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on Macroeconomics |
| By: | Ricardo J. Caballero; Alp Simsek |
| Abstract: | We develop a model of central bank communication where market participants' uncertainty about desired financial conditions creates misunderstandings ("tantrums") and amplifies the impact of financial noise on asset prices and economic activity. We show that directly communicating the expected financial conditions path (FCI-plot) eliminates tantrums and recruits arbitrageurs to insulate conditions from noise, while communicating expected interest rates alone fails to achieve these benefits. We demonstrate that scenario-based FCI-plot communication enhances recruitment when participants disagree with the central bank regarding scenario probabilities. This enables an "agree-to-disagree" equilibrium where markets help implement central bank objectives despite differing views. |
| JEL: | E12 E32 E44 E52 E58 G10 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34325 |
| By: | Byeungchun Kwon; Taejin Park; Phurichai Rungcharoenkitkul; Frank Smets |
| Abstract: | Macroeconomic indicators provide quantitative signals that must be pieced together and interpreted by economists. We propose a reversed approach of parsing press narratives directly using Large Language Models (LLM) to recover growth and inflation sentiment indices. A key advantage of this LLM-based approach is the ability to decompose aggregate sentiment into its drivers, readily enabling an interpretation of macroeconomic dynamics. Our sentiment indices track hard-data counterparts closely, providing an accurate, near real-time picture of the macroeconomy. Their components–demand, supply, and deeper structural forces–are intuitive and consistent with prior model-based studies. Incorporating sentiment indices improves the forecasting performance of simple statistical models, pointing to information unspanned by traditional data. |
| Keywords: | macroeconomic sentiment, growth, inflation, monetary policy, fiscal policy, LLMs, machine learning |
| JEL: | E30 E44 E60 C55 C82 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1294 |
| By: | Luca Benati, Juan-Pablo Nicolini |
| Abstract: | Modern analysis of the welfare effects of monetary policy is based on moneyless models and therefore ignores the effect of inflation on the efficiency of transactions. A justification for this strategy is that these welfare effects are quantitatively very small, as argued by Ireland (2009). We revisit Ireland’s result using recent data for the United States and several other developed countries. Our computations are influenced by the experience of very low short-term rates observed since Ireland’s work in the countries we study. We estimate the welfare cost of a steady state nominal interest rate of 5% to be at least one order of magnitude higher than in Ireland (2009), which questions the validity of performing monetary policy evaluation in cashless models. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:ube:dpvwib:dp2508 |
| By: | Takuma Kunieda (School of Economics, Kwansei Gakuin University); Kei Kuwahara (Kunieda Laboratory in School of Economics, Kwansei Gakuin University) |
| Abstract: | This paper empirically examines collateral constraints in the Kiyotaki and Moore [1997. Credit cycles. Journal of Political Economy 105(2), 211-248] model using land price data from three major prefectures in Japan: Tokyo, Osaka, and Hyogo. After confirming the stationarity of land prices, we estimate their dynamic equations and show that they follow a second-order autoregressive (AR(2)) process, consistent with the presence of binding collateral constraints. We further apply the supremum Wald test and identify structural breaks at the onset of the early 1990s asset price bubble collapse. These results suggest that financial frictions played a critical role in shaping land price dynamics in Japan's regional economies. Overall, our findings demonstrate that the Kiyotaki-Moore framework provides a useful tool for capturing the dynamic behavior of financially constrained economies. By providing new regional evidence, this study contributes to the literature on macroeconomics and financial market imperfections. |
| Keywords: | Collateral Constraints, Financial Frictions, Land Price Dynamics, Kiyotaki-Moore Model, Credit Cycles, Regional Economies. |
| JEL: | G12 E32 E44 R30 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:kgu:wpaper:300 |
| By: | Carlo Alcaraz; Stijn Claessens; Gabriel Cuadra; David Marques-Ibanez; Horacio Sapriza |
| Abstract: | How does the credible announcement of an unconventional monetary policy intervention affect bank lending standards during crises? We use a major central bank announcement, the "whatever it takes" speech of the European Central Bank President that boosted the capital of banks, as a natural experiment. We compare changes in lending standards of subsidiaries of euro area versus other banks in a third country, Mexico. The speech reversed a prior trend of euro area banks augmenting their risk-taking via loan growth, lending rates, and credit risk. Our findings show that policies that amount to capitalization can reduce risk-taking in times of stress, adding a new dimension to the bank capital channel. |
