The new center, known as the EEC, is led by Michael Weber, an economist at ESMT Berlin and a recipient of the Alexander von Humboldt Professorship, one of Germany’s most prestigious endowed research awards. According to the announcement, the center examines how households, firms, and financial market participants form expectations about economic developments and how these expectations influence economic behavior. That tripartite focus is significant. Much of the existing literature on expectations has concentrated on one actor at a time, typically households in household surveys or professional forecasters in financial contexts. A center designed from the outset to span all three groups reflects a growing recognition among economists that expectations do not form in isolation, but interact across the economy in ways that can amplify or dampen economic shocks.

The intellectual case for the center rests on a body of research that has transformed macroeconomics over the past two decades. Expectations about the economic future shape many of the decisions made by individuals and businesses: how much they spend or save, whether they invest, and how they plan for the long term. In theoretical models, anticipated inflation, expected income growth, and perceived employment risk all enter the calculus that determines consumption and investment. But empirical work has repeatedly shown that the expectations held by ordinary people diverge sharply from those of professional economists. Households often misjudge inflation levels, react asymmetrically to price increases and decreases, and update their beliefs in response to salient personal experiences rather than statistical aggregates. These deviations matter, because if expectations drive behavior, then systematic biases in expectations can produce systematic patterns in aggregate economic outcomes.

The better expectations are understood, the more reliably researchers can assess how individuals and organizations respond to economic shocks, market developments, and public communication. This point has taken on particular urgency since the inflation surge that followed the pandemic era, when central banks across Europe and North America discovered how difficult it is to gauge what the public actually believes about future prices. Survey measures of inflation expectations rose alongside realized inflation, and policymakers debated whether those beliefs had become unanchored, a scenario in which the public loses faith that inflation will return to target and behaves in ways that make the fear self-fulfilling. The episode underscored a lesson that the EEC’s research agenda takes as a starting point: reliable, high-frequency, well-designed measurement of expectations is not a luxury but a prerequisite for sound economic management.

To examine these dynamics, the center has organized its research program around four key areas. The first concerns households and how individuals form expectations about inflation, income, employment, and broader economic conditions. Research in this area typically draws on large-scale surveys that ask respondents not only for point forecasts but also for the uncertainty they attach to those forecasts, the information they use, and how their answers change when they receive new information. A second area examines how firms form expectations and how those expectations shape investment, hiring, and pricing decisions. Firm-level expectations have attracted growing attention because corporate beliefs about future demand, costs, and interest rates directly determine hiring plans and price setting, the channels through which expectations translate into realized inflation and employment.

The third area is financial markets, where expectations play a central role in investment decisions, asset pricing, and broader market behavior. Financial market participants are, in a formal sense, professional expectation formers, yet research has documented persistent heterogeneity in their beliefs, herding behavior, and sensitivity to framing and communication. The fourth area covers monetary policy and communication, examining how central bank actions and communication influence the economic expectations of households, firms, and financial market participants. This last area connects the center directly to one of the most consequential policy questions of the era: how words from a central bank move beliefs, and why the same message may persuade markets while leaving the broader public largely unaffected.

Weber framed the mission in terms of data. To better understand economic developments, he said, researchers need reliable data on what people and businesses think and how their assessments change over time. With the European Expectations Center, he said, the aim is to collect this data and make it available for research and economic policy debates. The emphasis on data infrastructure rather than on any single study suggests that the center sees itself as a platform for the wider research community. Survey-based expectations research is notoriously resource-intensive: designing questionnaires that avoid framing biases, recruiting representative samples, fielding surveys at sufficient frequency to capture dynamics, and harmonizing measures across countries all require sustained funding and technical expertise that individual research projects rarely command.

Toward that end, the EEC plans to provide the research community with extensive datasets and research resources, including survey datasets, questionnaires and survey instruments, working papers, and research outputs. Making survey instruments openly available is a deceptively important contribution. When researchers use comparable questions across countries and over time, their findings can be pooled, replicated, and extended, accelerating cumulative progress in a field where results have often been fragmented across bespoke surveys. The center also intends to work with partners to support knowledge exchange, develop new research initiatives, and disseminate findings to academic, policy, and practitioner audiences. Its current partner is the Bank for International Settlements, the Basel-based institution often described as the central bank for central banks, whose involvement signals an interest in connecting academic measurement work directly to the policy institutions that depend on it.

