arXiv · arXiv q-fin · 2024
Weather parametric insurance relies on weather indices rather than actual loss assessments, enhancing claims efficiency, reducing moral hazard, and improving fairness. In the context of increasing climate change risks, despite growing interest and demand,, weather parametric insurance's market share remains limited due to inherent basis risk, which is the mismatch between actual loss and payout, leading to loss witho…
Hang Gao, Shuohua Yang, Xinli Liu
arXiv · arXiv q-fin · 2022
Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements (including natural disasters, climate and weather conditions) and stock prices, using historical stock market data, historical climate data, and various clim…
Alapan Chaudhuri, Zeeshan Ahmed, Ashwin Rao, Shivansh Subramanian, Shreyas Pradhan
arXiv · arXiv q-fin · 2020
We present the closed-form solution to the problem of hedging price and quantity risks for energy retailers (ER), using financial instruments based on electricity price and weather indexes. Our model considers an ER who is intermediary in a regulated electricity market. ERs buy a fixed quantity of electricity at a variable cost and must serve a variable demand at a fixed cost. Thus ERs are subject to both price and q…
Javier Pantoja Robayo, Juan C. Vera
arXiv · arXiv q-fin · 2003
We price weather-contingent options by use of Monte Carlo simulations. After calibrating the models to fit quoted prices, we analyze bid-ask spreads in terms of correlations across markets. Results are presented for a double-trigger Weather vs. Natural Gas call option.
Rene' Carmona, Dario Villani
arXiv · arXiv q-fin · 2026
Prediction markets offer a natural testbed for trading agents: contracts have binary payoffs, prices can be interpreted as probabilities, and realized performance depends critically on market microstructure, fees, and settlement risk. We introduce PredictionMarketBench, a SWE-bench-style benchmark for evaluating algorithmic and LLM-based trading agents on prediction markets via deterministic, event-driven replay of h…
Avi Arora, Ritesh Malpani
arXiv · arXiv q-fin · 2023
Prediction markets allow traders to bet on potential future outcomes. These markets exist for weather, political, sports, and economic forecasting. Within this work we consider a decentralized framework for prediction markets using automated market makers (AMMs). Specifically, we construct a liquidity-based AMM structure for prediction markets that, under reasonable axioms on the underlying utility function, satisfy …
Hamed Amini, Maxim Bichuch, Zachary Feinstein
arXiv · arXiv q-fin · 2019
This book, which is in Spanish, provides detailed descriptions, including over 550 mathematical formulas, for over 150 trading strategies across a host of asset classes (and trading styles). This includes stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility (as an asset class), real estate, distressed assets, cash, cryptocurrencies, miscella…
Zura Kakushadze, Juan Andrés Serur
arXiv · arXiv q-fin · 2026
Two communities that rarely cite each other -- spatial statisticians fitting high-dimensional weather fields, and quantitative investors building portfolios -- have independently arrived at the same mathematical object: a Schur complement, damped by one interpretable parameter. In spatial modeling the Schur complement is the conditional covariance that makes a Gaussian (Vecchia) pseudo-likelihood estimable at scale, …
Peter Cotton
arXiv · arXiv q-fin · 2026
The rapid growth of weather-dependent renewable generation increases price volatility and imbalance penalty risk in power markets, creating the need for advanced quantitative trading strategies. We develop a data-driven continuous-time stochastic optimal control framework for intraday electricity trading using stochastic differential equations with drift terms ensuring mean reversion to deterministic forecast traject…
Chiheb Ben Hammouda, Michael Samet, Raúl Tempone
arXiv · arXiv q-fin · 2025
In power markets, Green Power Purchase Agreements have become an important contractual tool of the energy transition from fossil fuels to renewable sources such as wind or solar radiation. Trading Green PPAs exposes agents to price risks and weather risks. Also, developed electricity markets feature the so-called cannibalisation effect : large infeeds induce low prices and vice versa. As weather is a non-tradable ent…
Richard Biegler-König, Daniel Oeltz
arXiv · arXiv q-fin · 2023
An increasing share of energy is produced from renewable sources by many small producers. The efficiency of those sources is volatile and, to some extent, random, exacerbating the problem of energy market balancing. In many countries, this balancing is done on the day-ahead (DA) energy markets. This paper considers automated trading on the DA energy market by a medium-sized prosumer. We model this activity as a Marko…
Łukasz Lepak, Paweł Wawrzyński
arXiv · arXiv q-fin · 2022
We study how to assess the potential benefit of diversifying an equity portfolio by investing within and across equity sectors. We analyse 20 years of US stock price data, which includes the global financial crisis (GFC) and the COVID-19 market crash, as well as periods of financial stability, to determine the `all weather' nature of equity portfolios. We establish that one may use the leading eigenvalue of the cross…
Nick James, Max Menzies, Georg A. Gottwald
arXiv · arXiv q-fin · 2022
Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls …
Katarzyna Maciejowska, Bartosz Uniejewski, Rafał Weron
arXiv · arXiv q-fin · 2022
Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work and school commute patterns, crash rates can be determined with a reasonable degree of accuracy. However, this process becomes more complicated for an area that is frequently subj…
Darren Shannon, Grigorios Fountas
arXiv · arXiv q-fin · 2022
This paper develops a new stochastic volatility model for the temperature that is a natural extension of the Ornstein-Uhlenbeck model proposed by Benth and Benth (2007). This model allows to be more conservative regarding extreme events while keeping tractability. We give a method based on Conditional Least Squares to estimate the parameters on daily data and estimate our model on eight major European cities. We then…
Aurélien Alfonsi, Nerea Vadillo
arXiv · arXiv q-fin · 2020
We formulate an equilibrium model of intraday trading in electricity markets. Agents face balancing constraints between their customers consumption plus intraday sales and their production plus intraday purchases. They have continuously updated forecast of their customers consumption at maturity with decreasing volatility error. Forecasts are prone to idiosyncratic noise as well as common noise (weather). Agents prod…
René Aid, Andrea Cosso, Huyên Pham
arXiv · arXiv q-fin · 2018
In Europe, Germany is taking the lead in the switch from the conventional to renewable energy. This poses new challenges as wind and solar energy are fundamentally intermittent, weather-dependent and less predictable. It is therefore of considerable interest to investigate the evolution of price volatility in this post-transition era. There are a number of reasons, however, that makes the practical studies difficult.…
Abdolrahman Khoshrou, Eric J. Pauwels
arXiv · arXiv · 2026
Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge this persistent domain gap, we introduce a large-scale, dynamic dataset curated from visually complex AAA games. Using a novel dual-screen stitched capture method, we extracted 4M continuous frames (720p/30 FPS) of synchronized RGB and five G-…
Zheng-Hui Huang, Zhixiang Wang, Jiaming Tan, Ruihan Yu, Yidan Zhang