| Keywords: | monetary policy; financial institutions and regulation |
| JEL: | E51 G21 F34 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedrwp:101854 |
| By: | Anoushka Harit; Zhongtian Sun; Jongmin Yu |
| Abstract: | We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies \emph{Granger-causal hypergraph structure}, \emph{Riemannian geometry}, and \emph{causally masked Transformer attention}. CSHT models the directional influence of financial news and sentiment on asset returns by extracting multivariate Granger-causal dependencies, which are encoded as directional hyperedges on the surface of a hypersphere. Attention is constrained via angular masks that preserve both temporal directionality and geometric consistency. Evaluated on S\&P 500 data from 2018 to 2023, including the 2020 COVID-19 shock, CSHT consistently outperforms baselines across return prediction, regime classification, and top-asset ranking tasks. By enforcing predictive causal structure and embedding variables in a Riemannian manifold, CSHT delivers both \emph{robust generalisation across market regimes} and \emph{transparent attribution pathways} from macroeconomic events to stock-level responses. These results suggest that CSHT is a principled and practical solution for trustworthy financial forecasting under uncertainty. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.04357 |
| By: | Ava C. Blake; Nivika A. Gandhi; Anurag R. Jakkula |
| Abstract: | Accurate prediction of financial market volatility is critical for risk management, derivatives pricing, and investment strategy. In this study, we propose a multitude of regime-switching methods to improve the prediction of S&P 500 volatility by capturing structural changes in the market across time. We use eleven years of SPX data, from May 1st, 2014 to May 27th, 2025, to compute daily realized volatility (RV) from 5-minute intraday log returns, adjusted for irregular trading days. To enhance forecast accuracy, we engineered features to capture both historical dynamics and forward-looking market sentiment across regimes. The regime-switching methods include a soft Markov switching algorithm to estimate soft-regime probabilities, a distributional spectral clustering method that uses XGBoost to assign clusters at prediction time, and a coefficient-based soft regime algorithm that extracts HAR coefficients from time segments segmented through the Mood test and clusters through Bayesian GMM for soft regime weights, using XGBoost to predict regime probabilities. Models were evaluated across three time periods--before, during, and after the COVID-19 pandemic. The coefficient-based clustering algorithm outperformed all other models, including the baseline autoregressive model, during all time periods. Additionally, each model was evaluated on its recursive forecasting performance for 5- and 10-day horizons during each time period. The findings of this study demonstrate the value of regime-aware modeling frameworks and soft clustering approaches in improving volatility forecasting, especially during periods of heightened uncertainty and structural change. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.03236 |
| By: | Falk Bräuning; Joanna Stavins |
| Abstract: | Monetary policy impacts consumer spending via the effect of interest rate changes on credit card borrowing. Using supervisory account-level spending and balance data, we estimate that a 1 percentage point increase in the interest rate reduces credit card spending by nearly 9 percent and revolving balances by close to 4 percent. Aggregate results are primarily driven by revolving accounts, while we estimate small and statistically insignificant interest-rate elasticity for transaction accounts. Consistent with financial constraints, low-credit-score accounts tend to adjust spending, while high-credit-score accounts adjust balances. |
| Keywords: | credit cards; interest rates; consumer spending |
| JEL: | D12 D14 E43 G21 |
| Date: | 2025–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:101889 |
| By: | Bertie Vidgen; Abby Fennelly; Evan Pinnix; Chirag Mahapatra; Zach Richards; Austin Bridges; Calix Huang; Ben Hunsberger; Fez Zafar; Brendan Foody; Dominic Barton; Cass R. Sunstein; Eric Topol; Osvald Nitski |
| Abstract: | We introduce the first version of the AI Productivity Index (APEX), a benchmark for assessing whether frontier AI models can perform knowledge work with high economic value. APEX addresses one of the largest inefficiencies in AI research: outside of coding, benchmarks often fail to test economically relevant capabilities. APEX-v1.0 contains 200 test cases and covers four domains: investment banking, management consulting, law, and primary medical care. It was built in three steps. First, we sourced experts with top-tier experience e.g., investment bankers from Goldman Sachs. Second, experts created prompts that reflect high-value tasks in their day-to-day work. Third, experts created rubrics for evaluating model responses. We evaluate 23 frontier models on APEX-v1.0 using an LM judge. GPT 5 (Thinking = High) achieves the highest mean score (64.2%), followed by Grok 4 (61.3%) and Gemini 2.5 Flash (Thinking = On) (60.4%). Qwen 3 235B is the best performing open-source model and seventh best overall. There is a large gap between the performance of even the best models and human experts, highlighting the need for better measurement of models' ability to produce economically valuable work. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.25721 |
