Antonios Alexandridis | University of Kent (original) (raw)

Uploads

Books by Antonios Alexandridis

Papers by Antonios Alexandridis

Weather derivatives are financial instruments that can be used by organizations or individuals as... more Weather derivatives are financial instruments that can be used by organizations or individuals as part of a risk management strategy to reduce risk associated with adverse or unexpected weather conditions. Just as traditional contingent claims, whose payoffs depend upon the price of some fundamental, a weather derivative has an underlying measure such as: rainfall, temperature, humidity or snowfall. In this thesis the problem of pricing weather futures written on various temperature indices, as well as weather options on weather futures is addressed. In order to accurately price weather derivatives based on temperature indices, first, a model that describes the evolution of the daily average temperature was developed. This thesis provides a concise and rigorous treatment of the stochastic modelling of weather market. The Ornstein-Uhlenbeck process is described as the basic modelling tool for daily average temperature dynamics, while the innovations are driven by a Brownian motion. W...

28th Annual European Real Estate Society Conference

The version in the Kent Academic Repository may differ from the final published version. Users ar... more The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

SSRN Electronic Journal, 2022

IFIP Advances in Information and Communication Technology, 2009

Communications in Computer and Information Science, 2013

In this paper, we use wavelet analysis to localize in Paris, France, a mean-reverting Ornstein-Uh... more In this paper, we use wavelet analysis to localize in Paris, France, a mean-reverting Ornstein-Uhlenbeck process with seasonality in the level and volatility. Wavelet analysis is an extension of the Fourier transform, which is very well suited to the analysis of non-stationary signals. We use wavelet analysis to identify the seasonality component in the temperature process as well as in the volatility of the temperature anomalies (residuals). Our model is validated on more than 100 years of data collected from Paris, one of the European cities traded at Chicago Mercantile Exchange. We also study the effect of replacing the original AR(1) process with ARMA, ARFIMA and ARFIMA-FIGARCH models, and the impact of the temperature outliers on the normality of the temperature anomalies.

Communications in Computer and Information Science, 2009

The weather derivatives market.- Introduction to Stochastic Calculus.- Handling the data.- Pricin... more The weather derivatives market.- Introduction to Stochastic Calculus.- Handling the data.- Pricing approaches of temperature.- Modeling the daily average temperature.- Pricing temperature derivatives.- The use of meteorological forecasts.- The effects of the geographical and basis risk.- Pricing the power of the wind a. Introduction to wind derivatives.- Precipitation Derivatives a. Introduction.- Rainfall Derivatives.- Snow Derivatives.- Appendix A.- Appendix B.- Index.- References.

Weather Derivatives, 2012

28th Annual European Real Estate Society Conference

SSRN Electronic Journal, 2022

IFIP Advances in Information and Communication Technology, 2009

Communications in Computer and Information Science, 2013

Communications in Computer and Information Science, 2009

Weather Derivatives, 2012

Wind is considered to be a free, renewable and environmentally friendly source of energy. However... more Wind is considered to be a free, renewable and environmentally friendly source of energy. However, wind farms are exposed to excessive weather risk since the power production depends on the wind speed and the wind direction. This risk can be successfully hedged using a financial instrument called weather derivatives. In this study the dynamics of the wind generating process are modeled using a non-parametric non-linear wavelet network. Our model is validated in New York. The proposed methodology is compared against alternative methods, proposed in prior studies. We find that wavelet networks can model the wind process very well and consequently they constitute an accurate and efficient tool for wind derivatives pricing. Finally, we provide the pricing equations for wind futures.

In this paper, we use wavelet neural networks in order to model a mean-reverting Ornstein-Uhlenbe... more In this paper, we use wavelet neural networks in order to model a mean-reverting Ornstein-Uhlenbeck temperature process, with seasonality in the level and volatility. We forecast up to two months ahead out of sample daily temperatures and we simulate the corresponding Cumulative Average Temperature and Heating Degree Day indices. The proposed model is validated in 8 European and 5 USA cities all traded in Chicago Mercantile Exchange. Our results suggest that the proposed method outperforms alternative pricing methods proposed in prior studies in most cases. Our findings suggest that wavelet networks can model the temperature process very well and consequently they constitute a very accurate and efficient tool for weather derivatives pricing. Finally, we provide the pricing equations for temperature futures on Heating Degree Day index.

The scope of this study is to present a complete statistical framework for model identification o... more The scope of this study is to present a complete statistical framework for model identification of wavelet neural networks (WN). Model identification can be separated in two parts, model selection and variable significance testing. In each step in WN construction we test various methods already proposed in literature. In the first part we compare four different methods for the initialization and construction of the WN. A wavelet is a waveform of effectively limited duration that has an average value of zero and localized properties hence an appropriate initialization reduces training times and leads to the global minimum of the minimization of the loss function. Next various information criteria as well as sampling techniques proposed in previous works were compared in order to derive an algorithm for selecting the correct topology of a WN. In variable significance testing the performance of various sensitivity and model-fitness criteria were examined and an algorithm for selecting the significant explanatory variables is presented. Finally the partial derivatives with respect to the weights of the network, to the dilation and translation parameters as well as the derivative with respect to each input variable are presented.

