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on Forecasting |
| By: | Ruben Aag; Hilde C. Bjornland; Peder Eliassen |
| Abstract: | This paper compares forecasting approaches for oil and natural gas prices within a unified pseudo-real-time framework. While oil price forecasting is well established, natural gas markets remain less explored and are characterized by more regionalized and less globally integrated pricing. By adapting established oil forecasting models to natural gas, we systematically assess how differences in market structure shape model transferability and predictive accuracy. Forecast combinations consistently outperform individual models for both commodities, underscoring the value of model averaging. However, the forecast gains are considerably larger for natural gas, reflecting greater potential for improvement in a more localized market. Optimal weighting schemes also differ: equal weights dominate for oil, while performance-based weights yield superior accuracy for gas. Overall, the results demonstrate that forecasting performance is both commodity- and market-structure-dependent, offering new insights into reliable energy price prediction across global and regional markets. |
| Keywords: | oil and natural gas prices, forecasting, model combinations |
| JEL: | C32 F41 O47 Q3 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-54 |
| By: | Ta-Chung Chi (Kevin); Ting-Han Fan (Kevin); Raffaele M. Ghigliazza (Kevin); Domenico Giannone (Kevin); Zixuan (Kevin); Wang |
| Abstract: | We forecast the full conditional distribution of macroeconomic outcomes by systematically integrating three key principles: using high-dimensional data with appropriate regularization, adopting rigorous out-of-sample validation procedures, and incorporating nonlinearities. By exploiting the rich information embedded in a large set of macroeconomic and financial predictors, we produce accurate predictions of the entire profile of macroeconomic risk in real time. Our findings show that regularization via shrinkage is essential to control model complexity, while introducing nonlinearities yields limited improvements in predictive accuracy. Out-of-sample validation plays a critical role in selecting model architecture and preventing overfitting. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.11008 |
| By: | Basellini, Ugofilippo; Camarda, Carlo Giovanni |
| Abstract: | Accurate mortality forecasts are central to policy, insurance, and demographic research. Yet most existing approaches rely on age–period models, limiting their ability to capture the real experiences of birth cohorts. We address this gap by developing and evaluating new models for cohort mortality forecasting. Specifically, we extend the Lee–Carter framework with two cohort-specific variants and introduce Cohort Constrained P-splines (CCP-splines), a flexible method that embeds demographic constraints into a smoothing framework. Out-of-sample validation demonstrates that CCP-splines outperform not only the cohort LC variants but also the conventional diagonal Lexis approach, which derives cohort patterns from classic Lee–Carter age-period forecasts. Applications to eight populations confirm the advantages of CCP-splines, showing improved fit, reduced bias, and more reliable uncertainty estimates. By combining statistical rigour with demographic knowledge, this study provides a practical and flexible framework that positions CCP-splines as a new benchmark for cohort mortality forecasting. |
| Date: | 2025–10–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:8fxyk_v1 |
| By: | Ahad Yaqoob; Syed M. Abdullah |
| Abstract: | The application of deep learning models for stock price forecasting in emerging markets remains underexplored despite their potential to capture complex temporal dependencies. This study develops and evaluates a Long Short-Term Memory (LSTM) network model for predicting the closing prices of ten major stocks across diverse sectors of the Pakistan Stock Exchange (PSX). Utilizing historical OHLCV data and an extensive set of engineered technical indicators, we trained and validated the model on a multi-year dataset. Our results demonstrate strong predictive performance ($R^2 > 0.87$) for stocks in stable, high-liquidity sectors such as power generation, cement, and fertilizers. Conversely, stocks characterized by high volatility, low liquidity, or sensitivity to external shocks (e.g., global oil prices) presented significant forecasting challenges. The study provides a replicable framework for LSTM-based forecasting in data-scarce emerging markets and discusses implications for investors and future research. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.14401 |
| By: | Yun Lin; Jiawei Lou; Jinghe Zhang |
| Abstract: | Deep learning offers new tools for portfolio optimization. We present an end-to-end framework that directly learns portfolio weights by combining Long Short-Term Memory (LSTM) networks to model temporal patterns, Graph Attention Networks (GAT) to capture evolving inter-stock relationships, and sentiment analysis of financial news to reflect market psychology. Unlike prior approaches, our model unifies these elements in a single pipeline that produces daily allocations. It avoids the traditional two-step process of forecasting asset returns and then applying mean--variance optimization (MVO), a sequence that can introduce instability. We evaluate the framework on nine U.S. stocks spanning six sectors, chosen to balance sector diversity and news coverage. In this setting, the model delivers higher cumulative returns and Sharpe ratios than equal-weighted and CAPM-based MVO benchmarks. Although the stock universe is limited, the results underscore the value of integrating price, relational, and sentiment signals for portfolio management and suggest promising directions for scaling the approach to larger, more diverse asset sets. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.24144 |
