Navigating Economic Shocks: How Firms Adapted to the Energy crisis

Economic

Summary:

To manage the consequences of unexpected shocks, policymakers need to understand how firms respond to such shocks. This column uses linked, high-frequency survey micro data to analyse the high-dimensional responses of UK firms to the recent energy price shock. It finds that firms adjust along multiple margins, such as passing on costs to prices, building up cash reserves, increasing debt levels, and shifting towards remote working arrangements. As governments consider how to support firms during the ongoing energy crisis, the findings indicate that interventions should target firm size and industry needs.

Countries around the world are having to grapple with the economic repercussions of geopolitical tensions and other unexpected shocks. The latest in a long list of recent shocks is Russia’s ongoing invasion of Ukraine, which caused a spike in energy prices and disrupted global supply chains. To manage the economic fallout from these shocks successfully, policymakers need to understand how firms respond to them.

The impact of the energy shock

Russia’s war in Ukraine has had profound effects on energy prices worldwide, most notably in Europe. In the UK, wholesale energy prices quadrupled within a few months (see Figure 1). For many firms, this shock prompted immediate concerns about survival, given the fundamental role of energy in many production processes. There are strong reasons to believe that firm responses to such a shock are not homogeneous: supply-side factors such as size (Kalemli-Ozcan and Saffie 2023), production technology (Durante et al. 2022), market structure (Duso and Szucs 2017), and firm management (Lamorgese et al. 2022, Jones et al. 2024) can each affect the precise bundle of actions an affected firm might take. So can demand-side factors (Fabra and Reguant 2014). The recent energy price shock in particular bore the hallmarks of a reallocation shock, with firms adjusting their inputs, outputs, prices, and production processes in many ways.

We leverage a novel combination of linked, high-frequency survey microdata, a pre-registered analysis plan and analytical tools to analyse high-dimensional responses to document how firms with differential exposure to energy prices within and between industries react along a broad range of margins such as adjusting output prices, managing inputs, maintaining processes, and ultimately striving to survive in a transformed economic landscape.

Figure 1 UK wholesale gas prices

Navigating Economic Shocks: How Firms Adapted to the Energy crisis_1

Methodological advances

We highlight the usefulness of existing but under-utilised real-time survey and administrative microdata collected by the UK Office for National Statistics to help policymakers understand economic shocks in real time. In addition, we identify areas where existing data sources are lacking, either because survey and administrative data give conflicting results, or because researchers are forced to use coarse subjective data instead of high-quality administrative data that are already collected but not yet made accessible for research purposes. This effectively constrains policy effectiveness or under-utilises informational resources that could render fiscal interventions more effective (Fetzer et al. 2024, Feld and Fetzer 2024).

We also develop a data framework to analyse firm responses to economic shocks in near-real time, which we then apply to understanding firm responses to the energy price shock. Our methodology combines a transparent research design that lays open what variation is used to identify the effects, with advanced, high-dimensional analysis techniques to provide a nuanced understanding of how these responses differ by size and industry. To discipline the analysis, we laid out these methods in a publicly accessible pre-analysis plan before any initial data exploration.

Main findings

Cost pass-through: On average, firms tend to pass through some of the increased energy costs to their customers. However, this pass-through varies significantly by size. Smaller firms are generally more likely to increase their output prices compared to larger firms, who tend to absorb some of the costs and invest more in capital improvements instead.

Financial adjustments: Firms do not yet lay off workers or declare bankruptcies in noticeable numbers. Instead, they build up cash reserves and increase their debt levels as a response to the shock. This trend is particularly pronounced among small firms.

Operational changes: Our research indicates that businesses are altering their operational practices in response to energy prices. Most notably, both large and small firms are shifting towards more remote working arrangements, which can shift some energy costs to employees, illustrating an adaptive strategy to manage rising overheads.

Industry differences: The reactions of firms are highly heterogeneous and vary depending on size and industry. For instance, while construction firms are investing in capital, smaller businesses in the hospitality sector are increasing their stock levels instead.

Survival rates: Interestingly, our research suggests that despite the economic pressures, firms do not perceive an imminent threat to their survival in the immediate term. Confidence among small business owners remains high, even as the administrative data show signs of increased business exits among small firms.

Implications for policymakers

This research not only provides insights into how businesses adapt to the current energy crisis, but also offers lessons for future policy design. In the short term, as governments consider how to support firms during the ongoing energy crisis, our findings indicate that interventions should target firm size and industry needs (Cox et al. 2024).

For instance, our findings indicate the importance of tailored business support that recognises the differing impacts of energy price shocks across sectors. Moreover, policies to support struggling businesses may differ from those aimed at facilitating rapid adaptation to changing market conditions. The ability to accurately measure and analyse firm responses in near-real time is paramount for getting this targeting right.

In the long run, firm adaptations to the energy price shock can teach us about the path to a greener, more prosperous economy. Understanding these responses provides invaluable evidence for developing targeted policies that promote resilience in the long term. Our study serves as a vital resource for policymakers, helping to chart an evidence-based course through the complexities of the energy crisis, all while keeping an eye towards the larger goal of achieving a sustainable future.

Conclusion

In a world of uncertainty, having the tools to adapt can make all the difference – for businesses and for policymakers. The observations drawn from our work on the energy crisis may well serve as a blueprint for navigating future economic challenges, ensuring that firms are not just surviving, but thriving in a changing world.

