López Cabrera, Brenda
Overview
Works:  20 works in 54 publications in 1 language and 899 library holdings 

Roles:  Author 
Publication Timeline
.
Most widely held works by
Brenda López Cabrera
Statistics of financial markets : exercises and solutions by
Szymon Borak(
)
28 editions published between 2010 and 2013 in English and held by 833 WorldCat member libraries worldwide
"Practice makes perfect. Therefore the best method of mastering models is working with them. This book contains a large collection of exercises and solutions which will help explain the statistics of financial markets. These practical examples are carefully presented and provide computational solutions to specific problems, all of which are calculated using R and Matlab. This study additionally looks at the concept of corresponding Quantlets, the name given to these program codes and which follow the name scheme SFSxyz123. The book is divided into three main parts, in which option pricing, time series analysis and advanced quantitative statistical techniques in finance is thoroughly discussed. The authors have overall successfully created the ideal balance between theoretical presentation and practical challenges."Publisher's website
28 editions published between 2010 and 2013 in English and held by 833 WorldCat member libraries worldwide
"Practice makes perfect. Therefore the best method of mastering models is working with them. This book contains a large collection of exercises and solutions which will help explain the statistics of financial markets. These practical examples are carefully presented and provide computational solutions to specific problems, all of which are calculated using R and Matlab. This study additionally looks at the concept of corresponding Quantlets, the name given to these program codes and which follow the name scheme SFSxyz123. The book is divided into three main parts, in which option pricing, time series analysis and advanced quantitative statistical techniques in finance is thoroughly discussed. The authors have overall successfully created the ideal balance between theoretical presentation and practical challenges."Publisher's website
Weather risk management : CAT bonds and weather derivatives by
Brenda López Cabrera(
Book
)
5 editions published in 2010 in English and held by 33 WorldCat member libraries worldwide
5 editions published in 2010 in English and held by 33 WorldCat member libraries worldwide
Pricing of Asian temperature risk by
Fred Espen Benth(
)
3 editions published in 2009 in English and held by 4 WorldCat member libraries worldwide
Weather derivatives (WD) are different from most financial derivatives because the underlying weather cannot be traded and therefore cannot be replicated by other financial instruments. The market price of risk (MPR) is an important parameter of the associated equivalent martingale measures used to price and hedge weather futures/options in the market. The majority of papers so far have priced nontradable assets assuming zero MPR, but this assumption underestimates WD prices. We study the MPR structure as a time dependent object with concentration on emerging markets in Asia. We find that Asian Temperatures (Tokyo, Osaka, Beijing, Teipei) are normal in the sense that the driving stochastics are close to a Wiener Process. The regression residuals of the temperature show a clear seasonal variation and the volatility term structure of CAT temperature futures presents a modified Samuelson effect. In order to achieve normality in standardized residuals, the seasonal variation is calibrated with a combination of a fourier truncated series with a GARCH model and with a local linear regression. By calibrating model prices, we implied the MPR from Cumulative total of 24hour average temperature futures (C24AT) for Japanese Cities, or by knowing the formal dependence of MPR on seasonal variation, we price derivatives for Kaohsiung, where weather derivative market does not exist. The findings support theoretical results of reverse relation between MPR and seasonal variation of temperature process.  Weather derivatives ; continuous autoregressive model ; CAT ; CDD ; HDD ; risk premium
3 editions published in 2009 in English and held by 4 WorldCat member libraries worldwide
Weather derivatives (WD) are different from most financial derivatives because the underlying weather cannot be traded and therefore cannot be replicated by other financial instruments. The market price of risk (MPR) is an important parameter of the associated equivalent martingale measures used to price and hedge weather futures/options in the market. The majority of papers so far have priced nontradable assets assuming zero MPR, but this assumption underestimates WD prices. We study the MPR structure as a time dependent object with concentration on emerging markets in Asia. We find that Asian Temperatures (Tokyo, Osaka, Beijing, Teipei) are normal in the sense that the driving stochastics are close to a Wiener Process. The regression residuals of the temperature show a clear seasonal variation and the volatility term structure of CAT temperature futures presents a modified Samuelson effect. In order to achieve normality in standardized residuals, the seasonal variation is calibrated with a combination of a fourier truncated series with a GARCH model and with a local linear regression. By calibrating model prices, we implied the MPR from Cumulative total of 24hour average temperature futures (C24AT) for Japanese Cities, or by knowing the formal dependence of MPR on seasonal variation, we price derivatives for Kaohsiung, where weather derivative market does not exist. The findings support theoretical results of reverse relation between MPR and seasonal variation of temperature process.  Weather derivatives ; continuous autoregressive model ; CAT ; CDD ; HDD ; risk premium