The European setting gives the project particular relevance. The euro area presents a distinctive laboratory for expectations research, because monetary policy is set at the supranational level while economic conditions vary considerably across member states. Households in different countries may experience the same monetary policy decision very differently, depending on their local inflation rates, housing markets, and labor conditions, and their divergent expectations can complicate the transmission of policy. A European center dedicated to expectations research is positioned to document these differences systematically and to test how communication strategies can be designed to reach heterogeneous publics. The involvement of researchers based at a leading international business school in Berlin also bridges the traditionally separate literatures on household expectations, corporate decision-making, and financial markets.

For the broader public, the stakes of this research agenda are easy to state. Expectations are the hinge between economic news and economic behavior: a headline about inflation matters only insofar as it changes what people believe and therefore what they do. If researchers can measure those beliefs accurately, identify when and why they go wrong, and understand which forms of communication correct them, then forecasts improve, policies become better targeted, and the damaging feedback loops that turn fear into recession or optimism into overheating become easier to manage. The European Expectations Center will not answer those questions on its own, but by building the shared data foundation on which such answers depend, it aims to give economists, central bankers, and policymakers a clearer view of the beliefs that quietly steer the economy.

Subject of Research: Formation of economic expectations among households, firms, and financial market participants and their influence on economic behavior

Article Title: ESMT Berlin establishes European Expectations Center for research on economic expectations

Article References: ESMT Berlin establishes European Expectations Center for research on economic expectations. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: ESMT Berlin, European Expectations Center, Michael Weber, economic expectations, inflation expectations, household surveys, firm expectations, financial markets, monetary policy, central bank communication, Bank for International Settlements, economic behavior

The study, conducted by Siddharth Bhambhwani, assistant clinical professor of accounting at Penn State, compared borrowing and deposit rates on Aave, one of the most widely used decentralized finance platforms, with U.S. Treasury yields over the period from January 2023 to March 2026. The findings, published in the journal Finance Research Letters, show that Treasury yields significantly influence rates in cryptocurrency lending markets despite the absence of any direct institutional link. The result challenges a popular narrative that decentralized finance, often abbreviated as DeFi, functions as a largely separate financial ecosystem driven primarily by cryptocurrency-specific factors.

To understand why the connection matters, it helps to understand how DeFi lending actually works. Instead of routing loans through a bank, DeFi relies on computer programs called smart contracts that run on a blockchain—a shared ledger maintained simultaneously by many computers, with no single owner. A useful mental model is a marketplace built around pools of digital assets. Some users deposit assets into these pools and earn interest; others borrow from the pools and pay interest. Unlike a conventional bank loan, however, DeFi borrowing generally requires borrowers to post cryptocurrency worth more than the amount they borrow. To borrow eighty dollars, a user might have to lock up one hundred dollars of another asset. If the value of that collateral falls close to the borrowed amount plus accrued interest, the software automatically sells it and repays the loan, a mechanism designed to protect depositors without the need for a human loan officer.

The appeal of this model is straightforward. A DeFi protocol, as these programs are known, generally does not evaluate a borrower’s credit score, income, or employment history in the way a bank might. If a user holds the necessary digital assets and meets the collateral requirements, the transaction executes automatically. Markets run continuously, and they can be accessed from many parts of the world without opening a conventional bank account. For depositors, the main attractions are yield and access: deposits based on stablecoins—cryptocurrencies designed to maintain a steady price by being tied to a traditional asset, most often the U.S. dollar—have often paid substantially more than traditional bank savings accounts. Transactions settle quickly, and the rules governing major protocols are encoded in smart contracts whose code is public and open to inspection. Yet these benefits come with substantial risks. Smart contracts can contain vulnerabilities and have been exploited, users generally lack deposit insurance, and recovering funds after a hack or failure can be difficult.

The mechanics of how interest rates are set on these platforms differ sharply from traditional finance. A bank decides what rate to pay on savings accounts based on market interest rates, competition for deposits, and its own funding needs. Treasury yields, meanwhile, emerge from financial markets as investors buy and sell U.S. government securities, responding to expectations about inflation, economic growth, and monetary policy. On a DeFi platform, the process is purely mechanical. Rates are determined by utilization—the ratio of assets borrowed from a pool to assets deposited into it. When relatively little is being borrowed, rates fall; when borrowing demand surges, rates climb. By design, this system contains no reference to Treasury markets, central bank policy, or any traditional benchmark. That is precisely what makes the empirical connection so striking.