Source:Thiemo Fetzer Christina Palmou Jakob Schneebacher

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Risk Warnings and Disclaimers

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Owen Li

Economic

Artificial intelligence (AI) is transforming what machines can do, from processing natural language to analysing complex datasets and generating images. Recent advances in generative AI (for instance, large language models such as ChatGPT) are also animating a lively debate about the potential for large productivity gains that would allow economies to escape the disappointing productivity growth of the past two decades in many OECD countries (Goldin et al. 2024, Winker et al. 2021, Andre and Gal 2024).

Opinions in this debate vary widely (Figure 1). Some view AI as a transformative general-purpose technology that could unleash productivity growth across a wide range of economic activities and deliver large macroeconomic productivity gains over the next decade (Baily et al. 2023). Others argue that current AI technology is not particularly useful in most economic activities and predict that the aggregate productivity gains from AI will be modest (Acemoglu 2024). Our new paper (Filippucci et al. 2024) contributes to this debate by assessing the aggregate productivity gains from AI under different scenarios for sectoral productivity growth and by discussing the role of sectoral reallocation.

Figure 1 Divergent views about the aggregate productivity gains from AI

Miracle or Myth: Assessing the Macroeconomic Productivity Gains From Artificial Intelligence_1

Sources of disagreement regarding the aggregate productivity gains from AI

A growing body of research documents that AI can significantly increase the performance of workers in specific business contexts, such as customer service (by 14%), business consulting (by 40%), or software development (by more than 50%) (see Filippucci et al. 2024a and 2024b for a review of recent studies on the worker-level productivity impacts of AI). Given the mounting evidence of substantial productivity gains in specific domains, it may be surprising that opinions about the aggregate productivity benefits of AI remain so varied. However, predicting aggregate gains by extrapolating from evidence on the impact of AI in specific parts of the economy is challenging. The economy-wide impact of AI will depend on how broadly AI can be adopted to improve the production processes across many parts of the economy – often referred to as ‘exposure’ to AI – and on how rapidly firms will adopt AI.

In addition, aggregate productivity growth also depends on the relative demand for the goods and services produced in different sectors of the economy. Specifically, a Baumol effect (Baumol 1967, Nordhaus 2008) can arise in general equilibrium if productivity gains from AI are concentrated in a few sectors and relative sectoral demand reacts little to relative price changes. In this case, sectors where AI-driven productivity gains are low (e.g. construction, agriculture, and personal services) may grow as a share of GDP. Aggregate growth could turn out to be limited “not by what we do well but rather by what is essential and yet hard to improve” (Aghion et al. 2019).

We assess the macroeconomic productivity gains from AI under different scenarios for exposure to AI, the speed of AI adoption, and drivers of Baumol’s growth disease. In our main scenarios, we project that AI could contribute between 0.25 and 0.6 percentage points to annual total factor productivity growth in the US (or between 0.4 and 0.9 percentage points to annual labour productivity growth, assuming a standard long-run multiplier of 1.5 regarding the adjustment of the capital stock) over the next decade. Estimates for other economies are of similar magnitude, though somewhat lower, given that adoption of AI is expected to be slower and highly AI-exposed sectors are relatively smaller in these economies.

These predictions, if they indeed materialise, imply a substantial contribution to labour productivity in the context of weak productivity growth across the OECD over the past decades, which has been in the range of 1%–1.5% per year. The upper end of our estimates suggests a productivity gain from AI that is of similarly large magnitude as what has been attributed to ICT in the US during the high-growth decade starting in the mid-90s (around 1% per year; see Byrne et al. 2013 and Bunel et al. 2024).

From micro to macro

To derive projections for macroeconomic productivity growth, we proceed in two steps. First, inspired by Acemoglu (2024), we obtain sectoral productivity gains by combining estimates of worker-level performance gains with measures of sectoral exposure to AI (Figure 2) and projections of future adoption rates based on the historical experience with previous general-purpose technologies (Figure 3). The resulting ten-year sectoral gains in total factor productivity range from 1–2% in manual-intensive activities (agriculture, fishing, mining) to 15–20% in knowledge-intensive services (ICT, finance, professional services), depending on the specific assumptions on AI adoption and exposure.

Figure 2 Exposure to AI varies across sectors

Miracle or Myth: Assessing the Macroeconomic Productivity Gains From Artificial Intelligence_2

Figure 3 Different scenarios for the adoption path of AI

Miracle or Myth: Assessing the Macroeconomic Productivity Gains From Artificial Intelligence_3

In the second step, we derive the implied macroeconomic productivity gains using a calibrated multisector general-equilibrium model that accounts for sectoral input-output linkages and the role of demand in driving price adjustments and factor reallocation across sectors (Baqaee and Farhi 2019). Macroeconomic productivity gains are derived under different scenarios regarding the magnitude of micro-level productivity gains, sectoral exposure to AI, the speed of adoption, and structural determinants of sectoral reallocation (Figure 4). The aggregate productivity gains from AI can be decomposed into three effects: (1) a direct effect of increased productivity at the sectoral level; (2) an input-output multiplier effect as productivity gains in one sector also benefit other sectors through reduced costs of intermediate inputs; and (3) a Baumol effect.