Implied market price of weather risk by
Wolfgang Härdle(
)
1 edition published in 2009 in English and held by 3 WorldCat member libraries worldwide
Weather influences our daily lives and choices and has an enormous impact on cooperate revenues and earnings. Weather derivatives differ from most derivatives in that the underlying weather cannot be traded and their market is relatively illiquid. The weather derivative market is therefore incomplete. This paper implements a pricing methodology for weather derivatives that can increase the precision of measuring weather risk. We applied continous autoregressive models (CAR) with seasonal variation to model the temperature in Berlin and with that to get explicite nature of nonarbitrage prices for temperature derivatives. We infer the implied market price from Berlin cumulative monthly temperature futures that are traded at the Chicago Mercantile Exchange (CME), which is an important parameter of the associated equivalent martingale measures used to price and hedge weather future/options in the market. We propose to study the market price of risk, not only as a piecewise constant linear function, but also as a time dependent. In any of the previous cases, we found that the market price of weather risk is different from zero and shows a seasonal structure. With the extract information we price other exotic options, such as cooling/heating degree day temperatures and non standard contract with crazy maturities.  Weather derivatives ; weather risk ; weather forecasting ; seasonality ; continuous autoregressive model ; stochastic variance ; CAT index ; CDD index ; HDD index ; market price of risk ; risk premium ; CME
1 edition published in 2009 in English and held by 3 WorldCat member libraries worldwide
Weather influences our daily lives and choices and has an enormous impact on cooperate revenues and earnings. Weather derivatives differ from most derivatives in that the underlying weather cannot be traded and their market is relatively illiquid. The weather derivative market is therefore incomplete. This paper implements a pricing methodology for weather derivatives that can increase the precision of measuring weather risk. We applied continous autoregressive models (CAR) with seasonal variation to model the temperature in Berlin and with that to get explicite nature of nonarbitrage prices for temperature derivatives. We infer the implied market price from Berlin cumulative monthly temperature futures that are traded at the Chicago Mercantile Exchange (CME), which is an important parameter of the associated equivalent martingale measures used to price and hedge weather future/options in the market. We propose to study the market price of risk, not only as a piecewise constant linear function, but also as a time dependent. In any of the previous cases, we found that the market price of weather risk is different from zero and shows a seasonal structure. With the extract information we price other exotic options, such as cooling/heating degree day temperatures and non standard contract with crazy maturities.  Weather derivatives ; weather risk ; weather forecasting ; seasonality ; continuous autoregressive model ; stochastic variance ; CAT index ; CDD index ; HDD index ; market price of risk ; risk premium ; CME
A consistent twofactor model for pricing temperature derivatives by
Andreas Groll(
)
2 editions published in 2014 in English and held by 3 WorldCat member libraries worldwide
We analyze a consistent twofactor model for pricing temperature derivatives that incorporates the forward looking information available in the market by specifying a model for the dynamics of the complete meteorological forecast curve. The twofactor model is a generalization of the NelsonSiegel curve model by allowing factors with meanreversion to a stochastic mean for structural changes and seasonality for periodic patterns. Based on the outcomes of a statistical analysis of forecast data we conclude that the twofactor model captures well the stylized features of temperature forecast curves. In particular, a functional principal component analysis reveals that the model reflects reasonably well the dynamical structure of forecast curves by decomposing their shapes into a tilting and a bending factor. We continue by developing an estimation procedure for the model, before we derive explicit prices for temperature derivatives and calibrate the market price of risk (MPR) from temperature futures derivatives (CAT, HDD, CDD) traded at the Chicago Mercantile Exchange (CME). The factor model shows that the behavior of the implied MPR for futures traded in and out of the measurement period is more stable than other estimates obtained in the literature. This confirms that at least parts of the irregularity of the MPR is not due to irregular risk perception but rather due to information misspecification. Similar to temperature derivatives, this approach can be used for pricing other nontradable assets
2 editions published in 2014 in English and held by 3 WorldCat member libraries worldwide
