TY - JOUR

T1 - Pricing analysis of wind power derivatives for renewable energy risk management

AU - Kanamura, Takashi

AU - Homann, Lasse

AU - Prokopczuk, Marcel

N1 - Funding Information: 2. What are the factors critical to success or failure for AMIS projects? 2 Method In order to achieve a comprehensive charting of AMIS in LDCs two online literature surveys conducted consecutively in May 2008 and December 2009. For locating as many different AMIS as possible, the initial search was very open and conducted by consulting Google (www.google.com) and Google Scholar (http://scholar.google.com/). This search generated many cases of AMIS and useful links to websites and portals on the subject. In order to focus particularly on scientific evaluation papers the second search was conducted by an academic search engine which is hosted by Örebro University (Sweden) and covers several academic databases. In addition to these two search methods, the ‘snowball method’ [9] was also used to find relevant cases based on the literature we had found. For that search, saturation was used as the stop criterion: the search stopped when no new or special cases were found. We also consulted the online AMIS database [10] maintained by the Michigan State University (MSU). In addition to MSU, this paper used a working paper [11] on market information sources and AMIS-Africa Online database. Since this study specifically targets LDCs, we started with a search where each country’s name was combined with a search term – ‘Agriculture Market Information’. In case of unavailability of country specific such services, portals of the related ministries (e.g. agriculture in most cases) of concerned countries had been investigated. The data from the survey was categorized and analysed based on an evaluation matrix (Table 1) developed by the authors in lack of a commonly used evaluation toolkit in the IS research community. Although AMIS are more or less present in most countries, evaluation or impact studies are yet few [7, 12, 13]. One of the reasons of this lack is the deficiencies in measurement and methodological toolkits. While it is possible to know the number of information recipients, it is difficult to identify their needs and their uses of information. There are indeed some studies that attempt to quantify some benefits, but these are mostly based on limited empirical evidence [6, 13, 14, 15]. Focus dimensions Users (Target) Who are the targeted main user(s) ? (Needs) What needs are targeted to address? (Time) When do the users get the service? (Accessibility) How do the users access the service and how affordable the cost is? (Managers) Who manages? Management Funding (Sponsors) Who provides the funds? (Budget) What is the functional allocation of funding? (Period) What was the period for funding? (Sustainability) How the fund has been managed; short & long term perspective? Infrastructure (Suppliers) Who owns/provides both the supply and demand sides’ infrastructure? (Tools) What infrastructures are considered on both supply and demand sides? (Availability) When does the infrastructure readily available for targeted operation? (Use) How the infrastructure is used for targeted operation? Data (Providers) Who are the actors in data supply chain? (Data) What are the data? ( Lead-time) When is the data processed and disseminated? (Process) How is the data processed for operation? The research framework used in this study is called the “IS-PEM” model (Table 1). It has five focus dimensions (vertical); Users, Management, Funding, Infrastructure and Data. Each of these dimensions is investigated by means of four strategic and critical questions – who, what, when and how (horizontal). Each cell of the matrix contains a keyword and is associated with queries that would lead to find the most pertinent aspects of a project. The strategic questions are derived from the well-known 5W2H (Why, What, Who, When, Where, How to and How much) scheme which is frequently used in the discipline of Total Quality Management (TQM). The 5W2H is a systematic root cause analysis technique which is particularly useful when a suspected problem of a project needs to be better defined and reviewed and overall the project process has improvement opportunities [16, 17]. In our matrix, we assume that the aspect ‘why’ is inherited in all the other aspects while analyzing the situation under a certain context. Furthermore, the ‘where’ is a ‘space’ of the object under analysis which is (Roles) What are the roles of participants? (Duration) When did it start and end? (Strategy) How are the targeted needs planned to address or are addressed ? known already. Finally, ‘how to’ and ‘how much’ are merged together as only ‘how’ [17] which explains simultaneously the ways and extent of resolving the problems. This simplification leaves us with the IS-PEM model in Table 1. We used the model to investigate each project, looking primarily for success and failure factors. Success and failure is measured as stated by either independent evaluations or self-assessment made by, typically, service providers. We hence do not re-assess projects, but look for factors contributing to success or failure, as found in other people’s research.

PY - 2021/12/15

Y1 - 2021/12/15

N2 - The objective of this study is to analyse the theoretical pricing of wind power derivatives, which is important for renewable energy risk management but has a problem in the pricing due to the illiquidity of the assets and to show the application of the theory to the practical implementation of the pricing. We make three contributions to the literature. First, to the best of our knowledge, we are the first to conduct a detailed econometric analysis of the wind power futures underlying, i.e., the electricity production based on windmills, resulting in strong support of seasonality and mean reversion in the logit-transformed wind power load factors. Second, after proposing a new model of wind power load factors based on the econometric findings, we analyse the theoretical prices of wind power futures and call option contracts to which the good-deal bounds pricing within an illiquid market situation is applied as well as we show the application of the theory to the practical pricing with the illiquidity. Third, our empirical pricing analysis shows that theoretical wind power futures prices derived using seasonal modelling more accurately reflect reality than those derived without seasonality compared to market observations, resulting in the importance of seasonality modelling in theoretical wind power derivatives pricing. In particular, considering that the upper and lower price boundaries represent the selling and the buying prices in the incomplete market, respectively, we show that the pricing of the short position is more affected by the seasonality than the pricing of the long position. Finally, we illustrate and discuss the practical applications of the results obtained in our study.

AB - The objective of this study is to analyse the theoretical pricing of wind power derivatives, which is important for renewable energy risk management but has a problem in the pricing due to the illiquidity of the assets and to show the application of the theory to the practical implementation of the pricing. We make three contributions to the literature. First, to the best of our knowledge, we are the first to conduct a detailed econometric analysis of the wind power futures underlying, i.e., the electricity production based on windmills, resulting in strong support of seasonality and mean reversion in the logit-transformed wind power load factors. Second, after proposing a new model of wind power load factors based on the econometric findings, we analyse the theoretical prices of wind power futures and call option contracts to which the good-deal bounds pricing within an illiquid market situation is applied as well as we show the application of the theory to the practical pricing with the illiquidity. Third, our empirical pricing analysis shows that theoretical wind power futures prices derived using seasonal modelling more accurately reflect reality than those derived without seasonality compared to market observations, resulting in the importance of seasonality modelling in theoretical wind power derivatives pricing. In particular, considering that the upper and lower price boundaries represent the selling and the buying prices in the incomplete market, respectively, we show that the pricing of the short position is more affected by the seasonality than the pricing of the long position. Finally, we illustrate and discuss the practical applications of the results obtained in our study.

KW - Wind power

KW - Load factor

KW - Good-deal bounds

KW - Futures and options

KW - Mean reversion

KW - Seasonality

UR - http://www.scopus.com/inward/record.url?scp=85115948266&partnerID=8YFLogxK

U2 - 10.1016/j.apenergy.2021.117827

DO - 10.1016/j.apenergy.2021.117827

M3 - Article

VL - 304

JO - Applied Energy

JF - Applied Energy

SN - 0306-2619

M1 - 117827

ER -