fully micro-founded small open economy model with (i) an explicit intertemporal substitution channel, (ii) a banking sector with a financial accelerator mechanism, (iii) central bank asset purchases, (iv) interest rate-sensitive investment decisions and (v) a foreign economy block allowing for international spillovers. The MMR model is a closed economy DSGE model with (i) optimising households and firms, (ii) central bank asset purchases and (iii) a time-varying neutral interest rate. It also estimates the degree of attention to central bank communication, thereby helping to address the forward guidance puzzle encountered in standard DSGE models. ECB-BASE is a large semi-structural model designed to combine theoretical considerations with a good empirical fit and a comprehensive structure, reflecting its role as a workhorse model in the context of projections and policy simulations at the ECB. Its monetary policy transmission mechanism is stronger than in standard semi‑structural models, thanks to the explicit (VAR-based) role played by expectations and a multitude of financial channels.

  3. See “Review of macroeconomic modelling in the Eurosystem: current practices and scope for improvement”, Occasional Paper Series, No 267, ECB, September 2021.

  4. Both structural models capture asset purchases directly via the inclusion of the central bank’s balance sheet. In the ECB-BASE model, asset purchases are captured indirectly via their effect on long-term rates, so the impact of monetary policy normalisation is computed using both short and long-term interest rates.

  5. The impact that monetary policy has on short-term rates is computed on the basis of the upward shift observed in the forward curve for the €STR over the 2022-25 horizon. As increases in policy rates are typically transmitted one-to-one to the overnight rate, it is assumed that all changes in the €STR forward curve can be attributed to the tightening of policy. For long-term rates, the tightening impact stems from changes in expectations regarding balance sheet reduction. The impact of the latter is computed by mapping changes in balance sheet expectations derived from the Survey of Monetary Analysts into yields using an average across two models: (i) a term-structure model with a quantity variable and duration risk (see Eser, F., Lemke, W., Nyholm, K., Radde, S. and Vladu, A., “Tracing the impact of the ECB’s asset purchase programme on the yield curve”, Working Paper Series, No 2293, ECB, July 2019); and (ii) a large BVAR model where the impact of policy is identified using a dense event study (see Rostagno, M., Altavilla, C., Carboni, G., Lemke, W., Motto, R. and Saint Guilhem, A., “Combining negative rates, forward guidance and asset purchases: identification and impacts of the ECB’s unconventional policies”, Working Paper Series, No 2564, ECB, June 2021). The exchange rate is allowed to move endogenously.

  6. In all models, monetary policy is neutral in the long run. This implies that GDP growth will eventually turn positive after the initial negative impact. This happens earlier with the MMR model, as the exercise is conducted with expected shocks, hence the impact of policy is more frontloaded. This is illustrated in Chart C, which shows that, when shocks are unexpected, the profile of GDP growth is more similar to those of the other models.

  7. The May 2023 median expectations for the ECB’s balance sheet tightening are broadly consistent with the discontinuation of reinvestments under the APP programme as of July 2023. The tightening of balance sheet expectations is expected, on its own, to lower annual inflation by slightly more than 10 basis points in each year over the period 2023-25 and reduce GDP growth by the same amount over the period 2022-25.

  8. In contrast, there are fewer differences between ECB-BASE and the two DSGE models in terms of the modelling of the investment sector, with ECB-BASE featuring a financial accelerator mechanism.

  9. In the ECB-BASE model, expectations are modelled using VARs.

  10. More reactive expectations are obtained by increasing the elasticity of short-term inflation expectations relative to movements in interest rates (whereby greater elasticity is obtained by estimating the underlying VAR used for expectation formation using a different sample and an OLS estimator) and by allowing actual inflation developments to have a stronger effect on the perceived long-term inflation target. The impact that asset prices have on the valuation of wealth is strengthened by endogenising house prices and by increasing the elasticity of the revaluation term in financial wealth relative to movements in returns on financial assets.