Forecasting the Price of Carbon with Macroeconomic and Financial variables
University of Milan（米兰大学）；Fondazione Eni Enrico Mattei (FEEM)（埃尼·恩里科·马泰伊基金会）；Study Center Gerzensee, Foundation of the Swiss National Bank（瑞士国家银行格尔岑塞研究中心）；ClearBlue Markets
Andrea Bastianin, Elisabetta Mirto, Yan Qin, Luca Rossini
备注：
Accepted for publication in the Journal of Economic Dynamics and Control
We tackle the issue of producing point, sign, and density forecasts for the monthly real price of carbon in the European Union Emissions Trading System (EU ETS). We show that Bayesian Vector Autoregressive (BVAR) models augmented with factors based on macroeconomic and financial variables yield improvements in point and density forecasting performance, with the largest point-forecast gains emerging at intermediate and longer horizons. In particular, the one-factor BVAR model provides lowest values at six and nine months ahead, while the baseline BVAR performs best at the one-year horizon. By contrast, a large BVAR including all predictors individually does not improve point forecast accuracy, supporting the use of parsimonious model specifications. Simple forecast pooling delivers modest gains from medium horizons onward, but does not mitigate the time variation in relative forecast performance. We also provide a qualitative comparison of model-based forecasts with survey expectations and forecasts released by data providers, which further highlights that relative forecast performance varies over time. Moreover, we consider verified emissions and show that stochastic volatility improves point forecasts, with reductions of about 6.5 to 7.1% at the one-year horizon. Lastly, we use model-based forecasts to construct market monitoring tools that track demand and price pressure in the EU ETS.
Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks
SAFIR, Sorbonne University Abu Dhabi（索邦大学阿布扎比分校 SAFIR）；BITS Pilani, Hyderabad Campus（印度皮拉尼博拉理工学院海得拉巴校区）；Fidelity Investments（富达投资）；Sorbonne Center for Artificial Intelligence, Sorbonne University（索邦大学人工智能中心）
In this study, we propose a novel approach of nowcasting and forecasting the macroeconomic status of a country using deep learning techniques. We focus particularly on the US economy but the methodology can be applied also to other economies. Specifically US economy has suffered a severe recession from 2008 to 2010 which practically breaks out conventional econometrics model attempts. Deep learning has the advantage that it models all macro variables simultaneously taking into account all interdependencies among them and detecting non-linear patterns which cannot be easily addressed under a un…
We develop the methodology and a detailed case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the recently introduced foundational framework of BPS to the multivariate setting, with detailed application in the topical and challenging context of multi-step macroeconomic forecasting in a monetary policy setting. BPS evaluates-- sequentially and adaptively over time-- varying forecast biases and facets of miscalibration of individual forecast densities, and-- critically-- of time-varying inter-dependencies among them ov…
This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either towards zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designi…
In this paper, we are interested in evaluating the resilience of financial portfolios under extreme economic conditions. Therefore, we use empirical measures to characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described by the impact measures. The procedure for estimating the parameters of these models is described employing the Bayesian approach and using the prior information provided by the impact measures.…
This paper estimates models of high frequency index futures returns using `around the clock' 5-minute returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns, correlations between return and volatility shocks, and announcement effects. We develop an integrated MCMC approach to estimate interday and intraday parameters and states using high-frequency data without resorting to various aggregation measures like realized volatility. We provide a case study using …
We move beyond "Is Machine Learning Useful for Macroeconomic Forecasting?" by adding the "how". The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. In contrast, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so-called features (nonlinearities, regularization, cross-validation and alternative loss function) and study their behavior in both the data-rich and data-poor environments. To do so, we design experiments that allow to identify …
We combine high-dimensional factor models with fractional integration methods and derive models where nonstationary, potentially cointegrated data of different persistence is modelled as a function of common fractionally integrated factors. A two-stage estimator, that combines principal components and the Kalman filter, is proposed. The forecast performance is studied for a high-dimensional US macroeconomic data set, where we find that benefits from the fractional factor models can be substantial, as they outperform univariate autoregressions, principal components, and the factor-augmented err…
In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to traditional approaches such as GARCH. We compare the forecasting power of three approaches: GP regression on the absolute and squared returns; regression on the envelope of the returns and the absolute returns; and regression on the envelope of the negative and positive returns separately. We use a maximum a posteriori estimate with a Gaussian prior to determine ou…