Bhambhwani’s analysis found that for stablecoins, Treasury yields and DeFi rates move together in a systematic way. When Treasury yields rise, stablecoin borrowing and deposit rates tend to rise with them. The study estimated that a quarter-point move in the U.S. 10-year yield is associated with roughly a one-point move in stablecoin borrowing rates. The channel, though indirect, is intuitive. A stablecoin is designed to track the U.S. dollar, so a stablecoin depositor is effectively making a direct comparison: hold the token and earn the DeFi rate, or hold Treasury securities and earn the Treasury rate. When the outside opportunity changes, capital reallocates between the two options. Utilization shifts within the lending pools, and the mechanically determined DeFi rate adjusts—even though nothing in the protocol’s code references the Treasury market at all.

Notably, the relationship does not hold for volatile crypto assets such as Bitcoin and Ethereum. Someone depositing Bitcoin is not making the same comparison against a dollar-denominated benchmark. Instead, they are primarily focused on Bitcoin’s expected return, and a percentage-point change in Treasury yields is essentially noise compared with the often large and rapid swings in cryptocurrency prices, which can sometimes exceed ten percent in a single day. The contrast highlights that the bridge between traditional and decentralized finance runs specifically through dollar-pegged instruments, whose depositors behave like yield-seeking investors in any other money market rather than like speculative crypto traders.

The choice of the 10-year Treasury yield as the key benchmark carries particular significance. The 10-year yield reflects investors’ views about economic conditions over a relatively long horizon and serves as a foundational reference rate throughout global financial markets, influencing borrowing costs and asset valuations across mortgages, corporate debt, stocks, and other investments. What makes its relationship with DeFi especially interesting is that DeFi loans lack a conventional contractual maturity—they remain active as long as a borrower’s collateral exceeds the borrowed amount. Yet among the Treasury maturities examined in the study, the 10-year yield provided the most consistent additional information about stablecoin rates, suggesting that DeFi stablecoin markets are responding to the same broad financial conditions captured by this major traditional benchmark.

For anyone lending or borrowing stablecoins through DeFi platforms, the practical implication is that traditional interest rates may offer useful context for anticipating where DeFi rates are heading. A depositor chasing high stablecoin yields, or a borrower weighing the cost of a crypto-backed loan, can look to movements in Treasury yields as a signal of the direction DeFi rates are likely to follow. This reframes DeFi not as an isolated alternative economy but as a participant in the global market for capital, sensitive to the same macroeconomic forces—monetary policy expectations, inflation outlooks, and growth forecasts—that shape conventional asset prices.

More broadly, the findings arrive at a moment when decentralized finance is gaining mainstream appeal and policymakers, investors, and researchers are debating how integrated it has become with the existing financial system. The evidence from Aave’s lending markets indicates that as decentralized finance matures, traditional and decentralized markets may increasingly function not as two completely separate financial systems, but as interconnected parts of a larger market for capital. For a sector whose founding promise was independence from traditional finance, the data suggest a different reality: even code-governed lending pools, with no direct link to Wall Street or the Treasury market, cannot fully escape the gravitational pull of the world’s benchmark interest rate.

Subject of Research: The relationship between decentralized finance lending rates and U.S. Treasury yields

Article Title: Q&A: Is decentralized finance truly independent from traditional markets?

Article References: Q&A: Is decentralized finance truly independent from traditional markets?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: decentralized finance, DeFi, stablecoins, Treasury yields, Aave, cryptocurrency lending, smart contracts, interest rates, blockchain, Finance Research Letters, Penn State, financial markets

To understand why this matters, it helps to look at what most algorithmic trading systems actually do. The majority rely on price-based indicators such as moving averages, momentum oscillators, and volatility measures, which summarize what has already happened in the market. Some more recent systems have bolted on sentiment signals, but the authors of the new study argue that these are often simplified, reducing rich textual information to a single positive-or-negative score that strips away the context in which the sentiment was expressed. A headline saying that a company beat earnings expectations carries very different weight depending on whether it appears in a major financial outlet during a broad market rally or in an obscure forum during a panic. The framework developed by the Indian team was designed specifically to preserve that context, extracting sentiment features that capture both market relevance and directional tone before feeding them to the learning agent.