Figure 4 Macro-level productivity gains from AI under different scenarios

Miracle or Myth: Assessing the Macroeconomic Productivity Gains From Artificial Intelligence_4

AI adoption is a key driver of productivity growth, but uneven sectoral gains could limit aggregate growth through a Baumol effect

A key insight that emerges from this analysis is that the macroeconomic impact of AI will depend primarily on the adoption speed and the degree to which AI can benefit economic activities across a wide range of sectors in the economy. Currently, adoption varies strongly across firms and sectors, with country-level adoption rates being generally low, in the range of 5%–15%, as reported by official statistics of businesses and firm-level studies (e.g. Calvino and Fontanelli 2023a, 2023b). A comparison of scenarios 1 (low adoption) and 2 (high adoption and expanded capabilities) shows that fast and productive integration of AI in a wider range of economic activities through expanded AI capabilities (e.g. further integration with other digital tools) is necessary for the emergence of large macroeconomic gains.

A negative Baumol effect on aggregate productivity growth arises if the productivity benefits of AI are concentrated in a few sectors, as in scenario 3 (high adoption and expanded capabilities, plus uneven sectoral gains and adjustment frictions), where sectoral gains are more uneven because knowledge-intensive sectors such as ICT and finance are assumed to adopt AI more quickly. 1 Productivity gains in the previous technology-driven boom (during the ICT boom decade starting in the mid-90s) were concentrated in a few sectors. In this spirit, scenario 4 (very large gains, concentrated in most exposed sectors, plus adjustment frictions) considers a concentration of sectoral gains that are closer to what was observed during that period. 2 Here, the Baumol effect reduces aggregate productivity gains by a third.

In contrast, no Baumol effect arises if AI gains are more widespread across sectors, for instance if AI is better integrated with robotics technology, which would mean that not only cognitive but also manual-intensive activities could benefit from AI (scenario 5, AI combined with robotics technology, plus adjustment frictions).

We also explore how aggregate productivity effects might depend on the presence of frictions through their impact on changes in the sectoral composition of the economy. Specifically, we consider the possibility that factors of production (capital and labour) cannot be freely reallocated across sectors over our projection horizon. We show that such frictions could magnify the negative Baumol effect by requiring steeper declines in the relative output prices of AI-boosted sectors to create enough demand for their increased output. This would lead to a larger decline in their GDP share, especially if demand is inelastic. 3 Hence, even though such frictions would prevent the reallocation of factors from high- to low-growth sectors, a general equilibrium perspective clarifies that aggregate productivity growth would still be harmed by preventing the efficient allocation of factors towards sectors where they are most valued.

Overall, AI holds promise to revitalise productivity growth in OECD countries and beyond. Governments can also play a role in shaping the macroeconomic gains from AI, for example by resolving legal uncertainties around accountability, which may hold back productive AI adoption by firms (OECD 2024a). At the same time, governments can foster a competitive environment (both in the AI-using as well as the AI-producing sectors; see Aghion and Bunel 2024, OECD 2024b) that is conducive to innovation and experimentation, while monitoring potential labour market disruptions and supporting workers as they transition into new roles in the AI economy (e.g. Acemoglu et al. 2023a,b, Baily et al. 2023, OECD 2023).

Source:Francesco Filippucci Peter Gal Matthias Schief

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Risk Warnings and Disclaimers

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Saif

Economic

Innovation has emerged as the lodestone of economic development and competitiveness in our time (Kogan et al. 2017). Yet while the usual suspects – investment in research and development, human capital, and infrastructure – have commanded attention, the catalytic role of institutional quality, particularly at the subnational level, remains curiously underexplored. This oversight persists despite mounting evidence that robust local institutions can profoundly shape the destiny of innovation ecosystems.

In China, where constant economic changes continue to transform cities and regions with remarkable speed, local institutional reforms offer an unprecedented laboratory for examining this gap in our understanding and assessing how governmental efficacy might nurture innovation and reduce the risk of economic stagnation. Our recent research (Zhang and Rodríguez-Pose 2024) delves into this relationship, focusing on the transformative potential of government agency reforms in galvanising innovation across Chinese city-regions.

The institutional landscape of innovation

Institutions are the cornerstone of economic systems, with their influence reaching from property rights to the rule of law. They can serve as either midwife or mortician to innovation, often determining whether regions can attract investment, nurture business growth, and retain talent. Institutional scholars have demonstrated that high-quality governance can effectively dissolve administrative barriers and reduce transaction costs for firms, thereby providing a fertile soil for innovation (Rodríguez-Pose and Zhang 2019, D'Ingiullo and Evangelista 2020). Tebaldi and Elmslie (2013), for instance, revealed significant cross-country variations in patent production attributable to institutional structures, while research by Donges et al. (2023) showed how improved governance in French-occupied German regions drove long-term innovation.

Moreover, theoretical frameworks abound, with social filter and innovation ecosystems theories suggesting that institutional weakness – manifested in inefficient governance, lack of transparency and accountability, or outright corruption – can suffocate innovation by fostering a hostile environment for economic actors (Rodríguez-Pose 1999, Morgan 2007). However, most scrutiny of how institutional constructs shape innovation has been confined to national-level institutions. Yet the efficacy of institutions often varies dramatically across regions within a single country, particularly in behemoths like China. Regional disparities in institutional quality shape the extent to which local governments can implement policies conducive to innovation, suggesting an urgent need to assess how subnational reforms influence local innovation performance (Rodríguez-Pose and Di Cataldo 2015).