We analyze a consistent twofactor model for pricing temperature derivatives that incorporates the forward looking information available in the market by specifying a model for the dynamics of the complete meteorological forecast curve. The twofactor model is a generalization of the NelsonSiegel curve model by allowing factors with meanreversion to a stochastic mean for structural changes and seasonality for periodic patterns. Based on the outcomes of a statistical analysis of forecast data we conclude that the twofactor model captures well the stylized features of temperature forecast curves. In particular, a functional principal component analysis reveals that the model reflects reasonably well the dynamical structure of forecast curves by decomposing their shapes into a tilting and a bending factor. We continue by developing an estimation procedure for the model, before we derive explicit prices for temperature derivatives and calibrate the market price of risk (MPR) from temperature futures derivatives (CAT, HDD, CDD) traded at the Chicago Mercantile Exchange (CME). The factor model shows that the behavior of the implied MPR for futures traded in and out of the measurement period is more stable than other estimates obtained in the literature. This confirms that at least parts of the irregularity of the MPR is not due to irregular risk perception but rather due to information misspecification. Similar to temperature derivatives, this approach can be used for pricing other nontradable assets
Designing an index for assessing wind energy potential(
)
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
To meet the increasing global demand for renewable energy such as wind energy, more and more new wind parks are installed worldwide. Finding a suitable location, however, requires a detailed and often costly analysis of the local wind conditions. Plain average wind speed maps cannot provide a precise forecast of wind power because of the nonlinear relationship between wind speed and production. In this paper, we suggest a new approach of assessing the local wind energy potential: Meteorological reanalysis data are applied to obtain longterm lowscale wind speed data at turbine location and hub height; then, with actual highfrequency production data, the relation between wind data and energy production is determined via a five parameter logistic function. The resulting wind energy index allows for a turbinespecific estimation of the expected wind power at an unobserved location. A map of wind power potential for whole Germany exemplifies the approach
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
To meet the increasing global demand for renewable energy such as wind energy, more and more new wind parks are installed worldwide. Finding a suitable location, however, requires a detailed and often costly analysis of the local wind conditions. Plain average wind speed maps cannot provide a precise forecast of wind power because of the nonlinear relationship between wind speed and production. In this paper, we suggest a new approach of assessing the local wind energy potential: Meteorological reanalysis data are applied to obtain longterm lowscale wind speed data at turbine location and hub height; then, with actual highfrequency production data, the relation between wind data and energy production is determined via a five parameter logistic function. The resulting wind energy index allows for a turbinespecific estimation of the expected wind power at an unobserved location. A map of wind power potential for whole Germany exemplifies the approach
Volatility linkages between energy and agricultural commodity prices by
Brenda López Cabrera(
)
1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide
In this paper we investigate price and volatility risk originating in linkages between energy and agricultural commodity prices in Germany and study their dynamics over time. We propose an econometric approach to quantify the volatility and correlation risk structure, which has a large impact for investment and hedging strategies of market participants as well as for policy makers. Volatilities and their short and long run linkages (spillovers) are analyzed using a dynamic conditional correlation GARCH model as well as a multivariate multiplicative volatility model. Our approach provides a flexible and accurate fitting procedure for volatility and correlation risk
1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide
In this paper we investigate price and volatility risk originating in linkages between energy and agricultural commodity prices in Germany and study their dynamics over time. We propose an econometric approach to quantify the volatility and correlation risk structure, which has a large impact for investment and hedging strategies of market participants as well as for policy makers. Volatilities and their short and long run linkages (spillovers) are analyzed using a dynamic conditional correlation GARCH model as well as a multivariate multiplicative volatility model. Our approach provides a flexible and accurate fitting procedure for volatility and correlation risk
Statistical modelling of temperature risk by Zografia Anastasiadou(
)
1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide
Recently the topic of global warming has become very popular. The literature has concentrated its attention on the evidence of such effect, either by detecting regime shifts or change points in time series. The majority of these methods are designed to find shifts in mean, but only few can do this for the variance. In this paper we attempt to investigate the statistical evidence of global warming by identifying shifts in seasonal mean of daily average temperatures over time and in seasonal variance of temperature residuals. We present a time series approach for modelling temperature dynamics. A seasonal mean Lassotype technique based with a multiplicative structure of Fourier and GARCH terms in volatility is proposed. The model describes well the stylised facts of temperature: seasonality, intertemporal correlations and the heteroscedastic behaviour of residuals. The application to European temperature data indicates that the multiplicative model for the seasonal variance performs better in terms of out of sample forecast than other models proposed in the literature for modelling temperature dynamics. We study the dynamics of the seasonal variance by implementing quantile and expectile functions with confidence corridor to detrended and deseasonalized residuals. We show that shifts in seasonal mean and variance vary from location to location, indicating that all sources of trends other than mean and variance would rise trends over spatial scales. The local effects of temperature risk support the existence of global warming.  Weather ; temperature ; seasonality ; variance ; global warming ; expectile ; quantile
