Quantitative analysis of energy markets

In this paper, we make a survey on the research of time-of-use (TOU) electricity price and TOU pricing models and methods in China. We summarize the basic idea, hypothesis and the general model of the following pricing models: (1) TOU pricing model based on user response, (2) pricing model based on the user response and customer satisfaction, (3) pricing model based on the linkage between the generation side and the demand side, and (4) TOU pricing based on game theory. Then we make comparisons and reviews on these models and methods, analyze their advantages as well as the existed deficiencies, and put forward the research prospect of the TOU pricing model.

We propose a joint modeling of spot electricity prices, forwards prices and other derivative prices, using recent developments in discrete time asset pricing methods based on the notions of stochastic discount factor and of Compound Autoregressive (or affine) stochastic processes. We show that this approach provides quasi explicit formulae for forward and option prices, while allowing for a large flexibility in the modeling of dynamics, spikes and seasonality, both in the historical and the risk neutral worlds. We also propose a variety of inference techniques involving inversion methods, the Kalman filter and the Kitagawa–Hamilton filter. Finally, an application based on French spot prices and forward products is proposed.

Derivative pricing models require calibration to market conditions in order to determine quantities such ashedging positions and the prices of other instruments. For stochastic models and/or complex derivativeswhose prices are not of an analytic form, prices must be computed via simulation and the calibration ismore difficult. A method to facilitate the calibration of simulation-based pricing models is proposed. Thealgorithm uses a statistically designed experiment to select the points at which simulations are performed.The method is quite general as it is independent of the stochastic model for the underlying and allows fordifferent objective functions that can incorporate information such as open interest and volume. Further-more, market prices from European- and/or American-style derivatives covering a range of strike pricesand maturities can be handled by this technique. Examples show the procedure is successful at calibratingwell-known asset pricing models to both simulated and market data.Keywords: derivatives pricing; calibration; simulation; experimental design; regression.

Financial mathematics plays a pivotal role in various aspects of modern economics and finance. This paper provides an introduction to the fundamental concepts, theories, and applications of financial mathematics. It begins by outlining the basic principles of financial mathematics, including the time value of money, interest rates, and compounding. Computational programs enhance these mathematical models, offering robust solutions and efficient computation for complex financial problems. This study explores the integration of computational programs with financial mathematics, their methodologies, and applications in the finance sector. The results underscore the significance of computational methods in improving the accuracy, speed, and scalability of financial models, ultimately contributing to better decision-making and risk management. We explore fundamental concepts, models, and techniques employed in financial mathematics, aiming to provide a comprehensive understanding of their applications and significance in real-world financial scenarios. This paper provides a comprehensive overview of the application of differential equations in financial mathematics, highlighting key models such as the Black-Scholes model, interest rate models, and optimal investment strategies.

A Structural Model for Electricity Prices with Spikes - Measurement of Jump Risk and Optimal Policies for Hydropower Plant Operation

Long-term swings and seasonality in energy markets

Quantitative finance is a combination of economics, accounting, statistics, econometrics, mathematics, stochastic process, and computer science and technology. Increasingly, the tools of financial analysis are being applied to assess, monitor, and mitigate risk, especially in the context of globalization, market volatility, and economic crisis. This three-volume handbook, comprised of over 100 chapters, is the most comprehensive resource in the field to date, integrating the most current theory, methodology, policy, and practical applications. Showcasing contributions from an international array of experts, the Handbook of Quantitative Finance and Risk Management is unparalleled in the breadth and depth of its coverage. Volume 1 presents an overview of quantitative finance and risk management research, covering the essential theories, policies, and empirical methodologies used in the field. Chapters provide in-depth discussion of portfolio theory and investment analysis. Volume 2 covers options and option pricing theory and risk management. Volume 3 presents a wide variety of models and analytical tools. Throughout, the handbook offers illustrative case examples, worked equations, and extensive references; additional features include chapter abstracts, keywords, and author and subject indices. From arbitrage to yield spreads, the Handbook of Quantitative Finance and Risk Management will serve as an essential resource for academics, educators, students, policymakers, and practitioners.

Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

The dynamic of a financial asset's returns and prices can be expressed using a deterministic process if there is no uncertainty about its future behavior, or with a stochastic process in the more likely case when the value is uncertain. Stochastic processes in continuous time are the most used tool to explain the dynamics of a financial asset's returns and prices. They are the building blocks with which to construct financial models for portfolio optimization, derivatives pricing, and risk management. Continuous-time processes allow for more elegant theoretical modeling compared to discrete-time models and many results proven in probability theory can be applied to obtain a simple evaluation method. Keywords: Brownian motion; Poisson process; jump processes; Levy processes; stochastic process X =; gamma process; exponential process; variance gamma; process; stable process; standard CTS process; generalized tempered stable; process; modified tempered stable; process; standard MTS process; normal tempered stable; process; standard NTS process; normal inverse Gaussian; standard NIG process; Kim-Rachev tempered stable; standard KRTS process; rapidly decreasing tempered stable; standard RDTS process; Brownian motion; Independent increments; Stationary increments; time-changed Brownian motion; The Modified Tempered Stable Processes with Application to Finance; conditional expectation; change of measure; Esscher transform; Brownian Motion and Stochastic Calculus; Risk Assessment Decisions in Banking and Finance

The 2024 3rd International Conference on Economics, Mathematical Finance and Risk Management (EMFRM 2024) was successfully held during December 07-08, 2024 in Dubai, United Arab Emirates. This conference aims to bring together innovative scholars and experts in economic system, business statistics, mathematical finance, corporate governance, and risk management in a common forum. The main objective of EMFRM 2024 is to promote economic development research, another objective is to facilitate the exchange of scientific information among researchers, developers, engineers, students, and practitioners around the world. We are confident that the participants benefited from the theoretical insights, practical knowledge, and personal contacts that will help them to build a long-term and sustainable communication among researchers and practitioners in related scientific areas. EMFRM 2024 invited scholars and experts from various universities to participate in the review and guidance of this conference. The conference focuses on the latest research fields, aims to provide an international cooperation and exchange platform for experts, scholars, and business managers to share their research achievements. This collection of proceedings compiles oral and paper presentations submitted by the authors and scrutinized by the special committee. EMFRM Organizing Committees Dubai, United Arab Emirates

Risk control and management are becoming increasingly important in many organisational operations. It goes beyond the business sector to include security organisations like police departments. The fact that police officers engage in a widerange of daily activities that expose them to a variety of potentially hazardous situations Police risk management includes both physical and financial aspects. Risk identification is the first step in the risk management process, followed by risk analysis, risk evaluation, and risk treatment. According to the literature, the Dubai Police Department has no framework in place to fully manage risks and evaluate the effectiveness of police operations. Early warning systems are critical for detecting potential severe RM because police departments must rely on them to manage risks and keep track of any potential new risks. As a result, the UAE police department's risk management strategy was investigated in this paper. It classified the data collection process using exploratory factor analysis, which wasa quantitative approach. A self-administered survey yielded 381 valid questionnaires from Dubai Police Department officers. The study's findings revealed that risk identification, risk analysis, risk evaluation, and risk response are the four fundamental steps of effective risk management. The Crobach Alpha test was used to assess the consistency and reliability of the data, and the result was 0.814, indicating that the data is suitable for use. Only one of the twenty indicators, which were divided into four groups for risk management, was weak according to outliers and communalities tests. The risk management process was accurately described by another 20 factors. Total variance extraction yielded a rate of 57.130 percent, which was higher than the required 50%. As a result, the risk management procedure investigated in this study may be useful for effectively managing risks in the UAE police force.

The reasonable pricing of options can effectively help investors avoid risks and obtain benefits, which plays a very important role in the stability of the financial market. The traditional single option pricing model often fails to meet the ideal expectations due to its limited conditions. Combining an economic model with a deep learning model to establish a hybrid model provides a new method to improve the prediction accuracy of the pricing model. This includes the usage of real historical data of about 10,000 sets of CSI 300 ETF options from January to December 2020 for experimental analysis. Aiming at the prediction problem of CSI 300ETF option pricing, based on the importance of random forest features, the Convolutional Neural Network and Long Short-Term Memory model (CNN-LSTM) in deep learning is combined with a typical stochastic volatility Heston model and stochastic interests CIR model in parameter models. The dual hybrid pricing model of the call option and the put option of CSI 300ETF is established. The dual-hybrid model and the reference model are integrated with ridge regression to further improve the forecasting effect. The results show that the dual-hybrid pricing model proposed in this paper has high accuracy, and the prediction accuracy is tens to hundreds of times higher than the reference model; moreover, MSE can be as low as 0.0003. The article provides an alternative method for the pricing of financial derivatives.

