Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel Genetic Programming (GP) algorithm for pricing contracts.
Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world.
In recent years big financial institutions are interested in creating and maintaining property valuation models. The main objective is to use reliable historical data in order to be able to forecast the price of a new property in a comprehensive manner and provide some indication for the uncertainty around this forecast. In this paper we develop an automatic valuation model (AVM) for property valuation using a large database of historical prices from Greece.
The Greek property market is an inefficient, nonhomogeneous market, still at its infancy and governed by lack of information. As a result modelling the Greek real estate market is a very interesting and challenging problem.
Rainfall is a crucial phenomenon within a climate system, whose chaotic nature has a direct influence on water resource planning, agriculture and biological systems. Within finance, the level of rainfall over a period of time is vital for estimating the value of a financial security. In this study we evaluate seven machine learning methods for rainfall prediction in the context of weather derivatives.
A Wavelet Network
The purpose of this study is to develop a model that describes the dynamics of the daily average temperature accurately in the context of weather derivatives pricing. More precisely, we compare two state-of-the-art machine learning algorithms, namely wavelet networks and genetic programming, with the classic linear approaches that are used widely in the pricing of temperature derivatives in the financial weather market, as well as with various machine learning benchmark models such as neural networks, radial basis functions and support vector regression. Continue reading
Motivated by the need of engineers for a flexible and reliable tool for estimating and predicting grounding systems behavior, this study developed a model that accurately describes and forecasts the dynamics of ground resistance variation. Continue reading
This is the second part of the 10 most popular question related to weather derivatives. You can read the first part here. Read the questions and answers from 6-10.
In this post I’ll try to make a brief introduction to weather derivatives in the form of Q&A. I will try to answer with simple words the ten most common questions related to weather derivatives. Read below the first part.