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Weather is often referred as an uncontrollable factor, which influences customer’s buying decisions and causes the demand to move in any direction. Such a risk usually leads to loss to industries. However, only few research studies about weather and retail shopping are available in literature. The purpose of this paper is to develop a model and to analyze the relationship between weather and retail shopping behavior (i.e. store traffic and sales).

The data set for this research study is obtained from two food retail stores and a fashion retail store located in Lower Bavaria, Germany. All these three retail stores are in same geographical location. The weather data set was provided by a German weather service agency and is from a weather station nearer to the retail stores under study. The analysis for the study was drawn using multiple linear regression with autoregressive elements (MLR-AR). The estimated coefficients of weather variables using MLR-AR model represent corresponding weather impacts on the store traffic and the sales.

The snowfall has a significant effect on the store traffic and the sales in both food and fashion retail stores. In food retail store, the risk due to snowfall varies depending on the location of stores. There are also significant lagging effects of snowfall in the fashion retail store. However, the rainfall has a significant effect only on the store traffic in the food retail stores. In addition to these effects, the sales in the fashion retail store are highly affected by the temperature deviation.

Limitations in availability of data for the weather variables and other demand influencing factors (e.g. promotion, tourism, online shopping, demography of customers, etc.) may reduce efficiency of the proposed MLR-AR model. In spite of these limitations, this study can be able to quantify the effects of weather variables on the store traffic and the sales.

This study contributes to the field of retail distribution by providing significant evidence of relationship between weather and retail business. Unlike previous studies, the proposed model tries to consider autocorrelation property, main and interaction effects between weather variables, temperature deviation and lagging effects of snowfall on the store traffic or the sales. The estimated weather impacts from this model can act as a reliable tool for retailers to explain the importance of different non-catastrophic weather events.

Retailers have long been aware that weather affects the sales of a myriad of products, but until now, most were not in a position to manage the risks weather presents. Rising weather variability combined with advances in weather-index financial instruments have prompted new interest in investigating the relationship between sales and weather. The purpose of this paper is to explore the impact of changes in weather on UK retail sales, to estimate the contribution of weather to sales, and evaluate the maximum potential loss caused by adverse weather, for each season and retail sector.

The authors present a methodology to identify and quantify the extent to which a company is exposed to weather risks, in order to incorporate them into its risk management policy and take actions to mitigate these risks. For each season and each retail category, the authors provide a measure of the impact of weather on sales that can be used as a benchmark to analyse sales performance.

The authors propose a new risk assessment indicator to evaluate the potential losses caused by adverse weather (WeatherRisk). The authors show that intra-annual changes in weather significantly affect retail sales. The exposure of retail categories to weather are not the same depending on the season, and the response of individual retail categories to the same change in weather varies considerably. Although temperature is a predominant explanatory variable, the authors show that weather-sensitivity analysis should include precipitation, humidity rate and wind.

One limitation of this study is that the authors individually compute WeatherRisk for each significant weather variable. Further research could explore new approaches to evaluate Total WeatherRisk, which take into account potential multicollinearity issues between weather variables.

The methodology allows retailers to measure the effects of weather on sales performance, evaluate the risks at stake, and protect sales and margins from weather risks, with newly available index-based financial instruments. Managers may now actively use weather as a differential advantage, and at the same time focus their efforts on improving resiliency to increasing climate variability.

In this paper, the authors produce a detailed analysis of the exposure of each retail sectors to unseasonal weather. This is the first time all retail sectors are analysed and ranked per season at a national level. The authors provide managers with actionable information to improve their understanding of how weather impact sales over each season, and to allow them to structure weather-index-based instruments with financial partners.

The paper aims to examine theoretically valuation of weather derivatives and their hedging roles in corporate risk management.

The paper introduces an extended financial market model in which the weather risk is included as an independent random process and examines the effectiveness of weather derivatives and traditional price forwards in a unified theoretical framework. It also provides a no‐arbitrage approach to price weather derivatives, which theoretically combines the actuarial and financial paradigms.

The results document that corporate leverage level is an essential factor determining the choice between price forwards and weather derivatives. In some cases; weather derivatives outperform price forwards, while in some other cases; a joint use of both instruments is optimal, depending on the firm's risky leverage level. Interestingly, the paper identifies the case when the leverage level is very high, the positive roles of both instruments diminish and the firm is unhedgeable.

