Modeling asymmetric volatility in weekly Dutch temperature data

In addition to clear-cut seasonality in mean and variance, weekly Dutch temperature data appear to have a strong asymmetry in the impact of unexpectedly high or low temperatures on conditional volatility. Furthermore, this asymmetry also shows fairly pronounced seasonal variation. To describe these features, we propose a univariate seasonal time series model with asymmetric conditionally heteroskedastic errors. We fit this (and other, nested) model(s) to 25 years of weekly data. We evaluate itsforecasting performance for 5 years of hold-out data and find that the imposed asymmetry leads to better out-of-sample forecasts of temperaturevolatility.seasonal variation;asymmetric volatility;temperature volatility;weekly dutch temperature

Cross-city hedging with weather derivatives using bivariate DCC GARCH models

As monopolies gave their way to competitive wholesale electricity markets, volumetric risk came into play. Electricity supplier can buy weather derivatives to protect from volumetric risk due to unexpected weather conditions. However, contracts can only be negotiated for weather variables measured at few selected locations. To hedge their specific risk, electricity supplier have to correlate their risk with the risk at tradeable locations. In this paper, we concentrate on temperature derivatives. More precisely, we examine if and how bivariate GARCH models with dynamic conditional correlations can help in modelling correlation between two distinct temperature time series. The knowledge of correlation dynamics between the temperature time series enables an electricity supplier to correlate his risk with the risk of a traded city and to construct a sensible hedge. It turns out that the application of bivariate DCC GARCH models to three German temperature time series provides encouraging results. --

Is temperature-index derivative suitable for China?

In this paper, we assessed the suitability of temperature derivatives for China through modeling. We assumed that if the physical dynamics of temperature of some cities are identical, then the same types of temperature derivatives can be used in these cities. Nearly twenty years temperature data of forty-seven cities with traded temperature derivatives on the Chicago Mercantile Exchange Group (CME) and seven Chinese cities were collected and analyzed in a two-step approach. Firstly, the AR-EGARCH model capturing the shock asymmetry of the volatility of temperature is used to simulate the dynamics of temperature of the cities. Secondly, the temperature of the cities are classified through cluster analysis based on model parameters from the AR-EGARCH model. The results showed that the fitting effect of the AR-EGARCH model is very good, and only a few cities did not display the shock asymmetry. The model for Nanjing fitted well into one of the categories of the cities in the CME; but the other six Chinese cities belong to new categories, which are different from the cities in the CME. We concluded that HDD and CAT in Europe and CAT∗ in Japan can be used directly in Nanjing, but the existing temperature derivatives in CME were unsuitable for the other six Chinese cities. Recommendations for the establishment of weather derivatives market in China have been proposed

Exploring the financial risk of a temperature index: a fractional integrated approach

This paper introduces a new temperature index, which can suitably represent the underlying of a weather derivative. Such an index is defined as the weighted mean of daily average temperatures measured in different locations. It may be used to hedge volumetric risk, that is the effect of unexpected fluctuations in the demand/supply for some specific commodities—of agricultural or energy type, for example—due to unfavorable temperature conditions. We aim at exploring the long term memory property of the volatility of such an index, in order to assess whether there exist some long-run paths and regularities in its riskiness. The theoretical part of the paper proceeds in a stepwise form: first, the daily average temperatures are modeled through autoregressive dynamics with seasonality in mean and volatility; second, the assessment of the distributional hypotheses on the parameters of the model is carried out for analyzing the long term memory property of the volatility of the index. The theoretical results suggest that the single terms of the index drive the long memory of the overall aggregation; moreover, interestingly, the proper selection of the parameters of the model might lead both to cases of persistence and antipersistence. The applied part of the paper provides some insights on the behaviour of the volatility of the proposed index, which is built starting from single daily average temperature time series. This is a post-peer-review, pre-copyedit version of an article published in Annals of Operations Research. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10479-018-3063-

Price volatility transmission in the value chain of fresh anchovies in Spain

This study examines the price volatility transmission of fresh anchovies (Engraulis encrasicolus) among different markets along the value chain in Spain. For this purpose, the prices in the first-hand sale, wholesale, and retail markets are considered. A vector autoregressive (VAR) model and an asymmetric multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model are used to analyse the relationship of price volatility among the markets in the value chain. The results indicate that the retail market has the lowest volatility. Therefore, volatility in the first-hand sale and wholesale markets is only minimally transmitted to consumers. Finally, asymmetric effects are observed in the price volatility transmission along the fresh anchovy value chain.The authors wish to acknowledge the helpful comments of two anonymous referees, the Associate Editor Cameron Speir, and Tom Doan. This paper is part of R&D+I project RTI2018–099225-B-100, funded by MCIN/ AEI/10.13039/501100011033/ and the European Regional Development Fund (ERDF) ‘A way to make Europe’, and R&D+I project UHU-202046 (‘An´alisis Econ´omico de los Mercados del Boquer´on y la Sardina en Espa˜na. Impacto Sobre los Productores Andaluces’), funded by Andalucía ERDF 2014–20 OP. Funding for open access charge: Universidad de Huelva / CBUA

Application of Non-Linear Time Series Models to Power Risk Management : A Case Study for Germany

Die Liberalisierung des Energiesektors stellt Energiemanager vor neue Aufgaben. Sie müssen sich mit Marktpreis- und Mengenrisiken auseinandersetzen. Marktpreisrisiken spiegeln sich in der hohen Volatilität der Spotpreise wider, die hauptsächlich in der Nichtspeicherbarkeit von Strom begründet ist. Ferner ist die Nachfrage nach Strom stark wetterabhängig. Während milder Winter wird weniger Strom abgesetzt als erwartet. Folglich sind Stromproduzenten auch einem Mengenrisiko ausgesetzt. Um sich gegen Marktpreisrisiken abzusichern, sind geeignete Modelle für den Spotpreis notwendig. Diese Modelle können zur Bewertung von Derivaten auf dem Spotpreis als dem Underlying einerseits und zur operationalen Kurzfristoptimierung andererseits eingesetzt werden. Im ersten Teil der Arbeit diskutiert der Verfasser ausgewählte bestehende Ansätze zur Spotpreismodellierung und stellt einen neuen Ansatz vor. Außerdem untersucht der Verfasser den Einfluss von Wetter auf die Spotpreise. Im zweiten Teil der Arbeit, wird die Bivariate Modellierung von Temperaturzeitreihen diskutiert. Dies ist von Bedeutung für Cross- city hedging mit Wetterderivaten. Wetterderivate sind Finanzinstrumente, die es erlauben sich gegen Mengenrisiken hervorgerufen durch unvorhergesehene Wetterbedingungen abzusichern

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008