Based in the agrarian worlds of commercial sesame farming in northern Paraguay, where insurance companies are now selling weather derivatives to poor farmers, this article tracks financial practices that depend less on the healthy crops... more
Based in the agrarian worlds of commercial sesame farming in northern Paraguay, where insurance companies are now selling weather derivatives to poor farmers, this article tracks financial practices that depend less on the healthy crops and more on the weeds that thrive among the profitable plants. Parametric insurance operates like a derivative and is triggered by certain weather conditions, which raises questions about the limits of survivability for human-crop relations. I sketch out a series of concerns about weeds as an entry point and helpful heuristic for multiple overlapping kinds of speculation in a multispecies, capitalist, and troubled landscape. By grid- ding the world to a limited set of expedient parameters, what generative social and human grounds do we lose in the process? A speculative anthropological imaginary might posit “weedy finance” as a critical standpoint and set of political claims for casting climate-based finance as one of the lively systems that can and should be intentionally and selectively weeded out. [financialization; parametric insurance; weather; commercial agriculture; kinship; Paraguay]
In this study we develop a Lévy process driven Ornstein-Uhlenbeck daily temperature model. The model takes into account a time-dependent speed of mean reversion. It is statistically demonstrated that historical data and temperature... more
In this study we develop a Lévy process driven Ornstein-Uhlenbeck daily temperature model. The model takes into account a time-dependent speed of mean reversion. It is statistically demonstrated that historical data and temperature differences are not normally distributed and hence we have argued against modeling temperature residuals as a Wiener process rather we have used the normal inverse Gaussian distribution which can ably describe skewed and heavy tailed data. Neural networks have been applied to estimate parameters of the detrended and deseasonalized temperature data because there is no prior knowledge on the nature of the function that describes the speed of mean reversion in the model.
Weather risk is a crucial element of overall risk management for a wide variety of businesses (Cao, Li & Wei, 2003) in energy, agriculture, food, tourism and hospitality sectors. Particularly, hospitality businesses such as hotels,... more
Weather risk is a crucial element of overall risk management for a wide variety of businesses (Cao, Li & Wei, 2003) in energy, agriculture, food, tourism and hospitality sectors. Particularly, hospitality businesses such as hotels, restaurants and cafes are highly vulnerable when faced with unexpected weather conditions.
Në këtë punim është trajtuar kompania CME group, si një gjigand financiar i klerimit dhe i menaxhimit të riskut. Katër aspektet në të cilat u shqyrtua kjo kompani janë : dimensionet e operacioneve, projektimi i procesit, projektimi i... more
Në këtë punim është trajtuar kompania CME group, si një gjigand financiar i klerimit dhe i menaxhimit të riskut. Katër aspektet në të cilat u shqyrtua kjo kompani janë : dimensionet e operacioneve, projektimi i procesit, projektimi i produktit dhe zinxhiri i furnizimit. Duke qenë se kompania ofron një varietet të caktuar produktesh, fokusi është vendosur mbi një rast të vetëm si kontratat e mbrojtjes nga kushtet e motit. Si një produkt financiar i veçantë, në punim përshkruhen proceset e dizenjimit të këtij produkti si edhe krijimi i ekosistemeve specifike nga ana e bashkëpunëtorëve të CME, të cilët lidhin kërkesën dhe ofertën për marrje risku.
Italian policy against agricultural risks is briefly summarized; attention is given to infrastructural damages, and possible strategies based on weather derivatives are suggested. Weather derivatives change the shape of the financial... more
Italian policy against agricultural risks is briefly summarized; attention is given to infrastructural damages, and possible strategies based on weather derivatives are suggested. Weather derivatives change the shape of the financial losses probability distributions, and these new distributions are compared with the old ones by calculation of espected utilities; the utility function chosen is a modified logarithm and the calculations are performed by Monte Carlo simulation.
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... 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.
In this study we develop a Lévy process driven Ornstein-Uhlenbeck daily temperature model. The model takes into account a time-dependent speed of mean reversion. It is statistically demonstrated that historical data and temperature... more
In this study we develop a Lévy process driven Ornstein-Uhlenbeck daily temperature model. The model takes into account a time-dependent speed of mean reversion. It is statistically demonstrated that historical data and temperature differences are not normally distributed and hence we have argued against modeling temperature residuals as a Wiener process rather we have used the normal inverse Gaussian distribution which can ably describe skewed and heavy tailed data. Neural networks have been applied to estimate parameters of the detrended and deseasonalized temperature data because there is no prior knowledge on the nature of the function that describes the speed of mean reversion in the model.
Weather derivatives comprise efficient financial tools for managing hydrometeorological uncertainties in various markets. With ~46% utilization by the energy industry, weather derivatives are projected to constitute a critical element for... more
Weather derivatives comprise efficient financial tools for managing hydrometeorological uncertainties in various markets. With ~46% utilization by the energy industry, weather derivatives are projected to constitute a critical element for dealing with risks of low and medium impacts -contrary to standard insurance contracts that deal with extreme events. In this context, we design and engineer -via Monte Carlo pricing- a weather derivative for a remote island in Greece -powered by an autonomous diesel-fuelled generator- resembling to a standard call option contract to test the benefits for both the island’s public administration and a bank -as the transaction’s counterparty.
Comment:
This paper provides an overly simplified framework which can be used as a starting point for a more complete, thorough and accurate analysis of specific scenarios in which weather derivatives can find potential use. The model is easy to implement and associates costs and benefits of weather derivatives’ use in order to buffer the weather variation risks in remote islands based on autonomous energy units (especially those with high seasonal variation in population, e.g. tourist arrivals during summer periods), as well as to diversify the portfolio of the financial institutions involved and -thus- reduce their exposure.
Italian policy against agricultural risks is briefly summarized; attention is given to infrastructural damages, and possible strategies based on weather derivatives are suggested. Weather derivatives change the shape of the financial... more
Italian policy against agricultural risks is briefly summarized; attention is given to infrastructural damages, and possible strategies based on weather derivatives are suggested. Weather derivatives change the shape of the financial losses probability distributions, and these new distributions are compared with the old ones by calculation of espected utilities; the utility function chosen is a modified logarithm and the calculations are performed by Monte Carlo simulation.
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... 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 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... 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.
