Matthias Ritter

Meteorological Forecasts and the Pricing of Temperature Futures

Futures contracts on the weather may seemodd, yet weather risk can be a serious matter. Weather is plainly not a storable commodity, so the cost-of-carry pricing model does not apply. Instead, one expects a temperature futures contract to be priced close to the consensus expectation based on current information for the temperature variable at contract maturity. But everyone knows how difficult it is to predict the weather, even though some patterns are well understood (e.g., it is colder in the winter than in the summer, on average). In this article, the authors build and estimate temperature models for three major cities that have traded weather futures contracts. The models include seasonal variation, positive serial correlation over short periods, a possible longterm warming trend, and random fluctuation. To the model based on historically observed temperatures, they add forwardlooking weather forecasts from an online weather service. These forecast-augmented models are more accurate in matching market prices for weather futures, and they are even closer to futures prices than would be “perfect” forecasts constructed from realized temperature data

Pricing Rainfall Derivatives at the CME

Many business people such as farmers and financial investors are affected by indirect losses caused by scarce or abundant rainfall. Because of the high potential of insuring rainfall risk, the Chicago Mercantile Exchange (CME) began trading rainfall derivatives in 2011. Compared to temperature derivatives, however, pricing rainfall derivatives is more difficult. In this article, we propose to model rainfall indices via a flexible type of distribution, namely the normal-inverse Gaussian distribution, which captures asymmetries and heavy-tail behaviour. The prices of rainfall futures are computed by employing the Esscher transform, a wellknown tool in actuarial science. This approach is flexible enough to price any rainfall contract and to adjust theoretical prices to market prices by using the calibrated market price of risk. This empirical analysis is conducted with U.S. precipitation data and CME futures data providing first results on the market price of risk for rainfall derivatives.

Meteorological forecasts and the pricing of weather derivatives

In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the incorporation of meteorological forecasts in the framework of weather derivative pricing and is able to estimate the information gain compared to a benchmark model without meteorological forecasts. This approach is applied for temperature futures referring to New York, Minneapolis and Cincinnati with forecast data 13 days in advance. Despite this relatively short forecast horizon, the models using meteorological forecasts outperform the classical approach and more accurately forecast the market prices of the temperature futures traded at the Chicago Mercantile Exchange (CME). Moreover, a concentration on the last two months or on days with actual trading improves the results.

Land price diffusion across borders: The case of Germany (03/2018)

Land market regulations are often justified by the assumption that activities of foreign and nonagricultural investors drive up land prices in countries with low land price levels. However, empirical knowledge about the dynamics of agricultural land prices across borders is sparse. Using the German reunification as a natural experiment, we study the effect of the former inner German border on the dynamics of agricultural land prices in East and West Germany. We apply a land price diffusion model with an error correction specification that estimates to what extent agricultural land markets are spatially integrated. A novel feature of our model is its ability to distinguish price diffusion within states and across state borders. We find that local agricultural land markets in Germany are linked by a long-run equilibrium relationship. Spatial market integration, however, does not hold among all counties in our study area. Regarding our main research question, we provide evidence for a persistent border effect given that the fraction of spatially integrated counties is larger within states than across the former border. Moreover, we observe non-significant error correction terms for many counties along the former border. From a policy perspective, it is striking to realize that even 25 years after German reunification, pronounced land price differences persist. It is quite likely that price diffusion through existing borders within the EU would take even more time given language barriers, different administrative procedures for land acquisitions, different tax systems, and information asymmetries between domestic and foreign market participants.

Power Curve Monitoring with Flexible EWMA Control Charts

EWMA control charts have been used by several authors to monitor residuals of power curves for fault detection in wind turbines. In this study, we analyze the effect of non-constant variance in the residuals of a power curve on the performance of an EWMA control chart. We show that the variance can be modelled as a function of wind speed and use this insight to derive wind speed-adjusted control limits. A subsequent application of this procedure to a real data set spanning 2.5 years and on simulated data shows that it is able to slightly reduce the false alarm rate of the EWMA control chart.

Efficiency of Wind PowerProduction and itsDeterminants

This article examines the efficiency of wind energy production. Using non-convex efficiency analysis, we quantify production losses for 19 wind turbines in four wind parks across Germany. In a second stage regression, we adapt the linear regression results of Kneip, Simar, and Wilson (2014) to explain electricity losses by means of a bias-corrected truncated regression analysis. The results show that electricity losses amount to 27% of the maximal producible electricity. Most of these losses are from changing wind conditions, while 6% are from turbine errors.

Can the market forecast the weather better than meteorologists

Many companies depend on weather conditions, so they require reliable weather forecasts for production planning or risk hedging. In this article, we propose a new way of gaining weather forecasts by exploiting the forward-looking information included in the market prices of weather derivatives traded at the Chicago Mercantile Exchange (CME). For this purpose, the CME futures prices of two monthly temperature indices relevant for the energy sector are compared with index forecasts derived from meteorological temperature forecasts. It turns out that the market prices generally outperform the meteorological forecasts in predicting the outcome of the monthly index. Hence, companies whose prot strongly depends on these indices, such as energy companies, can prot from this additional information source about future weather.