| By: | Ross Koval; Nicholas Andrews; Xifeng Yan |
| Abstract: | Text and time series data offer complementary views of financial markets: news articles provide narrative context about company events, while stock prices reflect how markets react to those events. However, despite their complementary nature, effectively integrating these interleaved modalities for improved forecasting remains challenging. In this work, we propose a unified neural architecture that models these interleaved sequences using modality-specific experts, allowing the model to learn unique time series patterns, while still enabling joint reasoning across modalities and preserving pretrained language understanding capabilities. To further improve multimodal understanding, we introduce a cross-modal alignment framework with a salient token weighting mechanism that learns to align representations across modalities with a focus on the most informative tokens. We demonstrate the effectiveness of our approach on a large-scale financial forecasting task, achieving state-of-the-art performance across a wide variety of strong unimodal and multimodal baselines. We develop an interpretability method that reveals insights into the value of time series-context and reinforces the design of our cross-modal alignment objective. Finally, we demonstrate that these improvements translate to meaningful economic gains in investment simulations. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.19628 |
| By: | Lucía Cuadro-Sáez (BANCO DE ESPAÑA); Corinna Ghirelli (BANCO DE ESPAÑA); Maximiliano Moreno-López (BANCO DE ESPAÑA AND PARIS SCHOOL OF ECONOMICS); Javier J. Pérez (BANCO DE ESPAÑA) |
| Abstract: | This paper presents a comprehensive framework developed by the Banco de España to monitor and forecast food price dynamics in the euro area, particularly in response to the sharp increase in food inflation observed between 2022 and 2024. The study introduces a suite of models tailored to different aspects of the food value chain, integrating data from consumer and producer prices, farm-gate prices and international commodity and futures markets. Key tools include a Food Value Chain Model (VARx) to estimate the pass-through of commodity and fuel price shocks to consumer prices, an Asymmetric Price Transmission Model to capture non-linear effects and a Conditional Forecasting framework using different modelling approaches and futures data to simulate inflation scenarios. Additionally, a Vector Error Correction Model (VECM) assesses the long-term relationship between food and non-food prices. These tools aim to enhance central bank decision-making and food security analysis by providing timely, scenario-based insights into food inflation trends. |
| Keywords: | food prices, food inflation, inflation, euro area, monitoring, forecasting, central bank |
| JEL: | E31 C53 Q11 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:bde:opaper:2521e |
| By: | Haochuan (Kevin); Wang |
| Abstract: | Liquidity withdrawal is a critical indicator of market fragility. In this project, I test a framework for forecasting liquidity withdrawal at the individual-stock level, ranging from less liquid stocks to highly liquid large-cap tickers, and evaluate the relative performance of competing model classes in predicting short-horizon order book stress. We introduce the Liquidity Withdrawal Index (LWI) -- defined as the ratio of order cancellations to the sum of standing depth and new additions at the best quotes -- as a bounded, interpretable measure of transient liquidity removal. Using Nasdaq market-by-order (MBO) data, we compare a spectrum of approaches: linear benchmarks (AR, HAR), and non-linear tree ensembles (XGBoost), across horizons ranging from 250\, ms to 5\, s. Beyond predictive accuracy, our results provide insights into order placement and cancellation dynamics, identify regimes where linear versus non-linear signals dominate, and highlight how early-warning indicators of liquidity withdrawal can inform both market surveillance and execution. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.22985 |
| By: | Andrii Babii; Luca Barbaglia; Eric Ghysels; Jonas Striaukas |
| Abstract: | The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data. Using a panel of 19 Euro area countries, we investigate whether directly nowcasting the Euro area aggregate is better than weighted individual country nowcasts. Our results highlight the importance of the information from small- and medium-sized countries, particularly when including the COVID-19 pandemic period. The empirical analysis is supplemented by studying the so-called Big Four -- France, Germany, Italy, and Spain -- and the value added of news data when official statistics are lagging. From a theoretical perspective, we formally show that the aggregation of individual components forecasted with pooled panel data regressions is superior to direct aggregate forecasting due to lower estimation error. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.24780 |
| By: | Paponpat Taveeapiradeecharoen; Nattapol Aunsri |