| By: | Ragasa, Catherine; Ma, Ning; Hami, Emmanuel |
| Abstract: | Many rural producer groups face poor management practices, low productivity, and weak market linkages. An information and communication technology (ICT)-based intervention bundle was provided to producer groups to transform them into ICT hubs, where members learn about and adopt improved management practices and increase their productivity and incomes. The intervention bundle includes phone messages and videos, promotion of the call center/hotline, and facilitation of radio listening clubs and collective marketing. The study, a cluster-randomized controlled trial, randomly assigned 59 groups into treatment groups and 59 into control groups. After 18 months of interventions, results show positive but small impact on crop sales (USD65 per household) and no impact on productivity. The income effect was mainly from Kasungu and Nkhota-kota, which experienced increased production and sales of rice, soybean, and groundnut and received higher prices due to collective marketing. Farmers in Kasungu and Nkhota-kota improved a few agricultural management practices, while farmers in other districts did not improve their management practices. Results show more farmers accessing phone messaging on agriculture and markets, greater awareness and use of the call center, more listening groups established, and more farmers—especially women—joining these groups. Nevertheless, coverage and uptake remain very low, which are likely reasons for the limited impact. |
| Keywords: | markets; Information and Communication Technologies; digital agriculture; digital extension tools; impact assessment; sales; productivity; agriculture; Malawi; Africa; Eastern Africa |
| Date: | 2024–06–30 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:148814 |
| By: | Okan Akarsu; Altan Aldan; Unal Seven |
| Abstract: | [EN] This study examines how firms’ perceptions of their business environment and expectations of their own future sales shape their inflation expectations, with particular attention to differences across inflation regimes. Using firm-level data from Türkiye’s Business Tendency Survey (BTS), we find that firms with a weaker assessment of their industry’s current state tend to forecast higher inflation, particularly during periods of heightened uncertainty. In high-inflation environments, firms’ inflation expectations are more strongly associated with their perceptions of current industry conditions, whereas expectations about their own future sales play a comparatively smaller role. Conversely, firms' expectations for their own sales become a more decisive factor in their inflation forecasts during low-inflation or disinflationary periods. These results highlight the state-dependent nature of firms’ inflation expectation formation. By shedding light on the mechanisms through which firms form expectations, this note underscores how reducing macroeconomic uncertainty and strengthening policy communication can enhance the effectiveness of monetary policy. [TR] Bu calisma, firmalarin sektorel duruma iliskin algilarinin ve satis beklentilerinin enflasyon beklentilerini nasil sekillendirdigini, farkli enflasyon rejimleri altinda incelemektedir. Iktisadi Yonelim Anketi (IYA) verilerini kullanan analizimiz, sektorlerinin mevcut durumuna iliskin gorece daha olumsuz beklentiler icinde olan firmalarin, ozellikle belirsizligin arttigi donemlerde, diger firmalara gore daha yuksek enflasyon tahmininde bulunma egiliminde olduklarini gostermektedir. Yuksek enflasyon ortaminda, firmalarin enflasyon beklentileri, sektorlerinin mevcut kosullarina iliskin algilariyla daha guclu bir sekilde iliskilidir; buna karsilik, gelecekteki satislarina iliskin beklentilerinin enflasyon bekleyisleri uzerine etkisi daha sinirli kalmaktadir. Buna karsin, dusuk enflasyon veya dezenflasyon donemlerinde, firmalarin satis beklentileri enflasyon tahminlerinde daha belirleyici bir faktor haline gelmektedir. Bu bulgular, firmalarin enflasyon beklenti olusumunun duruma bagli bir yapiya sahip oldugunu ortaya koymaktadir. Firmalarin beklenti olusturma mekanizmalarina isik tutan bu calisma, makroekonomik belirsizligin azaltilmasi ve politika iletisiminin guclendirilmesinin para politikasinin etkinligini artirmada onemli bir rol oynayabilecegini vurgulamaktadir. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:econot:2521 |
| By: | Peiyun Jiang; Takashi Yamagata |
| Abstract: | In this paper, we propose a novel bootstrap algorithm that is more efficient than existing methods for approximating the distribution of the factor-augmented regression estimator for a rotated parameter vector. The regression is augmented by $r$ factors extracted from a large panel of $N$ variables observed over $T$ time periods. We consider general weak factor (WF) models with $r$ signal eigenvalues that may diverge at different rates, $N^{\alpha _{k}}$, where $0 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.00947 |
| By: | Gabriela Wojak; Ernest G\'orka; Micha{\l} \'Cwi\k{a}ka{\l}a; Dariusz Baran; Rafa{\l} \'Swiniarski; Katarzyna Olszy\'nska; Piotr Mrzyg{\l}\'od; Maciej Frasunkiewicz; Piotr R\k{e}czajski; Daniel Zawadzki; Jan Piwnik |
