Packham, Natalie
Overview
Works:  12 works in 17 publications in 2 languages and 49 library holdings 

Roles:  Author 
Publication Timeline
.
Most widely held works by
Natalie Packham
Credit dynamics in a firstpassage time model with jumps and Latin hypercube sampling with dependence by
Natalie Packham(
)
2 editions published in 2008 in English and held by 30 WorldCat member libraries worldwide
2 editions published in 2008 in English and held by 30 WorldCat member libraries worldwide
Latin hypercube sampling with dependence and applications in finance by
Natalie Packham(
Book
)
1 edition published in 2008 in English and held by 3 WorldCat member libraries worldwide
1 edition published in 2008 in English and held by 3 WorldCat member libraries worldwide
Credit dynamics in a first passage model with jumps by
Natalie Packham(
Book
)
2 editions published in 2009 in English and held by 3 WorldCat member libraries worldwide
2 editions published in 2009 in English and held by 3 WorldCat member libraries worldwide
Validierung von Konzepten zur Messung des Marktrisikos : insbesondere des Value at Risk und des Expected Shortfall by Fabian Mehmke(
)
1 edition published in 2012 in German and held by 3 WorldCat member libraries worldwide
Market risk management is one of the key factors to success in managing financial institutions. Underestimated risk can have desastrous consequences for individual companies and even whole economies, not least as could be seen during the recent crises. Overestimated risk, on the other side, may have negative effects on a company's capital requirements. Companies as well as national authorities thus have a strong interest in developing market risk models that correctly quantify certain key figures such as Value at Risk or Expected Shortfall. This paper presents several state of the art methods to evaluate the adequacy of almost any given market risk model. Existing models are enhanced by indepth analysis and simulations of statistical properties revealing some previously unknown effects, most notably inconsistent behaviour of alpha and beta errors. Furthermore, some new market risk validation models are introduced. In the end, a simulation with various market patterns demonstrates strenghts and weaknesses of each of the models presented under realistic conditions.  Backtesting ; Market Risk ; Value at Risk ; Expected Shortfall ; Validation ; Alpha Error ; Beta Error ; Time Until First Failure ; Proportion of Failure ; Traffic Light Approach ; Magnitude of Loss Function ; MarkowTest ; GaussTest ; Rosenblatt ; Kuiper ; KolmogorovSmirnov ; JarqueBera ; Regression ; Likelihood Ratio ; Truncated Distribution ; Censored Distribution ; Simulation
1 edition published in 2012 in German and held by 3 WorldCat member libraries worldwide
Market risk management is one of the key factors to success in managing financial institutions. Underestimated risk can have desastrous consequences for individual companies and even whole economies, not least as could be seen during the recent crises. Overestimated risk, on the other side, may have negative effects on a company's capital requirements. Companies as well as national authorities thus have a strong interest in developing market risk models that correctly quantify certain key figures such as Value at Risk or Expected Shortfall. This paper presents several state of the art methods to evaluate the adequacy of almost any given market risk model. Existing models are enhanced by indepth analysis and simulations of statistical properties revealing some previously unknown effects, most notably inconsistent behaviour of alpha and beta errors. Furthermore, some new market risk validation models are introduced. In the end, a simulation with various market patterns demonstrates strenghts and weaknesses of each of the models presented under realistic conditions.  Backtesting ; Market Risk ; Value at Risk ; Expected Shortfall ; Validation ; Alpha Error ; Beta Error ; Time Until First Failure ; Proportion of Failure ; Traffic Light Approach ; Magnitude of Loss Function ; MarkowTest ; GaussTest ; Rosenblatt ; Kuiper ; KolmogorovSmirnov ; JarqueBera ; Regression ; Likelihood Ratio ; Truncated Distribution ; Censored Distribution ; Simulation
Credit gap risk in a first passage time model with jumps by
Natalie Packham(
Book
)
3 editions published in 2009 in English and held by 3 WorldCat member libraries worldwide
The payoff of many credit derivatives depends on the level of credit spreads. In particular, credit derivatives with a leverage component are subject to gap risk, a risk associated with the occurrence of jumps in the underlying credit default swaps. In the framework of first passage time models, we consider a model that addresses these issues. The principal idea is to model a credit quality process as an Itô integral with respect to a Brownian motion with a stochastic volatility. Using a representation of the credit quality process as a timechanged Brownian motion, one can derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Lévydriven OrnsteinUhlenbeck process. The model can be implemented efficiently using a technique called Panjer recursion. Calibration to a wide range of dynamics is supported. We illustrate the effectiveness of the model by valuing a leveraged creditlinked note
3 editions published in 2009 in English and held by 3 WorldCat member libraries worldwide