Investments into wind power generation may be hampered by the uncertainty of future revenues caused by the natural variability of the wind resource, the impact of climate change on wind potential and future electricity prices, and the regulatory risks. We quantify the uncertainty of the economic value of wind farms in France, Germany, and Denmark, and evaluate the cost of support mechanisms needed to ensure the profitability of wind farms under present and future climates. To this end, we built a localised model for wind power output and a country-level model for electricity demand and prices. Our study reveals that support mechanisms are needed for current market conditions and the current climate, as well as under future climate conditions according to several scenarios for climate change and energy transition. The cost of support mechanisms during a 15-year period is evaluated to EUR 3.8 to EUR 11.5 billion per year in France, from EUR 15.5 to EUR 26.5 billion per year in Germany, and from EUR 1.2 to EUR 3.3 billion per year in Denmark, depending on the scenario considered and the level of penetration of wind energy.

Quantified investment refers to quantitative investment model and the way of issuing orders by programming, so as to obtain stable returns. In recent years quantitative investment is increasingly valued by institutional investors and hedge funds in terms of its discipline, systematicness, timeliness and decentralization. From the perspective of the effectiveness of China's securities market and the development experience of foreign securities market, the prospect of quantified investment is worth looking forward to. However, domestic quantitative investment products still have shortcomings such as small overall size, single quantitative strategy, differentiation of strategic performance. Therefore, it is of great significance to study the new quantitative investment mode and to dig out new modeling ideas to enrich the quantitative investment products, improve the market scale and promote the development of quantitative investment. Based on the method indicated by Eugene Fama and Kenneth French and using public information from China A-share market. This paper attempts to establish a multi-factor model which is able to explain the stock price in Chinese stock market. The model is based on four fundamental factors, including liquidity, profitability, growth opportunity and earning revision. In addition, four fundamental factors are further derived to ten factors. The effectiveness of the ten-factor model is tested by using historical data. The results show that this model could beat the market standard effectively and provide relatively stable excess income.

of thesis entitled “Modeling Electricity Prices for Generation Investment and Scheduling Analysis” Submitted by Yang HE for the degree of Doctor of Philosophy at The University of Hong Kong in February 2010 In the deregulated electric power industry, under the market environment, electricity spot prices are highly volatile and uncertain. For private generation companies, their profits are directly tied with and significantly affected by these fluctuations of electricity spot prices. To make decisions on building new power plants and scheduling the production thereafter, generation companies desire an electricity spot price model to assist their making these decisions. The system of electricity spot prices is of high complexity; it is driven by various physical underlying forces that play in different timescales. In the short time horizon of one week, the physical driving forces are intra-day and intra-week variations of electricity load, generation forced outages, etc.; in the mid-term of one year, it is the seasonal forces that are manifest, such as seasonal weather and temperature, annual generation maintenance, etc.; and in the time horizon of years and decades, the effecting physical forces are economic development and economic cycles, generation investment and retirement, fluctuations of fuel prices, etc. This work develops a Multi-granularity Framework to facilitate analyzing electricity spot prices, which views electricity spot prices in three timeperspectives, that is, multi-year yearly, intra-year weekly, and intra-week hourly. In each time-perspective, how the various underlying physical forces give rise to the very peculiar behaviors of electricity spot prices is carefully discussed. Because the physical forces that underlie electricity spot prices are independent to each other, play in different timescales, and affect electricity spot prices in different time horizons, this work adopts the methodology Divide and Conquer to build the price model: it decomposes the historical electricity spot price data into components that are driven by different and independent physical underlying forces, then models each price component respectively, and finally constructs a complete electricity spot price model out of the resulting sub-models. The overall price model explicitly considers the various physical forces that drive electricity spot prices, the model extends on a time horizon of multiple years and has a time unit of one hour, and its final result represents prices at each hour by a probability density function of Lognormal distribution. The model has been evaluated in the New-England and PJM electricity markets. Upon proper revisions, the same analysis framework and modeling methodology probably could be applied to many other electricity markets in the world. The proposed price model is physically grounded, mathematically simple, and computationally fast. It provides an analytical tool to generation companies for their making informed decisions in generation investment and scheduling analysis. Besides generation companies, the model could also be widely used by other players in electricity markets, like by power traders for pricing and trading electricity contracts, futures, options, and other electricity derivatives, and by power retailers and large power consumers for their power purchase and risk management.