The technical heart of the system is proximal policy optimization, an algorithm that has become one of the workhorses of modern reinforcement learning. In reinforcement learning, an agent learns by interacting with an environment, taking actions, and receiving rewards or penalties that shape its future behavior. In a trading setting, the actions are typically buy, sell, or hold decisions, and the reward is tied to portfolio performance. PPO belongs to a family of methods called policy-gradient algorithms, which directly adjust the parameters of the policy, the mapping from market states to actions, in the direction that increases expected reward. What distinguishes PPO is its use of a constrained update rule that prevents the policy from changing too drastically between iterations, a property that makes training far more stable in noisy environments. Financial markets, with their erratic feedback and non-stationary dynamics, are about as noisy as environments get, which makes this stability property especially valuable.

This stability advantage showed up clearly in the study’s comparisons. The researchers found that policy-gradient methods like PPO consistently outperformed value-based reinforcement learning models, a rival family of algorithms that learn to estimate the long-term value of being in a particular state before deriving a trading policy from those estimates. Value-based methods, such as those built on Q-learning principles, can struggle when the relationship between states and rewards shifts abruptly, as it does when markets move from calm to crisis. Policy-gradient approaches, by contrast, optimize the behavior itself and appeared to adapt more gracefully across changing market regimes. The authors report that the sentiment-aware PPO agent delivered better risk-adjusted performance, meaning it earned returns that were more favorable relative to the volatility and drawdowns it endured, a metric that professional traders tend to care about far more than raw profit alone.

A crucial part of the work lies in how realistically the trading environment was constructed. Many academic trading simulations are criticized for ignoring the frictions that erode real-world profits, effectively training agents in a fantasy market where trading is free and positions can be scaled without limit. The Junagadh team deliberately built those frictions in. Their environment imposes transaction costs on every trade, enforces position limits that cap how much exposure the agent can take, and applies cooldown periods that prevent the kind of rapid-fire churn that would be impractical or prohibited in live markets. Technical indicators are also incorporated into the state representation, giving the agent access to the same quantitative signals that human traders watch. By forcing the learning algorithm to operate under these constraints, the researchers aimed to produce policies that would translate more credibly from backtest to practice, rather than exploiting loopholes that exist only in idealized simulations.

The sentiment pipeline itself reflects the current moment in artificial intelligence, in which large generative language models trained on vast text corpora can extract nuanced meaning from unstructured language. The study situates its contribution within a rapidly growing body of research that connects language models to finance. Recent work has explored whether models like ChatGPT can forecast stock price movements, and technical reports such as FinRL-Llama have experimented with integrating LLM-based sentiment analysis directly into financial reinforcement learning pipelines. Surveys of large language models in equity markets document how quickly this field is moving. What the new study adds is a systematic evaluation of how context-aware sentiment, rather than crude sentiment scores, changes the behavior and performance of a reinforcement learning trader when everything else in the environment is held constant.

The evaluation spanned multiple assets across both equity and index markets, giving the authors a way to test whether the benefits of sentiment awareness generalize beyond a single ticker or market structure. Equity markets, where individual stocks respond to company-specific news, and index markets, which aggregate the behavior of many firms, present different information environments, and a framework that works in both is more convincing than one tuned to a single instrument. The consistent pattern in the results, the authors report, is that incorporating context-aware sentiment information improves the agent’s adaptability across market regimes. In practical terms, this means the agent appears to change its behavior appropriately when the market shifts from bullish to bearish conditions or from low to high volatility, rather than continuing to apply strategies that worked in the previous regime, a failure mode that has plagued many quantitative systems.

The findings arrive at a time when the intersection of generative AI and finance is attracting intense attention from both researchers and practitioners, and they carry implications for how trading systems of the future might be designed. If the study’s conclusions hold up under further scrutiny, the lesson is not simply that sentiment data is useful, which many traders already believe, but that the way sentiment is computed and integrated matters enormously. A single scalar sentiment score may wash out exactly the contextual signals that give language its predictive power, while richer, context-sensitive representations extracted by generative models can give a learning agent genuinely new information about the state of the market. Combined with the stability of policy-gradient optimization and a realistic simulation environment, this could point toward trading systems that are more robust than either pure price-based algorithms or earlier sentiment-driven attempts.