1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide
Recently the topic of global warming has become very popular. The literature has concentrated its attention on the evidence of such effect, either by detecting regime shifts or change points in time series. The majority of these methods are designed to find shifts in mean, but only few can do this for the variance. In this paper we attempt to investigate the statistical evidence of global warming by identifying shifts in seasonal mean of daily average temperatures over time and in seasonal variance of temperature residuals. We present a time series approach for modelling temperature dynamics. A seasonal mean Lassotype technique based with a multiplicative structure of Fourier and GARCH terms in volatility is proposed. The model describes well the stylised facts of temperature: seasonality, intertemporal correlations and the heteroscedastic behaviour of residuals. The application to European temperature data indicates that the multiplicative model for the seasonal variance performs better in terms of out of sample forecast than other models proposed in the literature for modelling temperature dynamics. We study the dynamics of the seasonal variance by implementing quantile and expectile functions with confidence corridor to detrended and deseasonalized residuals. We show that shifts in seasonal mean and variance vary from location to location, indicating that all sources of trends other than mean and variance would rise trends over spatial scales. The local effects of temperature risk support the existence of global warming.  Weather ; temperature ; seasonality ; variance ; global warming ; expectile ; quantile
Forecast based pricing of weather derivatives by
Wolfgang Karl Härdle(
)
1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide
Forecasting based pricing of Weather Derivatives (WDs) is a new approach in valuation of contingent claims on nontradable underlyings. Standard techniques are based on historical weather data. Forwardlooking information such as meteorological forecasts or the implied market price of risk (MPR) are often not incorporated. We adopt a risk neutral approach (for each location) that allows the incorporation of meteorological forecasts in the framework of WD pricing. We study weather Risk Premiums (RPs) implied from either the information MPR gain or the meteorological forecasts. The size of RPs is interesting for investors and issuers of weather contracts to take advantages of geographic diversification, hedging effects and price determinations. By conducting an empirical analysis to London and Rome WD data traded at the Chicago Mercantile Exchange (CME), we find out that either incorporating the MPR or the forecast outperforms the standard pricing techniques.  Weather derivatives ; seasonal variation ; temperature ; risk premia
1 edition published in 2012 in English and held by 2 WorldCat member libraries worldwide
Forecasting based pricing of Weather Derivatives (WDs) is a new approach in valuation of contingent claims on nontradable underlyings. Standard techniques are based on historical weather data. Forwardlooking information such as meteorological forecasts or the implied market price of risk (MPR) are often not incorporated. We adopt a risk neutral approach (for each location) that allows the incorporation of meteorological forecasts in the framework of WD pricing. We study weather Risk Premiums (RPs) implied from either the information MPR gain or the meteorological forecasts. The size of RPs is interesting for investors and issuers of weather contracts to take advantages of geographic diversification, hedging effects and price determinations. By conducting an empirical analysis to London and Rome WD data traded at the Chicago Mercantile Exchange (CME), we find out that either incorporating the MPR or the forecast outperforms the standard pricing techniques.  Weather derivatives ; seasonal variation ; temperature ; risk premia
Localising temperature risk(
)
1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide
On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. In order to apply pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and inter temporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of this research is to estimate this heteroscedastic function so that after scale normalisation a pure standardised Gaussian variable appears. Earlier work investigated this temperature risk in dfferent locations and showed that neither parametric component functions nor a local linear smoother with constant smoothing parameter are flexible enough to generally describe the volatility process well. Therefore, we consider a local adaptive modeling approach to find at each time point, an optimal smoothing parameter to locally estimate the seasonality and volatility. Our approach provides a more flexible and accurate fitting procedure of localised temperature risk process by achieving excellent normal risk factors.  Weather derivatives ; localising temperature residuals ; seasonality ; local model selection
1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide
