International Journal of Forecasting最新文献

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Real-time monitoring procedures for early detection of bubbles 实时监测程序，尽早发现气泡

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-27 DOI: 10.1016/j.ijforecast.2024.12.005

E.J. Whitehouse , D.I. Harvey , S.J. Leybourne

Asset price bubbles and crashes can have severe consequences for the stability of financial and economic systems. Policymakers require timely identification of such bubbles in order to respond to their emergence. In this paper we propose new econometric procedures that improve the speed of detection for an emerging asset price bubble in real time. Our new monitoring procedures make use of alternative variance standardisations that are better able to capture the behaviour of the underlying process during a bubble phase. We derive asymptotic results to show that using these alternative variance standardisations does not increase the probability of false detection under the no-bubble (unit root) null hypothesis relative to existing procedures. However, Monte Carlo simulations demonstrate that much earlier detection becomes possible with our new procedures under the bubble (explosive autoregressive) alternative. Empirical applications to OECD housing markets and bitcoin prices show the value in terms of earlier detection of bubbles that our new procedures can achieve. In particular, we show that the United States housing bubble that preceded the global financial crisis could have been detected as early as 1999:Q1 by our new procedures.

资产价格泡沫和崩溃可能对金融和经济体系的稳定造成严重后果。政策制定者需要及时识别此类泡沫，以便对其出现做出反应。在本文中，我们提出了新的计量经济学程序，以提高对新兴资产价格泡沫的实时检测速度。我们的新监测程序利用了可选择的方差标准化，它能够更好地捕获在泡沫阶段的底层过程的行为。我们推导出渐近结果，表明使用这些替代方差标准化并不会增加在无气泡（单位根）零假设下误检的概率。然而，蒙特卡罗模拟表明，在气泡（爆炸自回归）替代方案下，我们的新程序可以更早地检测到。对经合组织住房市场和比特币价格的实证应用表明，我们的新程序在早期发现泡沫方面可以实现价值。特别是，我们表明，在全球金融危机之前的美国房地产泡沫可以早在1999年第一季度就被我们的新程序发现。

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引用次数: 0

Time-varying parameters as ridge regressions 时变参数作为脊回归

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-27 DOI: 10.1016/j.ijforecast.2024.08.006

Philippe Goulet Coulombe

Time-varying parameter (TVP) models are frequently used in economics to capture structural change. I highlight a rather underutilized fact—that these are actually ridge regressions. Instantly, this makes computations, tuning, and implementation much easier than in the state-space paradigm. Among other things, solving the equivalent dual ridge problem is computationally very fast even in high dimensions, and the crucial ‘amount of time variation’ is tuned by cross-validation. Evolving volatility is dealt with using a two-step ridge regression. I consider extensions that incorporate sparsity (the algorithm selects which parameters vary and which do not) and reduced-rank restrictions (variation is tied to a factor model). To demonstrate the usefulness of the approach, I use it to study the evolution of monetary policy in Canada using large time-varying local projections and TVP-VARs with demanding lag lengths. The applications require the estimation of up to 4600 TVPs, a task within the reach of the new method.

时变参数（TVP）模型在经济学中经常用于捕捉结构变化。我强调一个未被充分利用的事实，即这些实际上是脊回归。这立即使计算、调优和实现比状态空间范式容易得多。除此之外，即使在高维情况下，解决等效双脊问题的计算速度也非常快，关键的“时间变化量”是通过交叉验证来调整的。使用两步脊回归处理不断变化的波动率。我考虑了包含稀疏性（算法选择哪些参数变化，哪些参数不变化）和降阶限制（变化与因子模型相关联）的扩展。为了证明该方法的实用性，我用它来研究加拿大货币政策的演变，使用大型时变本地预测和要求滞后长度的tvp -var。应用程序需要估计多达4600个TVPs，这是新方法可以达到的任务。

引用次数: 0

Predicting the relative performance among financial assets: A comparative analysis of different approaches 预测金融资产的相对表现：不同方法的比较分析

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-24 DOI: 10.1016/j.ijforecast.2024.12.008

Panagiotis Samartzis

We perform a comparative analysis of a wide array of approaches for the problem of forecasting the relative performance among different tradable assets in the framework of the M6 competition. To produce the forecasts, we employ various models spanning probabilistic, classification, and time-series methods, each approaching the problem from a different perspective. We demonstrate that in the case of financial forecasting, simple machine learning approaches have better performance compared to more complex deep-learning models. Furthermore, approaching the problem as a classification task appears to be beneficial. We also confirm findings from existing literature that using simple ensemble techniques can improve performance, and that forecasting performance is better for exchange-traded funds and assets that have lower idiosyncratic volatility. Finally, we benchmark our results against the performance of teams that participated in the M6 competition.