A natural experiment in institutional reform

From 2009 to 2016, China embarked upon a remarkable odyssey of institutional reform targeting local government agencies. Historically centralised and encumbered by inefficiencies, China's Administration for Industry and Commerce (AIC) agencies underwent a gradual transformation into Market Supervisory Authorities (MSAs) in a decentralised reform effort aimed at raising regulatory efficiency and market oversight. The reforms bestowed greater autonomy upon local agencies, streamlined procedures, and eliminated redundancies. Unlike top-down, one-size-fits-all initiatives, the AIC-to-MSA reforms were voluntary, triggering considerable variation in implementation timing across city-regions. Early adopters of these reforms, with Shenzhen leading the vanguard, implemented changes that dramatically compressed business licensing times and simplified administrative requirements, fashioning a more hospitable environment for innovative firms. Shenzhen's example soon inspired emulation across China. Yet not all cities embraced the reform, some remaining – whether through inertia or convenience – anchored in the old system (Figure 1).

Figure 1 Government agency reform

Government reform and innovation performance in China_1

Institutional reform as a boost to innovation

We leverage this staggered rollout as a natural experiment to examine how these reforms influenced regional innovation outcomes. We find that city-regions that adopted the MSA model early witnessed more robust growth in innovation. Early adopters of the reform saw marked increases in innovation, particularly in regions with medium to high baseline innovation levels (Figure 2). For example, Shenzhen, the trailblazer in AIC reforms, streamlined business licensing and consolidated regulatory functions, reducing the average time to obtain operational licences from nearly 23 days to 8.5 days. This reduction in bureaucratic drag not only enhanced business efficiency but also attracted high-quality talent and investment, contributing to Shenzhen's ascendancy as a national and global innovation hub.

Figure 2 Evolution of patenting per capita by the timing of the reform

Government reform and innovation performance in China_2

Further analysis reveals that the reform's impact was not uniform: it proved most pronounced in city-regions already possessed of some measure of innovation infrastructure and human capital. These findings align with other studies showing that institutional reforms prove particularly effective in places where economic actors can respond to enhanced governance by intensifying innovation activities (Rodríguez-Pose and Zhang 2019). For less innovative regions, however, institutional reforms alone may prove insufficient to spark innovation without concurrent investments in education, skills, and infrastructure.

The broader implications of China's local reforms for innovation policy

The implications of these findings are twofold. First, they underscore the paramount importance of localised institutional quality in fostering innovation. While China's central government has choregraphed economic reform efforts, often linked to reforms in corporate taxation (Chen et al. 2018) or to limit corruption (Zhao et al. 2017), the experience of local agency reforms suggests that decentralised institutional adjustments can yield remarkable economic benefits. Indeed, reforms tailored to the needs and capacities of specific regions may prove more effective than one-size-fits-all policies. In Europe, for instance, Rodríguez-Pose and Ketterer (2020) argue that improving government quality in lagging regions demands targeted interventions rather than universal solutions.

Second, this study brings to the fore the potential of institutional reform to act as a powerful development driver in emerging economies. The experience of Shenzhen and other early reform adopters in China demonstrates that institutional enhancements can facilitate not merely incremental improvements but transformative economic shifts. The success of Shenzhen's MSA model has inspired other cities to follow suit, suggesting that effective local governance can amplify the broader goals of national development policy.

Lessons for policymakers: Towards tailored institutional interventions

China's government agency reform illustrates that institutional quality at the local level serves as a critical lever for innovation-driven growth. For policymakers, the key insight is that enhancing regional institutions can yield substantial returns, particularly in city-regions poised for innovation. Effective institutions reduce transaction costs, safeguard property rights, and diminish corruption, creating fertile ground for investment in high-risk, high-reward activities like R&D. However, as demonstrated in both China and Europe, the success of institutional reforms depends profoundly on context. City-regions with higher innovation capacity tend to derive greater benefit from these changes, suggesting that reforms ought to be tailored to regional characteristics. In our case study, in already highly innovative region, the immediate gains from MSA reform are manifest, while in regions with lower initial levels of human capital or economic complexity, additional reforms in education and infrastructure might prove necessary to amplify the benefits of institutional improvements. A phased approach to institutional reform, coupled with continuous evaluation and adaptation, is likely the most adequate pathway for countries marked by high levels of regional inequalities in wealth and innovation, ensuring that reforms remain aligned with local economic conditions and capacities.

The Chinese experience also underscores the virtue of policy experimentation and evaluation. The staggered rollout of AIC reforms created natural laboratories for assessing the efficacy of different approaches. Nations facing similar challenges, particularly those marked by stark regional disparities, might profit from a measured approach to institutional reform, gauging outcomes at each stage and calibrating strategies accordingly.

Conclusion

In an age where innovation determines the fault lines between economic resilience and fragility, our research has put in evidence the imperative of calibrating institutional reforms to regional contexts, suggesting that improvements in local governance can serve as the decisive lever for promoting innovation in areas of latent potential. For China, this reform-driven boost to innovation not only aligns with national economic imperatives but also strengthens regional resilience and inclusivity, addressing the challenge of uneven development. For the rest of the world, the Chinese experience offers a compelling blueprint for policymakers worldwide seeking to harness local institutions for balanced and sustainable innovation and growth.

Source:Maarten R C van Oordt

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Risk Warnings and Disclaimers

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Saif

Cryptocurrency

Prominent voices have expressed notably diverse opinions about Bitcoin. Sceptics have labelled it “the mother of all bubbles” (Roubini 2018) and “a Ponzi-scheme” (Welch 2017, Carstens 2018). Enthusiasts have hailed it as “the flagship of a new asset class” (Harvey et al. 2021) and “digital gold” (Popper 2016, Fink 2024). The recent surge in the Bitcoin price to record-breaking levels has ignited the bubble debate about cryptocurrencies once again.