| Abstract: | Forecasting inflation in small open economies is difficult because limited time series and strong external exposures create an imbalance between few observations and many potential predictors. We study this challenge using Thailand as a representative case, combining more than 450 domestic and international indicators. We evaluate modern Bayesian shrinkage and factor models, including Horseshoe regressions, factor-augmented autoregressions, factor-augmented VARs, dynamic factor models, and Bayesian additive regression trees. Our results show that factor models dominate at short horizons, when global shocks and exchange rate movements drive inflation, while shrinkage-based regressions perform best at longer horizons. These models not only improve point and density forecasts but also enhance tail-risk performance at the one-year horizon. Shrinkage diagnostics, on the other hand, additionally reveal that Google Trends variables, especially those related to food essential goods and housing costs, progressively rotate into predictive importance as the horizon lengthens. This underscores their role as forward-looking indicators of household inflation expectations in small open economies. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.14805 |
| By: | Jan Kwiatkowski; Jaros{\l}aw A. Chudziak |
| Abstract: | Quantitative trading strategies rely on accurately ranking stocks to identify profitable investments. Effective portfolio management requires models that can reliably order future stock returns. Transformer models are promising for understanding financial time series, but how different training loss functions affect their ability to rank stocks well is not yet fully understood. Financial markets are challenging due to their changing nature and complex relationships between stocks. Standard loss functions, which aim for simple prediction accuracy, often aren't enough. They don't directly teach models to learn the correct order of stock returns. While many advanced ranking losses exist from fields such as information retrieval, there hasn't been a thorough comparison to see how well they work for ranking financial returns, especially when used with modern Transformer models for stock selection. This paper addresses this gap by systematically evaluating a diverse set of advanced loss functions including pointwise, pairwise, listwise for daily stock return forecasting to facilitate rank-based portfolio selection on S&P 500 data. We focus on assessing how each loss function influences the model's ability to discern profitable relative orderings among assets. Our research contributes a comprehensive benchmark revealing how different loss functions impact a model's ability to learn cross-sectional and temporal patterns crucial for portfolio selection, thereby offering practical guidance for optimizing ranking-based trading strategies. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.14156 |
| By: | Eleonora Granziera; Vegard H. Larsen; Greta Meggiorini; Leonardo Melosi |
| Abstract: | We examine how speeches by Federal Open Market Committee (FOMC) members, including regional Fed presidents, shape private sector expectations. Speeches that signal rising inflationary pressures prompt both households and professional forecasters to raise their inflation expectations, consistent with Delphic effects. Only professional forecasters respond to Odyssean communications—statements about the Fed’s intended policy response—leaving Delphic effects as the dominant channel for households. These household responses are driven by speeches from regional presidents, likely due to greater visibility in regional media coverage. A general equilibrium model, featuring agents who differ in their ability to interpret Odyssean signals, explains this heterogeneity. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:bny:wpaper:0142 |
| By: | Zheng Cao; Xingran Shao; Yuheng Yan; Helyette Geman |
| Abstract: | We propose a novel model, the Hyped Log-Periodic Power Law Model (HLPPL), to the problem of quantifying and detecting financial bubbles, an ever-fascinating one for academics and practitioners alike. Bubble labels are generated using a Log-Periodic Power Law (LPPL) model, sentiment scores, and a hype index we introduced in previous research on NLP forecasting of stock return volatility. Using these tools, a dual-stream transformer model is trained with market data and machine learning methods, resulting in a time series of confidence scores as a Bubble Score. A distinctive feature of our framework is that it captures phases of extreme overpricing and underpricing within a unified structure. We achieve an average yield of 34.13 percentage annualized return when backtesting U.S. equities during the period 2018 to 2024, while the approach exhibits a remarkable generalization ability across industry sectors. Its conservative bias in predicting bubble periods minimizes false positives, a feature which is especially beneficial for market signaling and decision-making. Overall, this approach utilizes both theoretical and empirical advances for real-time positive and negative bubble identification and measurement with HLPPL signals. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.10878 |
| By: | Gondauri, Davit |
| Abstract: | The Gondauri Index (GI) is introduced as a novel macro-financial composite index, grounded in the Millennium Development Goals and applied to economic modeling. It integrates three sub-indices: the Inequality-Ricci Subindex (IRS), measuring income distribution stability through Ricci flow dynamics; the Liquidity-Navier-Stokes Resilience (LNSR), capturing systemic robustness via fluid dynamics analogies; and the Inflation FPAS+ζ Credibility (IFC), enhancing inflation forecasting through hybrid FPAS-Riemann zeta methods. Each subindex is normalized on a 0-100 scale, with the final GI computed as a weighted geometric mean (35% IRS, 35% LNSR, 30% IFC). The methodology combines statistical calibration, normalization, and error-reduction benchmarks to ensure reliability and policy applicability. The GI provides a consolidated, forward-looking metric for evaluating inequality, financial stability, and inflation expectations, offering policymakers and researchers a robust tool for decision-making in complex socioeconomic environments. |