Caveats remain, as they do with any backtested trading research. The authors note that the data generated and analyzed in the study are available from the lead author upon reasonable request but are not yet publicly available due to ongoing research, which means independent verification will depend on access to the underlying datasets. On the other hand, the team has provided the source code as supplementary material with the published article for research and reproducibility purposes, a step that should help other groups examine and extend the framework. The authors, who received no external funding for the work and declare no competing interests, emphasize that the goal is adaptability and risk control rather than spectacular returns. As generative language models continue to improve at understanding the subtleties of financial discourse, studies like this one suggest that the next generation of trading algorithms may be distinguished not by how fast they process prices, but by how well they read the story the market is telling about itself.

Subject of Research: Sentiment-aware reinforcement learning using generative language models for algorithmic trading

Article Title: Sentiment-aware reinforcement learning for algorithmic trading

Article References: Fuletra, J., Patel, H., & Panchal, S. (2026). Sentiment-aware reinforcement learning for algorithmic trading. SN Social Sciences, 6(10), Article 463. https://doi.org/10.1007/s43545-026-01682-4

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01682-4

Keywords: reinforcement learning, algorithmic trading, proximal policy optimization, sentiment analysis, generative AI, large language models, quantitative finance, financial markets, machine learning, risk management, behavioral finance, trading algorithms

The research team, led by Victoria Olushola Olanrewaju and including Seyed Alireza Athari, Mohamed Djafar Henni, Anar Eminov, and Emmanuel Oluwatosin Adewusi, measured AI through the S&P Kensho Artificial Intelligence Enablers Index. They then tracked its relationship with the S&P Global Clean Energy Transition Index, the S&P GSCI Energy Index, and the S&P Global Agribusiness Index. The choice of sectors is deliberate. AI is widely credited with improving renewable energy forecasting, smart-grid optimization, and storage management, yet it simultaneously boosts efficiency in oil and gas operations through predictive maintenance, reservoir characterization, and production optimization. In agriculture, AI underpins precision farming, crop monitoring, yield prediction, and supply-chain coordination. Any single average effect would therefore risk obscuring a far messier reality.

That messiness is precisely what the data reveal. Before running their main models, the authors checked the statistical character of each return series, and the results were striking. The fossil-energy index showed the widest swings of all, with daily returns ranging from a plunge of more than 30 percent to a gain of nearly 16 percent, and a standard deviation of 2.51 — far larger than the dispersion seen in AI, clean energy, or agribusiness returns. All four series were negatively skewed and heavily leptokurtic, meaning they exhibit fat tails and sudden extreme moves far more often than a normal distribution would predict. Jarque-Bera tests rejected normality at the one percent level for every series, while BDS diagnostics uncovered nonlinear dependence across multiple embedding dimensions. In plain terms: these markets do not behave like tidy textbook systems, and linear models that report a single average relationship would hide much of the action.

To confront this complexity, the study deployed a relatively novel econometric toolkit called Asymmetric Wavelet Quantile Regression, or AWQR. The method combines three ideas. First, quantile regression, introduced by Koenker and Bassett in 1978, estimates relationships across the entire conditional distribution of outcomes rather than just the mean, capturing what happens in calm markets and in turbulent tails alike. Second, a maximal overlap discrete wavelet transform using the LA8 filter decomposes each time series into frequency components, separating short-term fluctuations lasting up to 168 trading days, medium-term cycles running to 672 days, and long-horizon components beyond that. Third, and most importantly for the study’s headline finding, the AI return series is split into positive and negative components, allowing the researchers to ask whether good news for AI stocks moves markets differently than bad news.

The short-horizon results were, in a sense, a story of restraint. Across all three markets, the associations between AI-related equity performance and sector returns were generally small and mostly statistically insignificant over horizons of up to roughly 168 trading days. AI-driven market movements, in other words, do not appear to translate into immediate, systematic shifts in clean energy, fossil energy, or agribusiness performance. This finding challenges a popular narrative in which every AI headline instantly ripples through commodity and sector markets. Instead, the transmission appears to build over time, suggesting that investors and market participants need longer windows to reprice the economic implications of technological change.