Differences with traditional securities

Financial analysts have a hard time discerning which side of the debate is correct. Asset pricing theory establishes a clear conceptual framework to detect bubbles or overpricing for traditional financial securities: compare the current price to the fundamental value, where the latter is defined as the sum of discounted cash flows. However, such an approach is not particularly helpful when analysing a cryptocurrency that does not pay dividends (at least, not in fiat currency).

Another important difference is that cryptocurrencies can be used as a means of payment while financial securities typically cannot – imagine the practical difficulties of sending a remittance by transferring the ownership of a fractional share of your favourite airline. The use of cryptocurrency as a means of payment generates transactional demand. At the same time, cryptocurrencies differ from fiat currencies in that cryptocurrencies typically are not used as a unit of account. The dollar or euro amount on the bill and the cryptocurrency’s latest available exchange rate determine how much cryptocurrency one must transfer in order to pay. All else being equal, one needs to transfer double the amount of cryptocurrency if its exchange rate halves.

Figure 1 Bitcoin price and the share of active bitcoins

Bitcoin Valuation: Transactional Demand Versus Speculative Bubble_1

A basic model

Many of the varied opinions about cryptocurrencies are based on intuition and expressed by informal arguments. In a recent paper (Van Oordt 2024), I take a more formal approach to reveal what type of underlying beliefs justify the views on both sides of the bubble debate, introducing a basic model that builds on the classical modelling framework for rational bubbles (Blanchard 1978, Blanchard and Watson 1982). This framework allows for bubble outcomes where an asset appreciates just because of a widely held belief that the price will continue to increase. The new model tailors this framework to analyse cryptocurrencies that face transactional demand but are not used as a unit of account.

A nonzero baseline price

First, I derive the exchange rate of a cryptocurrency in what one could refer to as the baseline equilibrium. This exchange rate reflects the lowest possible equilibrium price, conditional upon current and future transactional demand. The baseline exchange rate can be driven by transactional demand, investor demand, or both.

Investors in the baseline equilibrium may hold coins because they expect to sell them at a profit to future users. Investors choose to do so only if they anticipate sufficiently high growth in transactional demand and sufficiently low growth in the number of coins. The exchange rate in the baseline equilibrium will be driven by the view of investors regarding the future peak level of the discounted transactional demand per coin.

Some critics have claimed that bitcoins are worth zero (e.g. Taleb 2021). The baseline equilibrium indicates otherwise. It shows that a zero price cannot be an equilibrium outcome, provided that investors expect some nonzero transactional demand, either now or at some future point. The only belief consistent with a zero price is the belief that the cryptocurrency does not face any transactional demand, nor will it ever face any transactional demand from anyone in the future. For major cryptocurrencies, such a belief is clearly inconsistent with real-world observations.

Bubbles

The bottom line of the baseline equilibrium is that financial analysts need to assess whether the future peak level of the discounted transactional demand per coin can explain the current exchange rate. If such an explanation is not possible, one might still argue that a cryptocurrency is a reasonable investment due to the anticipated future inflow of investors’ funds. Such an outcome is indeed possible in the model, and is referred to as a bubble equilibrium.

The exchange rate of a cryptocurrency in a bubble equilibrium can be higher than that which can be explained by the future peak in transactional demand due to additional demand from investors. Investors in a bubble equilibrium choose to hold the cryptocurrency, not with the purpose of selling it to future users, but because they expect it to appreciate as a consequence of a widely held belief among investors that the price will continue to increase.

Share of coins held by investors

A critical difference between the baseline equilibrium and a bubble equilibrium is the evolution in the share of coins held by investors. In the baseline equilibrium, the share of coins held by investors tends to decrease over time. Investors sell their coins to users as the transactional demand approaches the peak in the (discounted) transactional demand per coin. The share of coins held by investors converges to zero once the cryptocurrency reaches that peak.

By contrast, in a bubble equilibrium, the share of coins held by investors tends to increase over time as the bubble persists. If investors trade a cryptocurrency at increasingly high prices, then users will need fewer and fewer coins of that cryptocurrency to facilitate the same dollar amount of payments. Sooner or later, users optimally adjust their coin balances to the lower amounts they need given the increasingly high exchange rate. This adjustment implies a shift in the ownership of coins from users to investors. This shift continues as the bubble continues to grow.

Sustaining a bubble

Standard arithmetic shows that sustaining a bubble equilibrium for cryptocurrencies tends to require a continuous net inflow of investors’ funds. The ongoing shift in coin ownership from users to investors requires, in the aggregate, continuous coin purchases by investors. The coin purchases by investors on an equilibrium path must be sufficiently large to ensure that the cryptocurrency is expected to appreciate at the investors’ required rate of return.

How large are the investment inflows required to sustain a bubble equilibrium path? This depends on a variety of factors. The required net inflows of investors’ funds are smaller if there is less new issuance of the cryptocurrency. Growth in transactional demand, which brings in additional funds from users, can also temporarily reduce the net investment inflows required to sustain a bubble equilibrium.

The required investment inflows to sustain a bubble equilibrium path are also smaller if the return required by investors is lower. A lower required return could be justified if investors perceive the cryptocurrency as an insurance for bad states of the world, or ‘digital gold’. Such an insurance hypothesis implies that investors would be willing to hold the cryptocurrency despite permanently low expected returns.