On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. In order to apply pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and inter temporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of this research is to estimate this heteroscedastic function so that after scale normalisation a pure standardised Gaussian variable appears. Earlier work investigated this temperature risk in dfferent locations and showed that neither parametric component functions nor a local linear smoother with constant smoothing parameter are flexible enough to generally describe the volatility process well. Therefore, we consider a local adaptive modeling approach to find at each time point, an optimal smoothing parameter to locally estimate the seasonality and volatility. Our approach provides a more flexible and accurate fitting procedure of localised temperature risk process by achieving excellent normal risk factors.  Weather derivatives ; localising temperature residuals ; seasonality ; local model selection
State Price Densities implied from weather derivatives by
Wolfgang Karl Härdle(
)
1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide
A State Price Density (SPD) is the density function of a risk neutral equivalent martingale measure for option pricing, and is indispensible for exotic option pricing and portfolio risk management. Many approaches have been proposed in the last two decades to calibrate a SPD using financial options from the bond and equity markets. Among these, non and semi parametric methods were preferred because they can avoid model misspecification of the underlying and thus give insight into complex portfolio propelling. However, these methods usually require a large data set to achieve desired convergence properties. Despite recent innovations in finan cial and insurance markets, many markets remain incomplete and there exists an illiquidity issue. One faces the problem in estimation by e.g. kernel techniques that there are not enough observations locally available. For this situation, we employ a Bayesian quadrature method because it allows us to incorporate prior assumptions on the model parameters and hence avoids problems with data sparsity. It is able to compute the SPD of both call and put options simultaneously, and is particularly robust when the market faces the illiquidity issue. By comparing our approach with other approaches, we show that the traditional way of estimating the SPD by differ entiating an interpolation of option prices does not hold in practice. As illustration, we calibrate the SPD for weather derivatives, a classical example of incomplete mar kets with financial contracts payoffs linked to nontradable assets, namely, weather indices. Finally, we study the dynamics of the implied SPD's and related to weather data.  weather derivatives ; temperature derivatives ; HDD ; CDD ; SPD ; mixture ; quadrature ; Bayesian ; Option trading Strategies ; illiquid
1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide
A State Price Density (SPD) is the density function of a risk neutral equivalent martingale measure for option pricing, and is indispensible for exotic option pricing and portfolio risk management. Many approaches have been proposed in the last two decades to calibrate a SPD using financial options from the bond and equity markets. Among these, non and semi parametric methods were preferred because they can avoid model misspecification of the underlying and thus give insight into complex portfolio propelling. However, these methods usually require a large data set to achieve desired convergence properties. Despite recent innovations in finan cial and insurance markets, many markets remain incomplete and there exists an illiquidity issue. One faces the problem in estimation by e.g. kernel techniques that there are not enough observations locally available. For this situation, we employ a Bayesian quadrature method because it allows us to incorporate prior assumptions on the model parameters and hence avoids problems with data sparsity. It is able to compute the SPD of both call and put options simultaneously, and is particularly robust when the market faces the illiquidity issue. By comparing our approach with other approaches, we show that the traditional way of estimating the SPD by differ entiating an interpolation of option prices does not hold in practice. As illustration, we calibrate the SPD for weather derivatives, a classical example of incomplete mar kets with financial contracts payoffs linked to nontradable assets, namely, weather indices. Finally, we study the dynamics of the implied SPD's and related to weather data.  weather derivatives ; temperature derivatives ; HDD ; CDD ; SPD ; mixture ; quadrature ; Bayesian ; Option trading Strategies ; illiquid
Pricing rainfall derivatives at the CME by
Brenda López Cabrera(
)
1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide
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 normalinverse Gaussian distribution, which captures asymmetries and heavytail 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.  weather derivatives ; precipitation ; Esscher transform ; market price of risk
1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide
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 normalinverse Gaussian distribution, which captures asymmetries and heavytail 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.  weather derivatives ; precipitation ; Esscher transform ; market price of risk
Forecasting generalized quantiles of electricity demand a functional data approach by
Brenda López Cabrera(
)
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