The paper provides important insights to investors and hedgers and extends the literature on corporate risk management.

In the equatorial Pacific, rainfall is affected by global climate phenomena, such as El Niño Southern Oscillation (ENSO). However, current publicly available methodologies for valuing weather derivatives do not account for the influence of ENSO. The purpose of this paper is to develop a complete framework suitable for valuing rainfall derivatives in the equatorial Pacific.

In this paper, we implement a Markov chain for the occurrence of rain and a gamma model for the conditional quantities using vector generalized linear models (VGLM). The ENSO forecast probabilities reported by the International Research Institute for Climate and Society (IRI) are included as independent variables using different alternatives. We then employ the Esscher transform to price rainfall derivatives.

The methodology is applied and calibrated using the historical rainfall data collected at the El Dorado airport weather station in Bogotá. All the estimated coefficients turn out to be significant. The results prove more accurate than those of Markovian gamma models based on purely statistical descriptions of the daily rainfall probabilities.

This procedure introduces the novelty of incorporating variables related to the climatic phenomena, which are the forecast probabilities regularly published for the occurrence of El Niño and La Niña.

Climate change-induced weather changes are severe and frequent, making it difficult to predict apparel sales. The primary goal of this study was to assess consumers' responses to winter apparel searches when external stimuli, such as weather, calendars and promotions arise and to develop a decision-making tool that allows apparel retailers to establish sales strategies according to external stimuli.

The theoretical framework of this study was the effect of external stimuli, such as calendar, promotion and weather, on seasonal apparel search in a consumer's decision-making process. Using weather observation data and Google Trends over the past 12 years, from 2008 to 2020, consumers' responses to external stimuli were analyzed using a classification and regression tree to gain consumer insights into the decision process. The relative importance of the factors in the model was determined, a tree model was developed and the model was tested.

Winter apparel searches increased when the average, maximum and minimum temperatures, windchill, and the previous day's windchill decreased. The month of the year varies depending on weather factors, and promotional sales events do not increase search activities for seasonal apparel. However, sales events during the higher-than-normal temperature season triggered search activity for seasonal apparel.

Consumer responses to external stimuli were analyzed through classification and regression trees to discover consumer insights into the decision-making process to improve stock management because climate change-induced weather changes are unpredictable.

Little research on the influence of external factors, such as weather and holiday periods, on retail sales on alcoholic beverages is available. This study aims to investigate how weekly retail sales of different alcoholic beverages vary in association with daily maximum temperatures and annual federal holidays across selected US counties in the years 2013 to 2015. The research provides information, which can contribute to better sales forecasts.

Secondary data of weekly retail sales (volume) of alcoholic beverages from 37,346 stores in 651 counties in the USA are analysed. The data cover on average 21% of all existing US counties and 12% of the total US off-trade retail sales of alcoholic beverages in the period studied (Euromonitor, 2017). Additional data of federal holidays and meteorological data are collated for each county in the sample. Seasonal autoregressive integrated moving average models with exogenous regressors (SARIMAX) are applied to develop forecasting models and to investigate possible relationships and effects.

The results indicate that off-trade retail sales of beer, liquor, red and white wine are temperature sensitive throughout the year, while contrary to expectations rosé, sparkling and other wines are not. Sales sensitivities to temperature also differ by geography. In the warmest regions, liquor and white wine sales do not respond to temperature changes, as opposed to the coolest regions, where they are responsive. Public holidays, particularly Easter, Thanksgiving, Christmas and New Year holidays, represent a constant influencing factor on short-term sales increases for all investigated alcoholic beverage categories.

This is the first large-scale study of weather and holiday-related sales variations over time, across geographies and different alcoholic beverage categories. Seasonal and non-seasonal short-term sales variations are important for retailers and manufacturers alike. Accounting for expected changes in demand accommodates efficiencies along the supply chain and has implications for retail management, as well as adjusting marketing efforts in competing categories.

The purpose of this study is to investigate international trade determinants, paying special attention to variables related to climate change and non-tariff measures (NTMs), as they shape more and more world trade flows, with particular incidence on globalised goods, such as wine.