Ponzi scheme accusation

Some in the ‘bubble’ camp have called cryptocurrencies such as Bitcoin a Ponzi scheme. A Ponzi scheme is an operation whose organisers pay returns to earlier investors from funds put into the scheme by later investors, who in turn are paid from funds contributed by even later investors, while the organisers skim off the top. An individual investor may profit from joining a Ponzi scheme provided that the scheme continues, but the future cash flows to investors in the aggregate are characterised by a negative present value, even if the scheme persists. Do cryptocurrencies share this feature for the aggregate cash flows to investors?

A careful analysis shows that the aggregate payoffs for investors in a cryptocurrency that follows the baseline price path are not equivalent to the payoffs of investors in a Ponzi scheme. True, investors experience negative aggregate cash outflows when they initially acquire the coins, but they expect positive aggregate cash flows from selling the coins to future users as the cryptocurrency approaches its peak in user demand per coin. In the baseline equilibrium, investors benefit in expectation from holding the cryptocurrency, both individually and in the aggregate.

Investors in a cryptocurrency whose price path follows a bubble equilibrium may experience aggregate cash flows that are equivalent to those from investing in a Ponzi-scheme. This is the case if the following conditions jointly hold true: (1) the cryptocurrency has non-negative issuance, (2) the return required by investors is non-negative, and (3) the growth rate of user demand will be permanently lower than the required return at some point in the future. Conditions (2) and (3) are relatively standard, but condition (1) depends on the design of the cryptocurrency supply.

Most, but not all, cryptocurrencies have a supply that is fixed or increases over time. Sustaining a bubble equilibrium price path for such cryptocurrencies requires a continuous net inflow of investors’ funds. In other words, if financial analysts cannot explain the valuations of such a cryptocurrency by the peak value of the discounted transactional demand per coin, then the aggregate payoffs for investors are expected to look remarkably close to those of a Ponzi scheme.

Source:Maarten R C van Oordt

Risk Warnings and Disclaimers

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Glendon

Economic

Artificial intelligence (AI) has revolutionized the trading industry, transforming the way traders and institutions approach financial markets. From analyzing vast datasets to automating complex trading strategies, AI has opened new possibilities for profitability and efficiency. However, despite its advantages, AI also introduces challenges and risks that traders must navigate carefully.

In this article, we’ll explore the key benefits and risks of using AI in trading to help you understand how to leverage this technology effectively.

Benefits of Using AI in Trading

Enhanced Data Analysis

AI systems can process and analyze large volumes of data in real-time.

Pattern Recognition: AI algorithms identify market trends and patterns that might be invisible to the human eye.

Sentiment Analysis: AI tools analyze news, social media, and other unstructured data to gauge market sentiment.

Speed and Efficiency

AI-powered trading systems execute trades faster than human traders, reducing latency and maximizing opportunities.

Automated strategies ensure traders can capitalize on market movements 24/7, even in global markets that operate across different time zones.

Improved Decision-Making

AI removes emotional biases from trading decisions, relying purely on data and predefined rules.

Risk Management: AI tools can set stop-loss and take-profit levels dynamically based on market conditions.

Backtesting: Algorithms simulate trading strategies using historical data, helping traders refine their approaches.

Cost Savings

By automating routine tasks and reducing the need for manual intervention, AI can lower operational costs significantly.

Institutions can replace time-consuming human analysis with faster, more accurate AI systems.

Scalability

AI allows traders to monitor and analyze multiple assets simultaneously, providing scalability that is impossible to achieve manually.

Risks of Using AI in Trading

Over-Reliance on Algorithms

Traders relying solely on AI may face issues if the system fails or encounters unforeseen market conditions.

Market Shocks: AI systems may not adapt well to sudden, unpredictable events like economic crises or geopolitical conflicts.

Algorithmic Bias: Flaws in the algorithm's design can lead to systematic errors and financial losses.

High Development and Maintenance Costs

Building and maintaining AI trading systems require significant investment in terms of technology and expertise.

Data Dependency: AI systems need vast amounts of high-quality data, which can be expensive to acquire and maintain.

Ethical and Regulatory Concerns

Market Manipulation: AI algorithms used for high-frequency trading (HFT) can unintentionally cause price manipulations or flash crashes.

Compliance Challenges: Traders must ensure AI systems adhere to financial regulations, which can be complex and subject to frequent changes.

Security Risks

AI systems are vulnerable to cyberattacks. A breach can lead to unauthorized access to trading accounts or sensitive financial data.Lack of Human Intuition

AI lacks the intuitive understanding of market psychology that experienced human traders possess. This limitation may lead to missed opportunities in nuanced situations.

How to Mitigate AI Risks in Trading

Regular Monitoring and Updates

Continuously monitor AI performance to identify and address issues promptly.

Update algorithms to adapt to changing market conditions.

Diversified Strategies

Avoid over-reliance on a single AI model. Combine AI tools with traditional trading methods to create a balanced approach.

Robust Security Measures

Invest in cybersecurity to protect AI systems and trading platforms from potential threats.

Regulatory Compliance

Work with legal experts to ensure that AI systems adhere to all relevant trading regulations and ethical guidelines.

Human Oversight

Combine AI systems with human oversight to leverage both machine efficiency and human intuition.

Conclusion

The integration of AI into trading offers unparalleled benefits, including enhanced data analysis, speed, and decision-making. However, these advantages come with risks, such as algorithmic bias, high costs, and security vulnerabilities. By understanding and addressing these risks, traders can harness the power of AI while safeguarding their investments.

As AI technology continues to evolve, its role in trading will only grow, making it essential for traders to stay informed and adapt to new advancements.

