Dissecting Climate Risks: Are they Reflected in Stock Prices?

Faccini, Renato; Matin, Rastin; Skiadopoulos, George

doi:10.2139/ssrn.3795964

Cited by 53 publications

(70 citation statements)

References 75 publications

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“…The HAR-RV model is tailored to capture such a pattern in the data, and, hence, we use this popular model to set the stage for our empirical analysis. Given that climate-change risk can be measured along a multitude of dimensions, ref [25] apply the Latent Dirichlet Allocation (LDA) technique, an unsupervised textualanalysis method, to anatomize climate-change risks and contrive climate-risk factors. The LDA technique initially was developed by [42] to deconstruct a text corpus into what they call "topics".…”

Section: Datamentioning

confidence: 99%

“…Ref. [25] apply the LDA technique to the articles that contain the words "climate change" and "global warming", published from January, 2000 to November, 2019 in Thomson Reuters News Archive. Once the LDA technique has identified the topics, ref [25] can give the topics an economic interpretation.…”

Section: Datamentioning

confidence: 99%

“…[25] apply the LDA technique to the articles that contain the words "climate change" and "global warming", published from January, 2000 to November, 2019 in Thomson Reuters News Archive. Once the LDA technique has identified the topics, ref [25] can give the topics an economic interpretation. Furthermore, they can generate time series of the the topic shares that represent the proportion of an article's text associated with a given topic.…”

Section: Datamentioning

confidence: 99%

“…In a final step, ref. [25] single out four key climate-related topics: the occurrence of natural disasters, the role of emissions in relation to global warming, U.S. climate policy, and international climate-change summits.…”

Section: Datamentioning

confidence: 99%

“…Given the importance of volatility forecasts for investors and policy authorities, and given that rich information contained in intraday data can produce more accurate estimates and forecasts of daily (realized) volatility [21], we augment the Heterogeneous Autoregressive (HAR) model developed by [22] to include climate-risk factors to forecast the realized daily volatility (RV), as computed from 5-minute-interval data, of crude oil-price returns over the period from January, 2001 to November, 2019 (alternatives to the HAR-RV model include Generalized Autoregressive Conditional Heteroskedasticity (GARCH), multifractal, and stochastic volatility models; for reviews of this literature, see [23,24]). The climate-risk factors have been recently constructed by [25] using techniques of computational linguistics. The climate-risk factors consist of daily proxies of physical (natural disasters and global warming) and transition (U.S. climate policy and international summits) climate risks.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment

Gupta

Pierdzioch

2021

Energies

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We extend the widely-studied Heterogeneous Autoregressive Realized Volatility (HAR-RV) model to examine the out-of-sample forecasting value of climate-risk factors for the realized volatility of movements of the prices of crude oil, heating oil, and natural gas. The climate-risk factors have been constructed in recent literature using techniques of computational linguistics, and consist of daily proxies of physical (natural disasters and global warming) and transition (U.S. climate policy and international summits) risks involving the climate. We find that climate-risk factors contribute to out-of-sample forecasting performance mainly at a monthly and, in some cases, also at a weekly forecast horizon. We demonstrate that our main finding is robust to various modifications of our forecasting experiment, and to using three different popular shrinkage estimators to estimate the extended HAR-RV model. We also study longer forecast horizons of up to three months, and we account for the possibility that policymakers and forecasters may have an asymmetric loss function.

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Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment

Gupta

Pierdzioch

2021

Energies

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Climate Risk and the Volatility of Agricultural Commodity Price Fluctuations: A Prediction Experiment

Gupta

Pierdzioch²

2022

Behavioral Finance and Asset Prices

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Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways

Lim

2024

Artif Intell Rev

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The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.

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Dissecting Climate Risks: Are they Reflected in Stock Prices?

Cited by 53 publications

References 75 publications

Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment

Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment

Climate Risk and the Volatility of Agricultural Commodity Price Fluctuations: A Prediction Experiment

Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways

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