我们对M6竞争框架下预测不同可交易资产之间相对表现的各种方法进行了比较分析。为了产生预测，我们采用了各种模型，包括概率、分类和时间序列方法，每个模型都从不同的角度来处理问题。我们证明，在财务预测的情况下，简单的机器学习方法比更复杂的深度学习模型具有更好的性能。此外，将问题作为分类任务来处理似乎是有益的。我们也证实了现有文献的发现，即使用简单的集成技术可以提高绩效，并且对具有较低特质波动率的交易所交易基金和资产的预测绩效更好。最后，我们将我们的结果与参加M6竞赛的团队的表现进行比较。

引用次数: 0

Predicting value at risk for cryptocurrencies with generalized random forests 用广义随机森林预测加密货币的风险值

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-24 DOI: 10.1016/j.ijforecast.2024.12.002

Rebekka Buse , Konstantin Görgen , Melanie Schienle

We study the prediction of value at risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that generalized random forests (GRF) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type models, and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns, and clearly superior in the cryptocurrency setup.

我们研究了加密货币的风险价值（VaR）预测。与传统资产相比，加密货币的回报往往波动很大，其特点是围绕单一事件出现大幅波动。通过对105种主要加密货币的综合分析，我们发现适应分位数预测的广义随机森林（GRF）比其他已建立的方法（如分位数回归、garch型模型和CAViaR模型）具有更好的性能。这种优势在不稳定时期和高度波动的加密货币类别中尤为明显。此外，我们确定了这些时期的重要预测因素，并显示了它们随时间对预测的影响。此外，一项全面的模拟研究表明，GRF方法至少与标准类型财务回报的VaR预测方法相当，并且在加密货币设置中明显优于现有方法。

引用次数: 0

How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts 培训如何提高个人预测？地缘政治预测中补偿性和非补偿性偏差的建模差异

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-22 DOI: 10.1016/j.ijforecast.2024.12.001

Vahid Karimi Motahhar , Thomas S. Gruca

Biases in human forecasters lead to poor calibration. We assess how formal training affects two types of bias in probabilistic forecasts of binary outcomes. Compensatory bias occurs when underestimation in one range of probabilities (e.g., less than 50%) is accompanied by overestimation in the opposite range. Non-compensatory bias occurs when the direction of misestimation is consistent throughout the entire range of probabilities. We present a new approach to modeling probabilistic forecasts to determine the extent and direction of compensatory and non-compensatory biases. Using data from the Good Judgment Project, we model the effects of training (randomly assigned) on the calibration of 39,481 initial forecasts from 851 forecasters across two years of the contest. The forecasts exhibit significant indications of both compensatory and non-compensatory biases across all forecasters. Training significantly reduces the compensatory bias in both years. It reduces the non-compensatory bias only in the second year of the contest.

人类预报员的偏见导致了糟糕的校准。我们评估了正规训练如何影响二元结果的概率预测中的两种类型的偏差。当一个概率范围内的低估（例如，小于50%）伴随着相反范围内的高估时，就会出现补偿性偏差。当错误估计的方向在整个概率范围内一致时，就会发生非补偿性偏差。我们提出了一种新的方法来建模概率预测，以确定补偿和非补偿偏差的程度和方向。使用来自Good Judgment Project的数据，我们对训练（随机分配）对来自851名预报员的39,481个初始预测的校准的影响进行了建模。所有预测者的预测都显示出显著的补偿性和非补偿性偏差。在这两年中，培训显著减少了补偿性偏差。它只在比赛的第二年减少了非补偿性偏见。

引用次数: 0

Quasi-average predictions and regression to the trend: An application to the M6 financial forecasting competition 准平均预测与趋势回归：在M6财务预测竞赛中的应用

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-22 DOI: 10.1016/j.ijforecast.2024.12.006

Jose M.G. Vilar

This paper presents the winning method that achieved fifth place overall in the M6 financial forecasting competition. The method is based on the idea that, under the efficient market hypothesis, it is often more effective to predict values close to the expected averages of categories and trends than to try to make precise predictions. By leveraging low-variability prediction methods, we forecast both the relative performance of multiple assets and their optimal investment positions. We demonstrate that combining asset-class and temporal averages yields modest but consistent advantages over reference indices. The results highlight the challenges of achieving above-average returns in efficient markets and the potential benefits of low-variability prediction methods in such contexts.