Based on panel data of Port wine exports to 60 countries, between 2006 and 2018, a gravity model has been estimated through Poisson pseudo-maximum likelihood. Explanatory variables include NTMs, mean temperature, temperature anomaly, gross domestic product (GDP), exchange rate, ad valorem equivalent tariffs and home bias.

The findings show that exports are inversely related to both mean temperature and temperature anomaly in importing countries. Regarding NTMs, it is found that only part of them are trade deterrent. Additionally, purchasing power in importing countries is one of the main determinants of Port wine exports.

The results show that, besides traditional economic variables, policymakers and wineries should include in their exports' decisions the impact of variables related to climate change and NTMs.

The novelty of this paper is to incorporate the impact of climatic variability of importing countries as a determinant of international trade of wine. Most former studies inspired of the gravity model consider explanatory variables such as GDP and exchange rate, and more recent ones started to consider NTMs too, however, this study may be the first paper to include the impact of climate change (quantified by mean temperature and temperature anomaly in importing countries) on exports.

The purpose of this paper is to explore the dynamics of the relationships between sales and internal and external environmental drivers in a retail company using a systems perspective in order to support retail management decisions with nonlinear methods.

The research and results are presented in two parts: the collection and explorative analysis of the data; and discussion of the managerial implications following a systems perspective. The exploratory analysis is conducted using a statistical comparison of linear and nonlinear models of sales data from a retail company. The data, which comprise two data sets, come from 45 retail stores located in different regions of the USA.

Specifically, nonlinear models provided a better explanation of variation in retail activity (R²=46 per cent) than linear models (R²=16 per cent). In such a situation, the nonlinear analysis captures the influence of internal and external environmental drivers on retail sales.

With a limited variety of external and internal drivers, the exploratory analysis aims to describe a general situation in which retailers are managing activities in complex environments as opposed to reflect on a particular retail chain.

The systems perspective is used to interpret the managerial implications of the nonlinear analysis fits, particularly in cases where retail decision-makers are adapting, transforming and restructuring sources of competitive advantage in complex environments.

The paper provides an alternative perspective (the systemic one) of how retailers could interpret the relationships between internal and external variables in the dynamic environment of the retail chains with nonlinear models.

In this article, the authors develop an arbitrage approach to valuing insurance‐linked securities (ILS) for non‐catastrophic events within a framework of stochastic interest rates. The prices of these transactions are driven by both an interest rate process and a non‐trivial actuarial risk process. The authors find that the duration of ILS is, in most cases, higher than the Macaulay duration of risk‐free bonds, which implies that the alleged relative out‐performance of ILS is illusory.

The authors investigate whether an index-based weather insurance (WII) product can complement or replace existing traditional crop yield insurance for mitigating farmers' financial risks, with an application to blueberry growers in British Columbia (BC).

A hybrid model combining expected utility (EU) and prospect values is developed to analyse farmers' demand for WII.

While weather data are used to investigate supply elements, a hybrid model combining EU theory and prospect theory (PT) is developed to analyse farmers' demand for WII. On the supply side, a quality index is constructed and the relationship between the quality index and key weather parameters is quantified using a partial least squares structural model. The authors then model weather parameters via time-series analysis and statistical distributions to provide reasonable estimates for calculating actuarially sound insurance premiums for a rainfall indexed, insurance product. This model indicates that decreases in the proportion of a blueberry grower's total revenue and revenue volatility will decrease the possibility that they participate in WII. At the same time, an increase in the value loss aversion coefficient and WII's basis risk further leads to less demand for WII. In short, a grower may decide not to participate in WII at an actuarially fair premium due to the combined effects of the above factors. Overall, while the supply analysis enables us to demonstrate that WII can potentially help in mitigating farmers' financial risks, it turns out that, on the demand side, blueberry growers are unwilling to pay for such a product without large government subsidies.

The authors argue that the demand for insurance may be affected by the level and the volatility of a berry grower's total revenue. Hence, the authors propose a hybrid expression that assumes a farmer seeks to maximize the total utility function to capture the rational and intuitive parts of a farmer's decision-making process. The EU represents rationality and the prospect value represents the intuitive component. Meanwhile, the authors investigate the possibility of using key weather parameters to construct a berry quality index – one that could be applied to other agricultural areas for studying the relationship between weather conditions and product quality.