| Keywords: | Gondauri Index, Ricci Flow, Navier-Stokes, Riemann Zeta, Inequality, Liquidity, Inflation Forecasting, Macro-Financial Index |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:328080 |
| By: | Yuntao Wu; Ege Mert Akin; Charles Martineau; Vincent Gr\'egoire; Andreas Veneris |
| Abstract: | We examine how textual features in earnings press releases predict stock returns on earnings announcement days. Using over 138, 000 press releases from 2005 to 2023, we compare traditional bag-of-words and BERT-based embeddings. We find that press release content (soft information) is as informative as earnings surprise (hard information), with FinBERT yielding the highest predictive power. Combining models enhances explanatory strength and interpretability of the content of press releases. Stock prices fully reflect the content of press releases at market open. If press releases are leaked, it offers predictive advantage. Topic analysis reveals self-serving bias in managerial narratives. Our framework supports real-time return prediction through the integration of online learning, provides interpretability and reveals the nuanced role of language in price formation. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.24254 |
| By: | Songrun He; Linying Lv; Asaf Manela; Jimmy Wu |
| Abstract: | We introduce a family of chronologically consistent, instruction-following large language models to eliminate lookahead bias. Each model is trained only on data available before a clearly defined knowledge-cutoff date, ensuring strict temporal separation from any post-cutoff data. The resulting framework offers (i) a simple, conversational chat interface, (ii) fully open, fixed model weights that guarantee replicability, and (iii) a conservative lower bound on forecast accuracy, isolating the share of predictability that survives once training leakage is removed. Together, these features provide researchers with an easy-to-use generative AI tool useful for a wide range of prediction tasks that is free of lookahead bias. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.11677 |
| By: | Andrew Foerster; Andreas Hornstein; Pierre-Daniel G. Sarte; Mark W. Watson |
| Abstract: | We explore the evolving significance of different production sectors within the U.S. economy since World War II and provide methods for estimating and forecasting these shifts. Using a compositional accounting approach, we find that the well-documented transition from goods to services is primarily driven by two compositional changes: 1) the rise of Intellectual Property Products (IPP) as an input producer, replacing Durable Goods almost one-for-one in terms of input shares in virtually all sectors; and 2) a shift in consumer spending from Nondurable Goods to Services. A structural model replicating these shifts reveals that the rise of IPP at the expense of Durable Goods is largely explained by increases in the efficiency of IPP inputs used in production: input-biased technical change. Trend variations in sectoral total factor productivity, and their attendant effects on relative prices and income, are the main driver of evolving consumption patterns. Both reduced-form and structural forecasts project these trends to continue over the next two decades, albeit at lower rates, indicating a slower pace of structural change. |
| Keywords: | production and investment |
| JEL: | E17 E23 E27 |
| Date: | 2025–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedrwp:101929 |
| By: | María Jesus Campion Arrastia; Emilio Dominguez Irastorza; Nuria Oses Eraso; Julen Perales Barriendo |
| Abstract: | The application of fertilisers is a fundamental aspect of soil fertility conservation. In recent decades, the growth in agricultural output has led to a significant increase in demand for fertilisers. The global fertiliser market is characterised by significant volatility and sensitivity to shifts in the global geopolitical landscape. In recent years, events such as the Russian aggression against Ukraine or export restrictions by the Chinese authorities have resulted in a considerable rise in prices and an increase in uncertainty about future behaviour. In this study, the prices of fertilisers typically available in Spain are employed to forecast not only the prices of fertilisers, but also those of essential macronutrients, namely nitrogen, phosphorus and potassium. Forecasting the prices of essential nutrients can inform the environmental assessment of human activities that modify the composition of soil nutrients. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:nav:ecupna:2402 |
| By: | Coenen, Günter; Mazelis, Falk; Motto, Roberto; Ristiniemi, Annukka; Smets, Frank; Warne, Anders; Wouters, Raf |