| Abstract: | This paper examines how Polish consumers are adapting to online insurance purchasing channels and what factors influence their preferences. Drawing on a structured survey of 100 respondents with varied demographic profiles, the study explores purchasing frequency, channel usage, price sensitivity, trust, and decision-making behaviors. Results indicate a clear shift toward digital tools, with many consumers valuing the speed, convenience, and transparency of online platforms, particularly for simple insurance products. However, barriers remain, including concerns about data security, lack of personal guidance, and difficulty understanding policy terms. A hybrid model is emerging, where online tools are used for research and comparison, while traditional agents are consulted for complex decisions. Respondents emphasized the importance of trust and personal contact, showing that emotional and psychological factors still play a role in digital adoption. Price was the dominant decision factor, but many consumers also prioritized service quality and reliability. The study concludes that insurers should invest in user-friendly digital experiences while maintaining human support options. Strategic omnichannel integration is recommended to meet diverse customer needs and reduce digital exclusion. Limitations of the study include a modest sample size and focus on the Polish market. Future research should investigate the role of AI in digital distribution, segment preferences by insurance type, and analyze trends across different regions or age groups. This paper adds empirical value to the understanding of insurance distribution and consumer behavior in digitally transforming financial markets. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.07933 |
| By: | Shota Ichihashi (Department of Economics, Queen's University, Kingston, ON, Canada) |
| Abstract: | I study a model of platform-enabled algorithmic pricing. Sellers offer identical products, to which consumers have heterogeneous values. Sellers can post a uniform price outside the platform or join the platform and delegate their pricing decision to the platform's algorithm. I show that the platform can offer a pricing algorithm to attract sellers, stifle off-platform competition, and earn a positive profit. Prohibiting the platform from using consumer data for its algorithm increases consumer surplus but decreases total surplus. A transparency requirement, which mandates the platform to share its data and algorithms with sellers, restores the first-best outcome for consumers. |
| Keywords: | price discrimination, algorithmic pricing, competition, collusion, algorithm |
| JEL: | D43 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:net:wpaper:2503 |
| By: | Massimo Motta; Michele Polo |
| Abstract: | The paper analyzes the design of industrial policies, in the form of sub- sidies to innovation activity or to local production, when domestic firms are inefficient and there is a risk of supply-chain disruption. We forst es- tablish a case for research subsidies, since private investment (to improve the inferior technology) is lower than the socially optimal one. We next show the equivalence with subsidies to (inecient) local production in case of intertemporal economies of scale. Then, within a general frame- work, we analyze profit and welfare maximizing investments and optimal subsidies in case of segmented markets and an integrated market orga- nized as a duopoly, a monopoly or a research joint-venture. We show that research joint ventures or a public research center socially outperform the other environments since they benefit from a larger integrated market and a wider circulation of the innovation while preserving a competitive mar- ket. Finally, in large markets with significant technology gaps, it may be convenient to concentrate all the research in a single lab while maintaining a competitive market. |
| Keywords: | Resilience, industrial policy |
| JEL: | L40 L52 O31 O32 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:upf:upfgen:1895 |
| By: | Kurter, Zeynep O. (University of Warwick; Department of Economics); Bhatti, Balaaj (University of Warwick; Department of Economics) |
| Abstract: | While artificial intelligence (AI) has become increasingly prevalent, empirical evidence on its impacton firm value is limited. This inaugural UK market study uses event study methodology to assess stock market reactions to AI investment announcements by FTSE 100 companies from 2019-2023. Analysing 138 announcements from 53 companies, the research reveals that AI investments have a marginally positive, but statistically insignificant impact of 0.114% on the announcement day, affirmed by both parametric and non-parametric tests. Further subsample analysis shows that high credit rating firms and early adopters experience significantly negative impacts on firm value, indicating investor risk-aversion and tentative evidence of a second-mover advantage. Crosssectional analysis demonstrates that industry and the type of AI investment critically influence returns, and confirms the size effect with larger firms experiencing more negative returns than smaller ones. Earnings before interest, taxes and amortization (EBITDA) margins and cyber risk ratings, however, do not significantly impact returns. This study advances AI literature by examining market dynamics associated with AI investments, providing a foundation for future research, and providing practical insights for investors and corporate managers aiming to maximize risk-adjusted returns and firm value |