Electricity load forecasts are an integral part of many decisionmaking processes in the electricity market. However, most literature on electricity load forecasting concentrates on deterministic forecasts, neglecting possibly important information about uncertainty. A more complete picture of future demand can be obtained by using distributional forecasts, allowing for a more efficient decisionmaking. A predictive density can be fully characterized by tail measures such as quantiles and expectiles. Furthermore, interest often lies in the accurate estimation of tail events rather than in the mean or median. We propose a new methodology to obtain probabilistic forecasts of electricity load, that is based on functional data analysis of generalized quantile curves. The core of the methodology is dimension reduction based on functional principal components of tail curves with dependence structure. The approach has several advantages, such as flexible inclusion of explanatory variables including meteorological forecasts and no distributional assumptions. The methodology is applied to load data from a transmission system operator (TSO) and a balancing unit in Germany. Our forecast method is evaluated against other models including the TSO forecast model. It outperforms them in terms of mean absolute percentage error (MAPE) and achieves a MAPE of 2:7% for the TSO
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
Electricity load forecasts are an integral part of many decisionmaking processes in the electricity market. However, most literature on electricity load forecasting concentrates on deterministic forecasts, neglecting possibly important information about uncertainty. A more complete picture of future demand can be obtained by using distributional forecasts, allowing for a more efficient decisionmaking. A predictive density can be fully characterized by tail measures such as quantiles and expectiles. Furthermore, interest often lies in the accurate estimation of tail events rather than in the mean or median. We propose a new methodology to obtain probabilistic forecasts of electricity load, that is based on functional data analysis of generalized quantile curves. The core of the methodology is dimension reduction based on functional principal components of tail curves with dependence structure. The approach has several advantages, such as flexible inclusion of explanatory variables including meteorological forecasts and no distributional assumptions. The methodology is applied to load data from a transmission system operator (TSO) and a balancing unit in Germany. Our forecast method is evaluated against other models including the TSO forecast model. It outperforms them in terms of mean absolute percentage error (MAPE) and achieves a MAPE of 2:7% for the TSO
Localizing Temperature Risk(
)
1 edition published in 2016 in English and held by 1 WorldCat member library worldwide
ABSTRACT: On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. To apply the pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and intertemporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of this research is to estimate this heteroscedastic function so that, after scale normalization, a pure standardized Gaussian variable appears. Earlier works investigated temperature risk in different locations and showed that neither parametric component functions nor a local linear smoother with constant smoothing parameter are flexible enough to generally describe the variance process well. Therefore, we consider a local adaptive modeling approach to find, at each time point, an optimal smoothing parameter to locally estimate the seasonality and volatility. Our approach provides a more flexible and accurate fitting procedure for localized temperature risk by achieving nearly normal risk factors. We also employ our model to forecast the temperaturein different cities and compare it to a model developed in 2005 by Campbell and Diebold. Supplementary materials for this article are available online
1 edition published in 2016 in English and held by 1 WorldCat member library worldwide
ABSTRACT: On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. To apply the pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and intertemporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic pattern. The object of this research is to estimate this heteroscedastic function so that, after scale normalization, a pure standardized Gaussian variable appears. Earlier works investigated temperature risk in different locations and showed that neither parametric component functions nor a local linear smoother with constant smoothing parameter are flexible enough to generally describe the variance process well. Therefore, we consider a local adaptive modeling approach to find, at each time point, an optimal smoothing parameter to locally estimate the seasonality and volatility. Our approach provides a more flexible and accurate fitting procedure for localized temperature risk by achieving nearly normal risk factors. We also employ our model to forecast the temperaturein different cities and compare it to a model developed in 2005 by Campbell and Diebold. Supplementary materials for this article are available online
Timeadaptive probabilistic forecasts of electricity spot prices with application to risk management by
Brenda López Cabrera(
)
1 edition published in 2016 in English and held by 1 WorldCat member library worldwide
The increasing exposure to renewable energy has amplified the need for risk management in electricity markets. Electricity price risk poses a major challenge to market participants. We propose an approach to model and fore cast electricity prices taking into account information on renewable energy production. While most literature focuses on point forecasting, our method ology forecasts the whole distribution of electricity prices and incorporates spike risk, which is of great value for risk management. It is based on func tional principal component analysis and timeadaptive nonparametric density estimation techniques. The methodology is applied to electricity market data from Germany. We find that renewable infeed effect both, the location and the shape of spot price densities. A comparison with benchmark methods and an application to risk management are provided