本文介绍了在M6财务预测大赛中获得综合第五名的获胜方法。该方法基于这样一种观点，即在有效市场假说下，预测接近类别和趋势的预期平均值的值往往比试图做出精确的预测更有效。通过利用低变异性预测方法，我们预测了多种资产的相对表现及其最优投资头寸。我们证明，与参考指数相比，资产类别和时间平均相结合产生适度但一致的优势。研究结果强调了在有效市场中实现高于平均水平回报的挑战，以及在这种情况下低变异性预测方法的潜在好处。

引用次数: 0

Forecasting stock market return with anomalies: Evidence from China 用异常预测股市回报：来自中国的证据

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-21 DOI: 10.1016/j.ijforecast.2024.12.007

Jianqiu Wang , Zhuo Wang , Ke Wu

We empirically investigate the relation between anomaly portfolio returns and market return predictability in the Chinese stock market. Using 132 long-leg, short-leg, and long-short anomaly portfolio returns, we employ various shrinkage-based statistical learning methods to capture predictive signals of the anomalies in a high-dimensional setting. Our analysis reveals statistically and economically significant return predictability using long- and short-leg anomaly portfolio returns. Moreover, high arbitrage risk enhances forecasting performance, supporting that the predictability stems from mispricing correction persistence. Contrary to findings in the US stock market, we find little evidence that the long-short anomaly portfolios contribute to market return predictability in China, due to the low persistence of asymmetric mispricing corrections. We provide simulation evidence to justify the distinct prediction patterns for the US and Chinese stock markets.

本文对中国股票市场异常投资组合收益与市场收益可预测性之间的关系进行了实证研究。利用132个长腿、短腿和多空异常组合回报，我们采用各种基于收缩的统计学习方法来捕获高维环境下异常的预测信号。我们的分析揭示了使用长腿和短腿异常投资组合回报的统计和经济上显著的回报可预测性。此外，高套利风险增强了预测绩效，支持可预测性源于错误定价修正的持久性。与美国股市的研究结果相反，我们发现很少有证据表明多空异常投资组合有助于中国市场回报的可预测性，因为不对称错误定价修正的持久性较低。我们提供了模拟证据来证明美国和中国股市的不同预测模式。

引用次数: 0

Subjective-probability forecasts of existential risk: Initial results from a hybrid persuasion-forecasting tournament 存在风险的主观概率预测：混合劝说-预测比赛的初步结果

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-17 DOI: 10.1016/j.ijforecast.2024.11.008

Ezra Karger , Josh Rosenberg , Zachary Jacobs , Molly Hickman , Phillip E. Tetlock

A multi-stage persuasion-forecasting tournament asked specialists and generalists (“superforecasters”) to explain their probability judgments of short- and long-run existential threats to humanity. Specialists were more pessimistic, especially on long-run threats posed by artificial intelligence (AI). Despite incentives to share their best arguments during four months of discussion, neither side materially moved the other’s views. This would be puzzling if participants were Bayesian agents methodically sifting through elusive clues about distant futures but it is less puzzling if participants were boundedly rational agents searching for confirmatory evidence as the risks of embarrassing accuracy feedback receded. Consistent with the latter mechanism, strong AI-risk proponents made particularly extreme long- but not short-range forecasts and over-estimated the long-range AI-risk forecasts of others. We stress the potential of these methods to inform high-stakes debates, but we acknowledge limits on what even skilled forecasters can achieve in anticipating rare or unprecedented events.