| Keywords: | Artificial intelligence ; AI ; firm value ; event study ; abnormal returns ; United Kingdom JEL classification: G11 ; G14 ; O33 ; M21 ; L1 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:warwec:1581 |
| By: | Alberto Galasso; El Hadi Caoui |
| Abstract: | Creative content is often the product of collaboration, which may lead to fractional ownership of intellectual property. We study the effect of fractional ownership on the licensing of copyrighted material and its reuse. To do so, we compile new data on the copyright ownership structure of songs and their licensing for use in movies. We document that fractional song ownership has increased substantially: the mean number of songwriters and publishers per song has tripled between 1958 and 2021. We show that, conditional on a rich set of controls, greater fractionalization is associated with lower likelihood of licensing. We leverage the Sony-led acquisition of EMI Music Publishing in 2012 to obtain within-song variation in ownership and find that consolidating ownership rights significantly increases licensing, beyond any standalone effects of the merger. |
| JEL: | K11 L8 O32 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34336 |
| By: | Taiki Wakatsuki; Kiyoshi Kanazawa |
| Abstract: | The Santa Fe model is an established econophysics model for describing stochastic dynamics of the limit order book from the viewpoint of the zero-intelligence approach. While its foundation was studied by combining a dimensional analysis and a mean-field theory by E. Smith et al. in Quantitative Finance 2003, their arguments are rather heuristic and lack solid mathematical foundation; indeed, their mean-field equations were derived with heuristic arguments and their solutions were not explicitly obtained. In this work, we revisit the mean-field theory of the Santa Fe model from the viewpoint of kinetic theory -- a traditional mathematical program in statistical physics. We study the exact master equation for the Santa Fe model and systematically derive the Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchical equation. By applying the mean-field approximation, we derive the mean-field equation for the order-book density profile, parallel to the Boltzmann equation in conventional statistical physics. Furthermore, we obtain explicit and closed expression of the mean-field solutions. Our solutions have several implications: (1)Our scaling formulas are available for both $\mu\to 0$ and $\mu\to \infty$ asymptotics, where $\mu$ is the market-order submission intensity. Particularly, the mean-field theory works very well for small $\mu$, while its validity is partially limited for large $\mu$. (2)The ``method of image'' solution, heuristically derived by Bouchaud-M\'ezard-Potters in Quantitative Finance 2002, is obtained for large $\mu$, serving as a mathematical foundation for their heuristic arguments. (3)Finally, we point out an error in E. Smith et al. 2003 in the scaling law for the diffusion constant due to a misspecification in their dimensional analysis. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.01814 |
| By: | Meera Mahadevan; Adrian Martinez; Ryan McCord; Robyn Meeks; Manisha Pradhananga |
| Abstract: | A central challenge in the global transition to cleaner energy is how governments can design policies that deliver large social benefits while facing trade-offs in energy security, fiscal costs, and household adoption frictions. We study this question in urban Nepal, where cooking is dominated by imported LPG, but abundant hydropower makes both large-scale electrification and improved energy security feasible. We embed household adoption decisions in a model of a planner balancing fiscal, fuel supply, and energy-security considerations, and estimate its key parameters using a scalable randomized controlled trial in Kathmandu Valley. Subsidies had large effects, increasing electric stove adoption by 23 percentage points and compatible cookware purchases by 41 percentage points. In contrast, information treatments highlighting cost or health benefits alone had little impact. Using detailed survey and electricity billing data, we find substitution away from LPG toward electricity, with meaningful household heterogeneity. Disciplined by these experimental estimates, the model evaluates counterfactual targeting rules, and estimates optimal subsidy levels under different macroeconomic conditions. |
| Keywords: | electrification, energy transition, technology adoption, policy design, development, climate change, energy security |
| JEL: | C93 O1 O33 Q48 Q56 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12190 |
| By: | Martin, Melissa; Timmermans, Oscar |
