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This thesis by Emanuel Evarest Sinkwembe presents a detailed study on modeling weather dynamics for the purpose of pricing weather derivatives, specifically focusing on temperature dynamics. The work introduces a two-regime switching model that effectively captures temperature features affected by urbanization and other factors, offering improvements over single-regime models. This model is compared against a benchmark model in terms of HDDs, CDDs, and CAT indices using data from various locations in Sweden, demonstrating superior results. The thesis also develops mathematical expressions for pricing futures and option contracts, employing Monte Carlo simulations. The intended audience includes academics and professionals in mathematics, finance, and meteorology interested in stochastic modeling and financial derivatives.

Weather derivatives. --- Stochastic processes. --- Weather derivatives --- Stochastic processes

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Weather derivatives provide a tool for weather risk management, and the markets for these exotic financial products are gradually emerging in size and importance. This unique monograph presents a unified approach to the modeling and analysis of such weather derivatives, including financial contracts on temperature, wind and rain. Based on a deep statistical analysis of weather factors, sophisticated stochastic processes are introduced modeling the time and space dynamics. Applying ideas from the modern theory of mathematical finance, weather derivatives are priced, and questions of hedging analyzed. The treatise contains an in-depth analysis of typical weather contracts traded at the Chicago Mercantile Exchange (CME), including so-called CDD and HDD futures. The statistical analysis of weather variables is based on a large data set from Lithuania.The monograph includes the research done by the authors over the last decade on weather markets. Their work has gained considerable attention, and has been applied in many contexts.

Stocks --- Weather derivatives. --- Prices. --- Stock prices --- Derivative securities --- Stockholder wealth --- Weather derivatives --- Prices --- E-books --- Actions (Titres de société) --- Prix

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This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects.

Technology: general issues --- History of engineering & technology --- electricity markets --- non-parametric regression --- minimum variance hedge --- spline basis functions --- cyclic cubic spline --- weather derivatives --- n/a --- distributed energy resources (DER) --- P2P energy trading --- cooperative mechanism --- renewable energy --- multi agent system --- blockchain --- cashflow management of electricity businesses --- electricity derivatives and forwards --- retailers and power producers --- solar power and thermal energy --- optimal hedging using nonparametric techniques --- empirical simulations --- peer-to-peer energy trading --- distributed energy resources --- microgrid --- digital grid --- bidding strategy --- electricity price --- electricity load --- electricity price forecasting --- wind energy --- day-ahead market --- intra-day market --- balancing power market --- peer to peer energy market --- hardware control --- demonstration experiment --- home energy management systems --- electric vehicles --- bidding agent --- electric vehicle --- functional autoregressive model --- functional principle component analysis --- vector autoregressive model --- functional final prediction error (FFPE) --- naive method --- P2P electricity market --- market maker --- liquidity --- price fluctuation --- artificial market simulation