At the medium horizon, the picture changed sharply. The symmetric specification — which uses the full AI return series — produced predominantly positive coefficients across the conditional distribution for all three markets, indicating that stronger AI-related equity performance tends to accompany stronger sector returns over these intermediate windows. But when the authors separated positive AI shocks from negative ones, a fascinating pattern emerged: the positive-shock coefficients were frequently negative, while the negative-shock coefficients were frequently positive. This is not a contradiction. The symmetric coefficient summarizes the net association over all AI movements, while the directional models condition separately on observations of opposite sign, revealing information that aggregation conceals. For the fossil-energy index, the negative association with positive AI shocks at the medium horizon is consistent with market expectations that AI advances support energy efficiency and substitution toward cleaner technologies, even as AI simultaneously improves drilling and production economics.

Crucially, the authors refused to declare asymmetry merely because two coefficients had different signs. They applied a formal bootstrap Wald test of the null hypothesis that the positive- and negative-shock coefficients are equal, using 499 pairs-bootstrap replications for each quantile-horizon cell. Only where this test rejected equality did they conclude that genuine asymmetry exists. The verdict: for the clean energy index, the fossil-energy index, and the agribusiness index alike, the null of coefficient equality was rejected throughout most of the medium horizon and across a substantial share of long-horizon quantiles, while evidence at short horizons was far more limited. The asymmetry, in other words, is statistically real where it matters most — at the horizons where medium- and long-term investors operate.

The long-horizon results added further texture. For the clean energy market, the symmetric association shifted from negative or weak values at lower quantiles toward positive values at the upper tail, hinting that AI and clean energy move together most strongly when clean energy markets are already performing well. For fossil energy, coefficients varied substantially across quantiles, with some positive long-horizon associations at upper quantiles plausibly reflecting the growing use of machine learning in petroleum exploration, predictive maintenance, and production forecasting. For agribusiness, the long-horizon symmetric coefficient was negative at lower quantiles and positive toward the upper tail, a profile consistent with AI’s role in crop monitoring, yield prediction, logistics, and resource allocation — benefits that may matter most when agricultural markets are strong.

The authors are careful about what these findings do and do not mean. The framework is deliberately bivariate, with no controls for broader stock-market conditions, interest rates, exchange rates, commodity prices, or geopolitical events, any of which could influence both AI equities and the sector indices. The indices may also share constituent companies or common factor exposures, meaning part of the estimated comovement could reflect overlapping exposures rather than a distinct AI transmission channel. And with hundreds of coefficients estimated across quantiles and horizons, isolated significant results could arise by chance, since no multiple-testing correction was applied. The researchers therefore place greater weight on patterns that persist across adjacent quantiles, survive lag-based robustness checks at lags one through four, and pass the formal Wald tests. The conclusions are framed as conditional associations, not causal effects.

Even with those caveats, the policy implications are tangible. For clean energy, the results reinforce the case for investment in AI-enabled forecasting, grid management, and storage applications. For fossil-energy activities, AI can be directed toward operational efficiency, emissions monitoring, safety, and environmental compliance — a reminder that the same technology driving the energy transition is also entrenching itself in the industry it may eventually displace. For agribusiness, digital infrastructure, precision agriculture, and climate-risk forecasting emerge as practical levers, though barriers such as high costs, weak infrastructure, and skill gaps can create uneven adoption. Perhaps the deepest lesson is for investors and policymakers alike: the market consequences of the AI revolution are not a single number but a landscape — one that shifts with the direction of the shock, the part of the distribution you occupy, and the clock you are watching.

Subject of Research: Asymmetric transmission of AI-related equity performance to clean energy, fossil energy, and agribusiness markets

Article Title: Digital intelligence, green transition, and fossil energy: Asymmetric market transmission from AI-related equity performance

Article References: Digital intelligence, green transition, and fossil energy: Asymmetric market transmission from AI-related equity performance. (n.d.). https://doi.org/10.1016/j.envc.2026.101673

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101673

Keywords: artificial intelligence, clean energy, fossil fuels, agribusiness, quantile regression, wavelet analysis, financial markets, energy transition, asymmetric shocks, S&P indices, econometrics, market volatility