| Abstract: | Relative performance evaluation has become an increasingly common component of executive compensation contracts. We study how these incentive plans relate to corporate disclosure and predict that they introduce an incremental disclosure cost. This cost arises because disclosures can help competitors make better investment decisions, enhancing their performance and thereby reducing managers’ expected compensation. Consistent with this prediction, we find a negative association between relative performance plans and voluntary, value-relevant management forecasts, alongside a positive association with redactions in mandatory filings. This pattern is specific to plans with accounting-based metrics and absent for plans with price-based metrics. The results for price-based metrics are consistent with the idea that the incentive to reduce information asymmetry with market participants outweighs disclosure costs in these plans. The results for accounting-based metrics are more pronounced for managers whose plans provide stronger incentives and for those whose forecasts provide meaningful information spillovers to peers. Overall, this paper contributes the idea that relative performance plans can impose disclosure costs, thereby shedding light on contracting mechanisms that discourage disclosure—a less well-understood aspect of disclosure research. |
| Keywords: | information disclosure; capital markets; relative performance evaluation; proprietary costs |
| JEL: | D82 J33 L10 M12 |
| Date: | 2025–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127506 |
| By: | Teng Liu; Brook Constantz; Galina Hale; Michael Beck |
| Abstract: | Measuring the financial value of nature is difficult, often resulting in insufficient funding directed to nature conservation and restoration. As coastal risks increase from development and climate change, a tangible benefit of nature is the protection it offers against storm damages. Many studies from the risk industry and others assess the direct effects of wetlands for reducing damage during storms. However, the value of wetlands for coastal protection could extend to many other benefits, including home prices in areas where storms are common. We use property-level housing transaction data from Zillow and show that proximity to mangroves in Florida moderates home price decline and dispersion following major hurricanes. The effects are substantial in magnitude, reducing the probability of losing a quarter or more of housing value by 2-7 percentage points, which corresponds to 20-40-thousand-dollar value for a million-dollar property, conditional on a hurricane. |
| JEL: | G12 Q54 R31 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34329 |
| By: | Anand Chopra; Malachy James Gavan; Antonio Penta |
| Abstract: | Safe Implementation (Gavan and Penta, 2025) combines standard implementation with the requirement that the implementing mechanism is such that, if up to k agents deviate from the relevant solution concept, the outcomes that are induced are still ‘acceptable’ at every state of the world. In this paper, we study Safe Implementation of social choice correspondences in mixed Nash Equilibrium. We identify a condition, Set-Comonotonicity, which is both necessary and (under mild domain restrictions) almost sufficient for this implementation notion. |
| Keywords: | Implementation, mechanism design, robustness, safe implementation, mixed implementation, Set-Comonotonicity |
| JEL: | C72 D82 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:upf:upfgen:1911 |
| By: | Cecilia Aubrun; Michael Benzaquen; Jean-Philippe Bouchaud |
| Abstract: | This is the second part of our work on Multivariate Quadratic Hawkes (MQHawkes) Processes, devoted to the calibration of the model defined and studied analytically in Aubrun, C., Benzaquen, M., & Bouchaud, J. P., Quantitative Finance, 23(5), 741-758 (2023). We propose a non-parametric calibration method based on the general method of moments applied to a coarse-grained version of the MQHawkes model. This allows us to bypass challenges inherent to tick by tick data. Our main methodological innovation is a multi-step calibration procedure, first focusing on ''self'' feedback kernels, and then progressively including cross-effects. Indeed, while cross-effects are significant and interpretable, they are usually one order of magnitude smaller than self-effects, and must therefore be disentangled from noise with care. For numerical stability, we also restrict to pair interactions and only calibrate bi-variate QHawkes, neglecting higher-order interactions. Our main findings are: (a) While cross-Hawkes feedback effects have been empirically studied previously, cross-Zumbach effects are clearly identified here for the first time. The effect of recent trends of the E-Mini futures contract onto the volatility of other futures contracts is especially strong; (b) We have identified a new type of feedback that couples past realized covariance between two assets and future volatility of these two assets, with the pair E-Mini vs TBOND as a case in point; (c) A cross-leverage effect, whereby the sign of the return of one asset impacts the volatility of another asset, is also clearly identified. The cross-leverage effect between the E-Mini and the residual volatility of single stocks is notable, and surprisingly universal across the universe of stocks that we considered. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.21244 |
| By: | International Monetary Fund |
