The Power of Weather

Huurman, Christian; Ravazzolo, Francesco; Zhou, Chen

doi:10.2139/ssrn.1946302

Cited by 14 publications

(15 citation statements)

References 25 publications

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“…Their conclusion is that weather can affect trading activities but not returns. Finally, an example of the influence of weather on the electricity market can be found in Huurman et al (2012) who study the weather premium in the electricity market. They show that using the next day weather forecast clearly improves electricity price predictions.…”

Section: Weather and Supply Chain Performancementioning

confidence: 99%

Weather and supply chain performance in sport goods distribution

Appelqvist

Babongo

Chavez‐Demoulin

et al. 2016

International Journal of Retail &Amp; Distribution Management

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Purpose – The purpose of this paper is to study how variations in weather affect demand and supply chain performance in sport goods. The study includes several brands differing in supply chain structure, product variety and seasonality. Design/methodology/approach – Longitudinal data on supply chain transactions and customer weather conditions are analysed. The underlying hypothesis is that changes in weather affect demand, which in turn impacts supply chain performance. Findings – In general, an increase in temperature in winter and spring decreases order volumes in resorts, while for larger customers in urban locations order volumes increase. Further, an increase in volumes of non-seasonal products reduces delays in deliveries, but for seasonal products the effect is opposite. In all, weather affects demand, lower volumes do not generally improve supply chain performance, but larger volumes can make it worse. The analysis shows that the dependence structure between demand and delay is time varying and is affected by weather conditions. Research limitations/implications – The study concerns one country and leisure goods, which can limit its generalizability. Practical/implications – Well-managed supply chains should prepare for demand fluctuations caused by weather changes. Weekly weather forecasts could be used when planning operations for product families to improve supply chain performance. Originality/value – The study focuses on supply chain vulnerability in normal weather conditions while most of the existing research studies major events or catastrophes. The results open new opportunities for supply chain managers to reduce weather dependence and improve profitability.

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Section: Weather and Supply Chain Performancementioning

confidence: 99%

Weather and supply chain performance in sport goods distribution

Appelqvist

Babongo

Chavez‐Demoulin

et al. 2016

International Journal of Retail &Amp; Distribution Management

View full text Add to dashboard Cite

show abstract

“…Ziel et al (2015) propose a vector autoregressive threshold ARCH (VAR TARCH), of which the forecasts outperform competitors. Others, such as Misiorek et al (2006) and Huurman et al (2012), find AR/ ARIMA(X) models outperform GARCH processes. Garcia et al (2005) argue that forecasts by GARCH models are particularly better when volatility and price spikes are present.…”

Section: Performance Of Different Time Series Modelsmentioning

confidence: 99%

Forecasting performance of time series models on electricity spot markets

Gürtler

Paulsen

2018

IJESM

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Purpose Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance. Design/methodology/approach The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market. Findings The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data. Originality/value The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

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“…In this section, we follow the interesting and rapidly growing strand of the literature (Diebold et al, 1998;Huurman, Ravazzolo, & Zhou, 2012;Kascha & Ravazzolo, 2010;Monticini & Ravazzolo, 2014 among others) on density forecasting by generating and evaluating the density forecasts from each of the methods. Generating and evaluating density forecasts also constitutes a useful robustness check for our point forecasting results.…”

Section: Density Forecast Evaluationmentioning

confidence: 99%

Forecasting the LIBOR‐Federal Funds Rate Spread During and After the Financial Crisis

Dbouk

Jamali

Kryzanowski

2015

Journal of Futures Markets

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In this paper, we examine the point and density forecast accuracy of econometric models, surveys and futures rates in predicting the LIBOR‐Federal Funds Rate (LIBOR‐FF) spread during and after the financial crisis. We provide evidence that the futures market forecast outperforms all competing forecasts during and after the financial crisis and that its predictive density is well calibrated. Our results also suggest that the predictive accuracy of the econometric models improves in the post‐crisis period. We argue that the post‐2009 improvement in the econometric models' forecasts is attributable to the absence of LIBOR manipulation. The economic significance of the uncovered predictability is assessed using a trading strategy. Our results suggest that trading based on the futures market and econometric forecasts generates positive risk‐adjusted returns. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:345–374, 2016

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The Power of Weather

Cited by 14 publications

References 25 publications

Weather and supply chain performance in sport goods distribution

Weather and supply chain performance in sport goods distribution

Forecasting performance of time series models on electricity spot markets

Forecasting the LIBOR‐Federal Funds Rate Spread During and After the Financial Crisis

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