1 edition published in 2016 in English and held by 1 WorldCat member library worldwide
The increasing exposure to renewable energy has amplified the need for risk management in electricity markets. Electricity price risk poses a major challenge to market participants. We propose an approach to model and fore cast electricity prices taking into account information on renewable energy production. While most literature focuses on point forecasting, our method ology forecasts the whole distribution of electricity prices and incorporates spike risk, which is of great value for risk management. It is based on func tional principal component analysis and timeadaptive nonparametric density estimation techniques. The methodology is applied to electricity market data from Germany. We find that renewable infeed effect both, the location and the shape of spot price densities. A comparison with benchmark methods and an application to risk management are provided
Pricing catastrophic bonds for earthquakes in Mexico(
)
1 edition published in 2006 in English and held by 1 WorldCat member library worldwide
1 edition published in 2006 in English and held by 1 WorldCat member library worldwide
Volatility linkages between energy and agricultural commodity prices by
Brenda López Cabrera(
)
1 edition published in 2013 in English and held by 1 WorldCat member library worldwide
1 edition published in 2013 in English and held by 1 WorldCat member library worldwide
Pricing rainfall derivatives at the CME by
Brenda López Cabrera(
)
1 edition published in 2013 in English and held by 1 WorldCat member library worldwide
1 edition published in 2013 in English and held by 1 WorldCat member library worldwide
Implied market price of weather risk by
Wolfgang Härdle(
Book
)
1 edition published in 2009 in English and held by 1 WorldCat member library worldwide
Weather influences our daily lives and choices and has an enormous impact on cooperate revenues and earnings. Weather derivatives differ from most derivatives in that the underlying weather cannot be traded and their market is relatively illiquid. The weather derivative market is therefore incomplete. This paper implements a pricing methodology for weather derivatives that can increase the precision of measuring weather risk. We applied continous autoregressive models (CAR) with seasonal variation to model the temperature in Berlin and with that to get explicite nature of nonarbitrage prices for temperature derivatives. We infer the implied market price from Berlin cumulative monthly temperature futures that are traded at the Chicago Mercantile Exchange (CME), which is an important parameter of the associated equivalent martingale measures used to price and hedge weather future/options in the market. We propose to study the market price of risk, not only as a piecewise constant linear function, but also as a time dependent. In any of the previous cases, we found that the market price of weather risk is different from zero and shows a seasonal structure. With the extract information we price other exotic options, such as cooling/heating degree day temperatures and non standard contract with crazy maturities.  Weather derivatives ; weather risk ; weather forecasting ; seasonality ; continuous autoregressive model ; stochastic variance ; CAT index ; CDD index ; HDD index ; market price of risk ; risk premium ; CME
1 edition published in 2009 in English and held by 1 WorldCat member library worldwide
Weather influences our daily lives and choices and has an enormous impact on cooperate revenues and earnings. Weather derivatives differ from most derivatives in that the underlying weather cannot be traded and their market is relatively illiquid. The weather derivative market is therefore incomplete. This paper implements a pricing methodology for weather derivatives that can increase the precision of measuring weather risk. We applied continous autoregressive models (CAR) with seasonal variation to model the temperature in Berlin and with that to get explicite nature of nonarbitrage prices for temperature derivatives. We infer the implied market price from Berlin cumulative monthly temperature futures that are traded at the Chicago Mercantile Exchange (CME), which is an important parameter of the associated equivalent martingale measures used to price and hedge weather future/options in the market. We propose to study the market price of risk, not only as a piecewise constant linear function, but also as a time dependent. In any of the previous cases, we found that the market price of weather risk is different from zero and shows a seasonal structure. With the extract information we price other exotic options, such as cooling/heating degree day temperatures and non standard contract with crazy maturities.  Weather derivatives ; weather risk ; weather forecasting ; seasonality ; continuous autoregressive model ; stochastic variance ; CAT index ; CDD index ; HDD index ; market price of risk ; risk premium ; CME
Statistics of Financial Markets : Exercises and Solutions(
)
1 edition published in 2010 in English and held by 0 WorldCat member libraries worldwide
1 edition published in 2010 in English and held by 0 WorldCat member libraries worldwide
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 Härdle, Wolfgang Author
 Borak, Szymon Author
 Ritter, Matthias
 Schulz, Franziska
 Odening, Martin
 Groll, Andreas Author
 Okhrin, Ostap
 Benth, Fred Author
 MeyerBrandis, Thilo
 Shen, Zhiwei
Covers
Alternative Names
Cabrera, Brenda López.
Cabrera, Brenda López 1980
LópezCabrera, Brenda.
LópezCabrera, Brenda 1980
Languages