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Risk Warnings and Disclaimers

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Owen Li

Economic

In an increasingly interconnected world, global financial market movements often reflect broader economic conditions rather than isolated events in individual countries – a phenomenon often referred to as the ‘global financial cycle’ (Miranda-Agrippino and Rey 2021). The increasing globalisation of the financial cycle reflects, in part, deeper real economic integration through international trade, and, in part, heightened financial integration, evidenced by the expansion of cross-border banking claims (Claessens et al. 2014) and the inclusion of assets from emerging economies into the major benchmark indices used by institutional investors.

Thus far, scholarly attention has predominantly focused on the US, finding that shocks originating there are one of the main drivers of the global business and financial cycles (Miranda-Agripino and Rey 2015, Miranda-Agrippino et al. 2020, Boehm and Kroner 2023). Recently, the literature has shifted its focus to China as a driver of the global financial cycle, as this country has undergone significant structural changes that have increased its relevance for the global economy. These papers argue that shocks originating in China have distinct transmission channels, often through trade and commodities (Miranda-Agrippino et al. 2020, Barcelona et al. 2022, Lodge et al. 2023, Watt et al. 2019), global value added chains (Copestake et al. 2023), or through global inflation (Dieppe et al. 2024). Emerging economies are especially vulnerable to global turbulence due to their less developed, incomplete and less liquid markets. A growing body of literature has studied these spillovers (Canova 2005, Eichengreen and Gupta 2015, Engler et al. 2023, Faia et al. 2024).

In our recent paper (Campos et al. 2024) we assess the reaction of financial markets in East Asia, Eastern Europe, and Latin America to Chinese shocks. Using daily financial data from China and the US, and a Bayesian vector autoregression (VAR) with a combination of narrative, sign and magnitude restrictions, we identify five structural (orthogonal) shocks: two monetary policy shocks (one emanating from China and the other from the US, two macroeconomic shocks (one for China and one for the US), and a global risk shock, following the methodology of Lodge et al. (2023). We then measure the dynamic response of financial markets in individual emerging countries to the structural shocks related to China using a local projections framework.

This strategy offers an additional advantage over previous approaches. The fact that the influence of China on the rest of the world is found to be exerted primarily through its effect on global GDP and mediated by trade relationships raises the question of whether spillovers from China should affect other countries with a delay rather than immediately, as trade flows take some time to materialise. Consequently, relying solely on actual trade flows for identification might overlook the anticipatory aspects of these developments, which should instead be captured by financial market reactions.

Our findings indicate that Chinese macroeconomic shocks have immediate and lasting effects to emerging economies’ equity markets, as shown in Figure 1. However, the impact of monetary policy shocks originating in China is negligible.

Figure 1 Responses of emerging economies equity index to shocks in China

A Hidden Dragon: China’s Spillovers on the Financial Markets of Emerging Economies_1

Moreover, spillovers from China are strongest for Latin America (Figure 2): a macroeconomic shock in China leads to an increase of about 0.26% in Latin American stock markets on the same day, compared to only 0.15% in East Asia and Eastern Europe.

Figure 2 Responses of the equity index in emerging economies to macroeconomic shocks in China

A Hidden Dragon: China’s Spillovers on the Financial Markets of Emerging Economies_2

Note: The figures show averages for each region of impulse response function of equity prices to a positive macroeconomic shock in China that raise the equity price in China by 1%. Impulse responses are estimated with Local Projections for each country and averaged by region. LA is the average for Brazil, Chile, Colombia, Mexico, and Peru. EA is the average for Korea, Malaysia, Indonesia, and Thailand. EE is the average for the Czech Republic, Bulgaria, Hungary, Poland, and Romania. Blue areas show averages by region of 95%-confidence bands with standard errors adjusted for serial correlation using the Newey-West adjustment. These areas are not proper confidence intervals, but give a rough indication of the uncertainty around the point estimates.

At first glance, the stronger spillovers for Latin America may be surprising, given the higher integration of the industrial sectors of East Asia with China. However, as the Chinese economy is an important driver of commodity prices, economies that are more linked to the global commodity cycle should react more strongly than those which are less dependent on raw materials. As shown in Figure 3, Latin American companies whose core business is related to commodities have a stronger reaction to Chinese macroeconomic shocks than those whose business is more dependent on the domestic cycle.

Figure 3 Responses of Latin America equity index to macroeconomic shocks in China

A Hidden Dragon: China’s Spillovers on the Financial Markets of Emerging Economies_3

Note: Black dots represent the impact of the estimated response of a variable to a positive macroeconomic shock in China that increases Chinese equities by 1% (i.e., the first element of the impulse response function). The impulse response functions are estimated using Local Projections. Grey areas show 95%-confidence bands with standard errors adjusted for serial correlation using the Newey-West adjustment. Commodity related firms are defined as mining and industrial metals companies. Domestic cycle firms belong to sectors comprising real estate, automobile, consumer staples, chemicals, telecommunications, health care, retailers, and banks.

In addition to equity markets, we also quantify the impact that shocks originating in China have on key financial variables of other emerging economies. Results are very similar: the impact of macroeconomic shocks on the cost of sovereign external debt of these economies, as well as on their exchange rates versus the US dollar, is significant (Figure 4). 1

Figure 4 Responses of financial variables of emerging markets to shocks in China

A Hidden Dragon: China’s Spillovers on the Financial Markets of Emerging Economies_4

Overall, our analysis reveals that macroeconomic shocks from China significantly influence emerging markets: a positive macroeconomic shock in China leads to an increase in stock prices, a compression of sovereign and (except in Eastern Europe countries) corporate external debt spreads, and an appreciation of local currencies, although financial spillovers from a monetary policy shock in China are found to be weak. Moreover, the macroeconomic shocks from China have a more substantial effect on Latin America, and this greater impact appears to be driven by fluctuations in commodity prices.