In the data-rich environment, using many economic predictors to forecast a few key variables has become a new trend in econometrics. The commonly used approach is factor augment (FA) approach. In this paper, we pursue another direction, variable selection (VS) approach, to handle high-dimensional predictors. VS is an active topic in statistics and computer science. However, it does not receive as much attention as FA in economics. This paper introduces several cutting-edge VS methods to economic forecasting, which includes: (1) classical greedy procedures; (2) l1 regularization; (3) gradient d…
Macroeconomic Forecasting with Large Language Models
School of Economics and Finance, Queen Mary University of London（伦敦大学女王学院经济与金融学院）；School of Business and Economics, Brandeis University（布兰迪大学商学院与经济学学院）
In a low-dimensional linear regression setup, considering linear transformations/combinations of predictors does not alter predictions. However, when the forecasting technology either uses shrinkage or is nonlinear, it does. This is precisely the fabric of the machine learning (ML) macroeconomic forecasting environment. Pre-processing of the data translates to an alteration of the regularization -- explicit or implicit -- embedded in ML algorithms. We review old transformations and propose new ones, then empirically evaluate their merits in a substantial pseudo-out-sample exercise. It is found…
Faculty of Economics, Khon Kaen University（孔敬大学经济学院）；Khon Kaen University（孔敬大学）
This paper demonstrates the potentials of the long short-term memory (LSTM) when applyingwith macroeconomic time series data sampled at different frequencies. We first present how theconventional LSTM model can be adapted to the time series observed at mixed frequencies when thesame mismatch ratio is applied for all pairs of low-frequency output and higher-frequency variable. Togeneralize the LSTM to the case of multiple mismatch ratios, we adopt the unrestricted Mixed DAtaSampling (U-MIDAS) scheme (Foroni et al., 2015) into the LSTM architecture. We assess via bothMonte Carlo simulations and …
Olin Business School, Washington University in St. Louis（华盛顿大学圣路易斯分校奥林商学院）；Chaifetz School of Business, Saint Louis University（圣路易斯大学蔡菲兹商学院）；Center for Economic Behavior and Decision-Making, Zhejiang University of Finance and Economics（浙江财经大学经济行为与决策研究中心）；Department of Economics, University of Chicago（芝加哥大学经济学系）
Recent research finds that forecasting electricity prices is very relevant. In many applications, it might be interesting to predict daily electricity prices by using their own lags or renewable energy sources. However, the recent turmoil of energy prices and the Russian-Ukrainian war increased attention in evaluating the relevance of industrial production and the Purchasing Managers' Index output survey in forecasting the daily electricity prices. We develop a Bayesian reverse unrestricted MIDAS model which accounts for the mismatch in frequency between the daily prices and the monthly macro …
A Projection-Based ARIMA Framework for Nonlinear Dynamics in Macroeconomic and Financial Time Series: Closed-Form Estimation and Rolling-Window Inference
Department of Economics University of California, Riverside（经济系加州大学河滨分校）
MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
School of Economics and Finance, Queen Mary University of London（伦敦大学玛丽女王学院经济与金融学院）；School of Business and Economics, Brandeis University（布兰迪大学商学院与经济学院）
Since the Great Financial Crisis (GFC), the use of stress tests as a tool for assessing the resilience of financial institutions to adverse financial and economic developments has increased significantly. One key part in such exercises is the translation of macroeconomic variables into default probabilities for credit risk by using macrofinancial linkage models. A key requirement for such models is that they should be able to properly detect signals from a wide array of macroeconomic variables in combination with a mostly short data sample. The aim of this paper is to compare a great number of…
University of Hamburg（汉堡大学）；Hamburg Business School（汉堡商学院）
This article is an introduction to machine learning for financial forecasting, planning and analysis (FP\&A). Machine learning appears well suited to support FP\&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine…
This paper proposes a forecast-centric adaptive learning model that engages with the past studies on the order book and high-frequency data, with applications to hypothesis testing. In line with the past literature, we produce brackets of summaries of statistics from the high-frequency bid and ask data in the CSI 300 Index Futures market and aim to forecast the one-step-ahead prices. Traditional time series issues, e.g. ARIMA order selection, stationarity, together with potential financial applications are covered in the exploratory data analysis, which pave paths to the adaptive learning mode…
Birla Institute of Technology and Science, Pilani（博拉理工与科学大学皮拉尼分校）
In todays global economy, accuracy in predicting macro-economic parameters such as the foreign the exchange rate or at least estimating the trend correctly is of key importance for any future investment. In recent times, the use of computational intelligence-based techniques for forecasting macroeconomic variables has been proven highly successful. This paper tries to come up with a multivariate time series approach to forecast the exchange rate (USD/INR) while parallelly comparing the performance of three multivariate prediction modelling techniques: Vector Auto Regression (a Traditional Econ…
Westlake Institute for Advanced Study, Westlake University（西湖大学高等研究院）；School of Engineering, Westlake University（西湖大学工学院）；The Insight Centre for Data Analytics, University College Dublin（都柏林大学学院洞察数据分析中心）；Department of Computer Science, University of Warwick（华威大学计算机科学系）
Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-base…
University of Oxford（牛津大学）；Saïd Business School（赛德商学院）
This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC…