In this paper, we try to investigate the factors that affect crude oil price for the period 1988 ... more In this paper, we try to investigate the factors that affect crude oil price for the period 1988 – 2008 comparing a linear and a non-linear approach. We studied the dynamics of the West Texas Intermediate crude oil price time series as well as the relation of crude oil price and returns to various explanatory variables. First we use wavelet analysis to extract the driving forces and dynamics of the crude oil price and returns processes. Wavelet analysis brought out events and breaks of the time series that were not originally visible. Moreover examining the wavelet transform of past events we can make assumptions about the future behavior of oil prices. Moreover we examine if a wavelet neural network estimator can provide some incremental value in understanding the crude oil price process. Unlike the linear model wavelet network findings are in line with those of economic analysts. Wavelet networks not only have better predictive power in forecasting crude oil returns, but can also model correctly the dynamics of returns. In addition the sensitivity of crude oil prices and returns were examined and the time evolution of the effect of each predictor to the crude oil returns presented. Finally, forecasted values of crude oil returns were presented under various scenarios.

In this paper we use wavelet neural networks to forecast cash money withdrawals in different loca... more In this paper we use wavelet neural networks to forecast cash money withdrawals in different locations in the UK. Cash demand needs to be forecasted accurately similarly to other products in vending machines, as an inventory of cash money needs to be ordered and replenished for a set period of time beforehand. If the forecasts are flawed, they induce costs: if the forecast is too high unused money is stored in the ATM incurring costs to the institution, similarly, if the ATM runs out of cash, profit is lost and customers are dissatisfied. Cash money demand represents a non-stationary, heteroscedastic process. The time series exhibits trends, singularities, seasonal and irregular structural components of the data as well as causal forces impacting on the data generating process. Having limited domain knowledge and no information on the causal forces we use wavelet analysis to extract the dynamics of the process. In order to evaluate our method we produce in-sample and out-of-sample forecasts in 11 different time series. The data provided by the Neural Network Association and first presented in the NN5 competition

According to International Energy Outlook 2007 the total world demand of energy is projected to i... more According to International Energy Outlook 2007 the total world demand of energy is projected to increase through 2030 about 95% for the non-OECD region and 24% for OECD nations. Crude oil is one of the most critical energy commodities while with coal and natural gas are projected to provide roughly the 86% share of the total US primary energy supply in 2030. In this paper, we use wavelet neural networks to forecast monthly West Texas Intermediate (WTI) crude oil spot prices. As explanatory variables we consider price lags, the producer price index for petroleum and the world production of crude oil. The data are provided by the Energy Information Administration (EIA). The proposed model is used to forecast in-sample and out-of-sample. We forecast one, three and six month future prices of crude oil and we compare our estimates with the EIA’s STEO econometric forecasting model.

In this paper we use wavelet neural networks to model and remove the seasonal cycle as well as an... more In this paper we use wavelet neural networks to model and remove the seasonal cycle as well as any possible trends, singularities or jumps of the temperature process. Moreover, we give a complete framework for structuring and training feed forward wavelet neural networks via back-propagation. As we demonstrate here, wavelet networks simplify significantly the mathematics of weather derivatives pric-ing, since no particular functional form is assumed. Our findings suggest that wave-let networks can model the temperature process very well and consequently they constitute a very accurate and efficient tool for weather derivatives pricing.

In this paper, in the context of an Ornstein-Uhlenbeck temperature process we use neural networks... more In this paper, in the context of an Ornstein-Uhlenbeck temperature process we use neural networks to examine the time dependence of the speed of the mean reversion parameter α of the process. We estimate non-parametrically with a neural network a model of the temperature process and then we compute the derivative of the network output w.r.t. the network input, in order to obtain a series of daily values for α. To our knowledge, this is done for the first time, and it gives us a much better insight in temperature dynamics and in temperature derivative pricing. Our results indicate strong time dependence in the daily values of α but no seasonal patterns. This is important, since in all relevant studies so far, α was assumed to be constant. Furthermore, the residuals of the neural network provide a better fit to the normal distribution, when compared with the residuals of the classic linear models which are being used in the context of temperature modeling (where α is constant). It follows, that by setting the mean reversion parameter to be a function of time we improve the accuracy of the pricing of the temperature derivatives. Finally, we provide the pricing equations for temperature futures and options, when α is time dependent.

In this paper, we use neural networks in order to model the seasonal component of the residual va... more In this paper, we use neural networks in order to model the seasonal component of the residual variance of a mean-reverting Ornstein-Uhlenbeck temperature process, with seasonality in the level and volatility. We also use wavelet analysis to identify the seasonality component in the temperature process as well as in the volatility of the temperature anomalies. Our model is validated on more than 100 years of data collected from Paris, one of the European cities traded at Chicago Mercantile Exchange. Our results show a significant improvement over more traditional alternatives, regarding the statistical properties of the temperature process, which can be used in the context of Monte-Carlo simulations for pricing weather derivatives.