Our results prompt two key consideration: (1) central banks’ multi country models often overlook significant and persistent financial spillovers from China to other emerging economies, while our results suggest that this is unrealistic; and (2) financial markets may channel the effects of commodity prices on real activity, beyond traditional trade linkages, as suggested by our results of relatively strong financial spillovers to Latin America.

Source:Rodolfo Campos Ana-Simona Manu Luis Molina Sánchez Marta Suárez-Varela

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Aussie, kiwi, loonie surrender to stronger US dollar

As 2024 approaches the finish line, the risk-linked currencies – the Australian dollar, the New Zealand dollar, and the Canadian dollar – also known as the commodity currencies, have been seen weakening notably, despite Wall Street conquering fresh record highs.

Year Ahead – Will Wounded Commodity Currencies Heal in 2025?_1

Although the Australian and New Zealand dollars outperformed their US counterpart in terms of year-to-date performance at some point in late September, October proved to be a dark month due to upbeat US data lessening the need for aggressive easing by the Fed, and due to investors remaining unsatisfied by the massive liquidity injections of the People’s Bank of China to shore up economic activity.

Year Ahead – Will Wounded Commodity Currencies Heal in 2025?_2

Fed and tariffs the biggest drivers

Alongside the already wounded loonie, all three currencies accelerated their slides after Republican candidate Donald Trump won the US election. Trump’s policies are seen as fueling inflation and thereby forcing the Fed to proceed even slower with easing, and even skip some rate cuts at its upcoming meetings. With several Fed officials, including Fed Chair Powell, noting recently that they are in no rush to further lower borrowing costs, investors are now assigning a strong chance for the Committee to take the sidelines as soon as at the first meeting of 2025, in January.

Year Ahead – Will Wounded Commodity Currencies Heal in 2025?_3

Having said that, Trump’s victory is not hurting the commodity-linked currencies only through the Fed-policy channel. His promises about massive tariffs on Chinese goods are translating into fears about a second trade war and deeper economic wounds for the biggest trading partner of Australia and New Zealand. Those fears could also result in lower commodity prices, as the world’s second largest economy is also the world’s top crude oil importer and the biggest copper and iron ore consumer. Trump has also threatened Canada and Mexico, as well as the BRICS countries, while there is nervousness about how he will proceed with Europe.

Year Ahead – Will Wounded Commodity Currencies Heal in 2025?_4

Thus, the outlook for the commodity currencies remains blurry, at least for the first half of the year, especially if US data continue to support the idea that the Fed may need to proceed with more rate-cut breaks down the road.

What about monetary policy?

But what happens if we remove the US dollar from the equation? Which of these three risk-linked currencies will perform best and which worst? The answer may lie down to the divergence in monetary policy strategies and expectations of the RBA, the RBNZ and the BoC.

Among the three central banks, the only one that hasn’t hit the rate cut button yet is the RBA, with market participants projecting its first 25bps reduction in April and almost another two by the end of 2025. This is largely due to underlying inflation remaining elevated. The Bank itself has pointed out that as measured by the trimmed mean, core inflation remains some way from the 2.5% midpoint of their inflation target range.

Year Ahead – Will Wounded Commodity Currencies Heal in 2025?_5

Both the BoC and the RBNZ reduced interest rates by equal amounts until now, but the one expected to continue cutting slightly more aggressively moving forward is the BoC.

The battle between the three

Thus, entering 2025, the aussie may continue to be the best performer among the three, while the loonie could be the laggard. But this could change once the RBA begins its own rate-cut cycle, as very dovish rate paths by the BoC and the RBNZ are already discounted and data coming out of Canada and New Zealand may not be bad enough to warrant so many basis points worth of reductions in 2025. Therefore, the aussie could run out of fuel at some point and take the last place.

As for the best performer during the second half of the year, it may be the Canadian dollar. Yes, the loonie suffered more on Trump’s first remarks about tariffs, but let’s not forget that he has pledged to proceed with a more aggressive policy against China, something that could weigh more on the aussie and kiwi.

Source:XM

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Navigating Economic Shocks: How Firms Adapted to the Energy crisis

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Methodological advances

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Implications for policymakers

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A basic model

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Risks of Using AI in Trading

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High Development and Maintenance Costs

Ethical and Regulatory Concerns

Security Risks

How to Mitigate AI Risks in Trading

Regular Monitoring and Updates

Diversified Strategies

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Regulatory Compliance

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Conclusion

A Hidden Dragon: China’s Spillovers on the Financial Markets of Emerging Economies

Figure 1 Responses of emerging economies equity index to shocks in China

Figure 2 Responses of the equity index in emerging economies to macroeconomic shocks in China

Figure 3 Responses of Latin America equity index to macroeconomic shocks in China

Figure 4 Responses of financial variables of emerging markets to shocks in China

Source:Rodolfo Campos Ana-Simona Manu Luis Molina Sánchez Marta Suárez-Varela

Year Ahead – Will Wounded Commodity Currencies Heal in 2025?

Aussie, kiwi, loonie surrender to stronger US dollar

Fed and tariffs the biggest drivers

What about monetary policy?

The battle between the three

Source:XM

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