一个多阶段的说服预测比赛要求专家和通才（“超级预测者”）解释他们对人类面临的短期和长期生存威胁的概率判断。专家们则更为悲观，尤其是对人工智能（AI）带来的长期威胁。尽管在四个月的讨论中，双方都有分享各自最佳观点的动机，但双方都没有实质性地改变对方的观点。如果参与者是系统地筛选关于遥远未来的难以捉摸的线索的贝叶斯代理，这将令人困惑；但如果参与者是有限理性的代理，随着令人尴尬的准确性反馈的风险消退，寻找确凿的证据，这就不那么令人困惑了。与后一种机制一致，强烈的人工智能风险支持者做出了特别极端的长期预测，而不是短期预测，并高估了其他人的长期人工智能风险预测。我们强调这些方法为高风险辩论提供信息的潜力，但我们承认，即使是熟练的预报员，在预测罕见或前所未有的事件方面所能取得的成就也是有限的。

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引用次数: 0

Stock return predictability in the frequency domain 股票收益在频域的可预测性

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-16 DOI: 10.1016/j.ijforecast.2024.11.007

Zhifeng Dai , Fuwei Jiang , Jie Kang , Bowen Xue

This paper investigates the role of time–frequency information in dimension reduction prediction of stock returns. Using the long-term wavelet component of monthly S&P500 excess returns as supervision, we employ a machine learning method to extract the common predictive factor from prevalent macroeconomic variables and construct a new macroeconomic index aligned with stock return prediction. The macroeconomic index exhibits significant predictive power, both in and out of sample, at the market and portfolio levels. It outperforms all individual macroeconomic predictors and the factors based on higher frequency information of realized returns. Our findings demonstrate substantial economic value of the new index in asset allocation. Moreover, we also observe a complementary relation between macroeconomic index and investor sentiment. The predictive power is most pronounced during high-economic-uncertainty periods when investors are likely to underreact to fundamental signals and stems from cash flow predictability channel.

本文研究了时频信息在股票收益降维预测中的作用。以标普500指数月度超额收益的长期小波分量为监督，采用机器学习方法从主流宏观经济变量中提取共同预测因子，构建与股票收益预测相一致的新宏观经济指标。宏观经济指数在样本内外、市场和投资组合水平上均表现出显著的预测能力。它优于所有个体宏观经济预测指标和基于实现收益高频信息的因素。我们的研究结果表明，新指数在资产配置方面具有巨大的经济价值。此外，我们还观察到宏观经济指数与投资者情绪之间存在互补关系。这种预测能力在经济高度不确定性时期最为明显，此时投资者可能对基本面信号反应不足，并且源于现金流可预测渠道。

引用次数: 0

Modeling and predicting failure in US credit unions 对美国信用合作社破产进行建模和预测

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-01-16 DOI: 10.1016/j.ijforecast.2024.12.004

Qiao Peng , Donal McKillop , Barry Quinn , Kailong Liu

This study presents a random forest (RF)-based machine learning model to predict the liquidation of US credit unions one year in advance. The model demonstrates impressive accuracy on the test set (97.9% accuracy, with 2.0% false negatives and 8.8% false positives) when utilizing all 44 factors. Simplifying the model to only the top five factors based on feature importance analysis results in a slightly lower, but still significant, accuracy on the test set (92.2% accuracy, with 7.8% false negatives and 17.6% false positives). Comparisons with seven other classification methods verify the superiority of the RF model. This study also uses the Cox proportional-hazards model and Shapley value-based approaches to interpret key feature significance and interactions. The model provides regulators and credit unions with a valuable early warning system for potential failures, enabling corrective measures or strategic mergers to ultimately protect the National Credit Union Share Insurance Fund.

本研究提出了一个基于随机森林（RF）的机器学习模型，以提前一年预测美国信用合作社的清算。当利用所有44个因素时，该模型在测试集上显示出令人印象深刻的准确性（97.9%的准确率，2.0%的假阴性和8.8%的假阳性）。将模型简化为基于特征重要性分析的前五个因素，测试集的准确率略低，但仍然显著（准确率为92.2%，假阴性为7.8%，假阳性为17.6%）。与其他7种分类方法的比较验证了射频模型的优越性。本研究还使用Cox比例风险模型和Shapley基于值的方法来解释关键特征的重要性和相互作用。该模型为监管机构和信用合作社提供了一个有价值的潜在失败预警系统，使纠正措施或战略合并能够最终保护国家信用合作社股份保险基金。

引用次数: 0

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International Journal of Forecasting

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