| Abstract: | This chapter of the Research Handbook of Inflation (2025) reviews the evolution and current relevance of medium-scale New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models, which serve as part of the core analytical framework in central banks and academic macroeconomics. The chapter assesses their capacity to analyse inflation dynamics, monetary transmission mechanisms, and policy interventions. Despite their exclusion of crisis-specific features, canonical models such as Smets and Wouters (2007) continue to explain inflation and output dynamics in the euro area and the US, owing in part to the differentiated effects of cost-push and demand shocks and the mitigating role of monetary policy. The chapter traces advancements in the European Central Bank’s New Area-Wide Model (NAWM), highlighting extensions that incorporate financial frictions, effective lower bounds, and energy price shocks. These enhancements have strengthened the model’s forecasting performance and interpretative power, especially during periods of unconventional monetary policy and energy-driven inflation. DSGE models are shown to be particularly effective for policy counterfactuals, enabling real-time assessments of policy decisions relative to model-based optimal policy. A robustness analysis under alternative scenarios demonstrates how policy rules can be evaluated through a welfare lens, informing the design of resilient monetary frameworks. Finally, the chapter identifies key modelling challenges exposed by recent inflation episodes and advocates for richer supply-side structures and nonlinear dynamics to improve the models’ capacity to capture complex macroeconomic developments. JEL Classification: E31, E32, E52, E58, C63 |
| Keywords: | effective lower bound, forecast evaluation, inflation dynamics, optimal policy, policy counterfactuals |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253137 |
| By: | Saeid Mashhadi; Amirhossein Saghezchi; Vesal Ghassemzadeh Kashani |
| Abstract: | This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter panel for 2010-2023 is constructed from Crunchbase and matched to U.S. Patent and Trademark Office (USPTO) data. Three horizons are evaluated: next funding within 12 months, patent-stock growth within 24 months, and exit through an initial public offering (IPO) or acquisition within 36 months. Preprocessing is fit on a development window (2010-2019) and applied without change to later cohorts to avoid leakage. Class imbalance is addressed using inverse-prevalence weights and the Synthetic Minority Oversampling Technique for Nominal and Continuous features (SMOTE-NC). Logistic regression and tree ensembles, including Random Forest, XGBoost, LightGBM, and CatBoost, are compared using the area under the precision-recall curve (PR-AUC) and the area under the receiver operating characteristic curve (AUROC). Patent, funding, and exit predictions achieve AUROC values of 0.921, 0.817, and 0.872, providing transparent and reproducible rankings for innovation finance. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.09465 |
| By: | Goldfayn-Frank, Olga; Kieren, Pascal; Trautmann, Stefan T. |
| Abstract: | Economists widely rely on measures of inflation expectations and uncertainty elicited via density forecasts. This method, where respondents assign probabilities to pre-specified ranges, has been subjected to criticism particularly in the recent times of high and volatile inflation. We propose a new method to elicit the full distribution of inflation expectations, which is rooted in decision theory and can be implemented in standard surveys. In two large surveys and one laboratory experiment, we demonstrate that it leads to well-defined expectations that fulfil both subjective and objective quality criteria. The method is neither perceived as more difficult nor does it take more time to complete compared to the current standard. In contrast to density forecasts, the method is robust to differences in the state of the economy and thus allows comparisons across time and across countries. The method is portable and can be applied to elicit different macroeconomic expectations. |
| Keywords: | Inflation expectations, measurement, macroeconomic beliefs, surveys |
| JEL: | D84 E31 E37 E71 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:328248 |
| By: | Onur Polat (Department of Public Finance, Bilecik Seyh Edebali University, Bilecik, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | This paper extends the discussion on the predictive role of bond market information over the stock market to a novel context by proposing a new predictor of stock market bubbles for the United States (US), namely the implied skewness of the Treasury yield. Using daily data from January 1988 to April 2025, we first implement the Multi-Scale Log-Period Power Law Confidence Indicator (MS-LPPLS-CI) framework to detect positive and negative bubbles at the short-, medium- and long-term. Next, employing a nonparametric causality-in-quantiles framework, we show that bond market signals inferred from the implied skewness of the Treasury yield carry significant predictive content for US and international stock market bubbles. While the predictive effect of Treasury yield skewness is found to be asymmetric across the short-, medium-, and long-term of the positive and negative bubble indicators, the strongest influence is observed at the lowest conditional quantiles of the bubble indicators, suggesting that bond market information captured by forward-looking skewness of interest rate implied by Treasury options can be used to forecast impending crashes in the stock market. These results hold when considering the remaining G7 and BRICS countries, providing support for the determinant role of interest rate signals by the Fed over risky asset dynamics in global stock markets and can be used by investors and policy authorities to have timely insights on imminent boom-bust cycles. |
| Keywords: | Multi-Scale Positive and Negative Bubbles; Stock Markets, Implied Skewness of the Treasury Yield, Nonparametric Causality-in-Quantiles Test, US, G7, and BRICS |
| JEL: | C22 G10 G12 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202539 |