| Abstract: | Growth accelerated in 2024 supported by a rebound in exports and accommodative policies. However, the successful export-led growth model faces significant new challenges from a more adverse and uncertain global trade environment. The authorities are undertaking a major institutional restructuring, and planning reforms and scaling-up public investment to boost medium-term growth. |
| Date: | 2025–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfscr:2025/283 |
| By: | Oliver Slumbers; Benjamin Patrick Evans; Sumitra Ganesh; Leo Ardon |
| Abstract: | Game theory has traditionally had a relatively limited view of risk based on how a player's expected reward is impacted by the uncertainty of the actions of other players. Recently, a new game-theoretic approach provides a more holistic view of risk also considering the reward-variance. However, these variance-based approaches measure variance of the reward on both the upside and downside. In many domains, such as finance, downside risk only is of key importance, as this represents the potential losses associated with a decision. In contrast, large upside "risk" (e.g. profits) are not an issue. To address this restrictive view of risk, we propose a novel solution concept, downside risk aware equilibria (DRAE) based on lower partial moments. DRAE restricts downside risk, while placing no restrictions on upside risk, and additionally, models higher-order risk preferences. We demonstrate the applicability of DRAE on several games, successfully finding equilibria which balance downside risk with expected reward, and prove the existence and optimality of this equilibria. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.03446 |
| By: | Marie Briere; Léopold Simar; Ariane Szafarz; Anne Vanhems |
| Abstract: | This paper presents a novel performance test for investment portfolios by constructing bootstrap confidence intervals around the distance to the efficient frontier of risky assets. Using a general input-output framework, with outputs like return and skewness, and inputs such as variance and kurtosis, our distance measure quantifies efficiency loss relative to a personalized efficient benchmark aligned with each investor’s risk preferences. We estimate the efficient frontier accounting for random asset return variations and apply subsampling to derive confidence intervals for the distances. In our empirical illustration, we evaluate ‘decarbonized’ portfolios that exclude the most polluting firms from the S&P 500, considering four distinct investor types: those aiming to maximize return, minimize variance, maximize skewness, or balance these objectives. Results show that investors prioritizing return, skewness, or balanced criteria can decarbonize without significant efficiency loss. In contrast, those focused on minimizing variance face larger performance declines. Moreover, the portfolio closest to the efficient frontier varies according to investor preferences, highlighting the importance of personalizing performance metrics to individual investment goals. |
| Keywords: | Portfolio Choice; Personalization; Performance Measure; Random Inputs and Outputs; Nonparametric Estimator; Subsampling; Efficient Frontier |
| JEL: | C44 C12 C67 G11 G14 |
| Date: | 2025–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:sol:wpaper:2013/394786 |
| By: | Adam Crowe (School of Accounting, Economics and Finance, Curtin Business School); Alan S Duncan (Bankwest Curtin Economics Centre (BCEC), Curtin University); Steven Rowley (School of Accounting, Economics and Finance, Curtin Business School) |
| Abstract: | Housing has emerged as one of the most urgent and complex challenges facing Western Australia. From surging rents to escalating house prices, the state’s housing market is being stretched to its limits, placing acute pressure on households, communities and service providers alike. The issue is not simply one of affordability, but of access, equity and sustainability. Wherever you turn, the signs of a housing system under strain are impossible to ignore. This latest Housing Affordability in Western Australia 2025 report provides a comprehensive and evidence-based response to WA’s housing situation, offering critical insights into homelessness and housing needs. It provides a fresh analysis of the supply and demand dynamics and the changing geography of WA’s rental stock supply, bringing new data to understand better the real cost pressures and housing circumstances faced by families across the country. The report highlights that WA’s housing market is not keeping pace with the state’s record population growth. While completions have reached a seven-year high, the shortfall remains in the thousands, tightening supply, driving up costs, and locking out a growing number of people from home ownership or secure rental. For renters, especially those on low incomes or in single-parent households, the challenge is acute. And homelessness is becoming both more entrenched and more widespread, with rising demand from women, older people, and even those in full-time work. |
| Keywords: | housing supply, housing demand, urban planning, construction costs, housing affordability, rental stress, housing costs, rental bonds, population growth |
| JEL: | L74 R3 N67 R21 R31 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:ozl:bcecrs:fwa19 |