Hedging potentials of green investments against climate and oil market risks

PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan's (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West's model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

US weekly economic index: Replication and extension

We revisit the US weekly economic index (WEI) put forth by Lewis, Mertens, Stock and Trivedi (2021). In a narrow sense, we replicate their main results with data gathered from its original sources. In a wide sense, we apply the methodology established in Wegmüller, Glocker and Guggia (2023) to adjust the weekly input series for seasonal patterns, calendar day effects, and excess volatility. In a long sense, we show that our proposed data adjustment significantly improves the nowcasting performance of the WEI. © 2023 John Wiley & Sons, Ltd.

Censored density forecasts: Production and evaluation

This paper develops methods for the production and evaluation of censored density forecasts. The focus is on censored density forecasts that quantify forecast risks in a middle region of the density covering a specified probability and ignore the magnitude but not the frequency of outlying observations. We propose a fixed-point algorithm that fits a potentially skewed and fat-tailed density to the inner observations, acknowledging that the outlying observations may be drawn from a different but unknown distribution. We also introduce a new test for calibration of censored density forecasts. An application using historical forecast errors from the Federal Reserve Board and the Monetary Policy Committee (MPC) at the Bank of England suggests that the use of censored density functions to represent the pattern of forecast errors results in much greater parameter stability than do uncensored densities. We illustrate the utility of censored density forecasts when quantifying forecast risks after shocks such as the global financial crisis and the COVID-19 pandemic and find that these outperform the official forecasts produced by the MPC. © 2023 John Wiley & Sons, Ltd.

Nowcasting the state of the Italian economy: The role of financial markets

This paper compares several methods for constructing weekly nowcasts of recession probabilities in Italy, with a focus on the most recent period of the Covid-19 pandemic. The common thread of these methods is that they use, in different ways, the information content provided by financial market data. In particular, a battery of probit models are estimated after extracting information from a large dataset of more than 130 financial market variables observed at a weekly frequency. The accuracy of these models is explored in a pseudo out-of-sample nowcasting exercise. The results demonstrate that nowcasts derived from probit models estimated on a large set of financial variables are, on average, more accurate than those delivered by standard probit models estimated on a single financial covariate, such as the slope of the yield curve. The proposed approach performs well even compared with probit models estimated on single time series of real economic activity variables, such as industrial production, business tendency survey data or composite PMI indicators. Overall, the financial indicators used in this paper can be easily updated as soon as new data become available on a weekly basis, thus providing reliable early estimates of the Italian business cycle. © 2023 John Wiley & Sons Ltd.

Testing the predictive accuracy of COVID-19 forecasts.

We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention for the epidemic in the United States. We find three main results. First, at the short horizon (1-week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3- and 4-week ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.

Weekly economic activity: Measurement and informational content

We construct a composite index to measure the real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data set capturing economic activity across distinct dimensions over a long time horizon. We propose a six-step procedure for extracting precise business cycle signals from the raw data. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: (i) our weekly index significantly outperforms a comparable index without adjusted input variables;and (ii) the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve other recently developed high-frequency indicators. © 2021 International Institute of Forecasters

Forecasting GDP with many predictors in a small open economy: forecast or information pooling?

This study compares two distinct approaches, pooling forecasts from single indicator MIDAS models versus pooling information from indicators into factor MIDAS models, for short-term Singapore GDP growth forecasting with a large ragged-edge mixed frequency dataset. We consider various popular weighting schemes in the literature when conducting forecast pooling. As for factor extraction, both the conventional dynamic factor model and the three-pass regression filter approach are considered. We investigate the relative predictive performance of all methods in a pseudo-out-of-sample forecasting exercise from 2007Q4 to 2020Q3. In the stable growth non-crisis period, no substantial difference in predictive performance is found across forecast models. In comparison, we find information pooling tends to dominate both the quarterly autoregressive benchmark model and the forecast pooling strategy particularly during the Global Financial Crisis. Supplementary Information: The online version contains supplementary material available at 10.1007/s00181-022-02356-9.

Assessing the hedging potential of gold and other precious metals against uncertainty due to epidemics and pandemics.

We assess the hedging capabilities of four prominent precious metals namely gold, palladium, platinum and silver against market risks due to epidemics and pandemics. The research objective is informed by the COVID-19 pandemic which amplifies health risks with attendant concerns for financial markets. We utilize the health-related uncertainty index developed by Baker et al. (Equity market volatility: infectious disease tracker [INFECTDISEMVTRACK], 2020) which measures uncertainty in the financial markets due to infectious diseases including the COVID-19 pandemic and construct a predictive model that accommodates the salient features of both the predictand and predictor series. Our results support the safe haven property only for gold before and during the COVID-19 pandemic. We push the analysis further for in-sample and out-of-sample forecast evaluation and find that accounting for uncertainty due to infectious diseases improves the forecast of the four precious metals relative to the benchmark model (historical average). We highlight for investors that the gold market remains the safest market among the precious metals particularly during the COVID-19 pandemic.

Weekly economic activity: Measurement and informational content

We construct a composite index to measure the real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data set capturing economic activity across distinct dimensions over a long time horizon. We propose a six-step procedure for extracting precise business cycle signals from the raw data. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: (i) our weekly index significantly outperforms a comparable index without adjusted input variables;and (ii) the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve other recently developed high-frequency indicators.

Nowcasting unemployment insurance claims in the time of COVID-19.

Near-term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. The state-level panel model, exploiting the variation in timing of state-of-emergency declarations, also performs better than models including Google Trends. Our results suggest that in times of structural change there is a bias-variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.