The payoff of many credit derivatives depends on the level of credit spreads. In particular, credit derivatives with a leverage component are subject to gap risk, a risk associated with the occurrence of jumps in the underlying credit default swaps. In the framework of first passage time models, we consider a model that addresses these issues. The principal idea is to model a credit quality process as an Itô integral with respect to a Brownian motion with a stochastic volatility. Using a representation of the credit quality process as a timechanged Brownian motion, one can derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Lévydriven OrnsteinUhlenbeck process. The model can be implemented efficiently using a technique called Panjer recursion. Calibration to a wide range of dynamics is supported. We illustrate the effectiveness of the model by valuing a leveraged creditlinked note
Incentive schemes, private information and the doubleedged role of competition for agents by
Christina E Metz(
)
2 editions published between 2014 and 2016 in English and held by 2 WorldCat member libraries worldwide
This paper examines the effect of imperfect labor market competition on the efficiency of compensation schemes in a setting with moral hazard, private information and riskaverse agents. Two vertically differentiated firrms compete for agents by offering contracts with fixed and variable payments. Vertical differentiation between firms leads to endogenous, typedependent exit options for agents. In contrast to screening models with perfect competition, we find that existence of equilibria does not depend on whether the leastcost separating allocation is interim efficient. Rather, vertical differentiation allows the inferior firm to offer (cross)subsidizing fixed payments even above the interim efficient level. We further show that the efficiency of variable pay depends on the degree of competition for agents: For small degrees of competition, lowability agents are underincentivized and exert too little effort. For large degrees of competition, highability agents are overincentivized and bear too much risk. For intermediate degrees of competition, however, contracts are secondbest despite private information
2 editions published between 2014 and 2016 in English and held by 2 WorldCat member libraries worldwide
This paper examines the effect of imperfect labor market competition on the efficiency of compensation schemes in a setting with moral hazard, private information and riskaverse agents. Two vertically differentiated firrms compete for agents by offering contracts with fixed and variable payments. Vertical differentiation between firms leads to endogenous, typedependent exit options for agents. In contrast to screening models with perfect competition, we find that existence of equilibria does not depend on whether the leastcost separating allocation is interim efficient. Rather, vertical differentiation allows the inferior firm to offer (cross)subsidizing fixed payments even above the interim efficient level. We further show that the efficiency of variable pay depends on the degree of competition for agents: For small degrees of competition, lowability agents are underincentivized and exert too little effort. For large degrees of competition, highability agents are overincentivized and bear too much risk. For intermediate degrees of competition, however, contracts are secondbest despite private information
Default probabilities and default correlations under stress by
Natalie Packham(
)
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
We investigate default probabilities and default correlations of Mertontype credit portfolio models in stress scenarios where a common risk factor is truncated. The analysis is performed in the class of elliptical distributions, a family of lighttailed to heavytailed distributions encompassing many distributions commonly found in financial modelling. It turns out that the asymptotic limit of default probabilities and default correlations depend on the maxdomain of the elliptical distribution's mixing variable. In case the mixing variable is regularly varying, default probabilities are strictly smaller than 1 and default correlations are in (0; 1). Both can be expressed in terms of the Student tdistribution function. In the rapidly varying case, default probabilities are 1 and default correlations are 0. We compare our results to the tail dependence function and discuss implications for credit portfolio modelling
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
We investigate default probabilities and default correlations of Mertontype credit portfolio models in stress scenarios where a common risk factor is truncated. The analysis is performed in the class of elliptical distributions, a family of lighttailed to heavytailed distributions encompassing many distributions commonly found in financial modelling. It turns out that the asymptotic limit of default probabilities and default correlations depend on the maxdomain of the elliptical distribution's mixing variable. In case the mixing variable is regularly varying, default probabilities are strictly smaller than 1 and default correlations are in (0; 1). Both can be expressed in terms of the Student tdistribution function. In the rapidly varying case, default probabilities are 1 and default correlations are 0. We compare our results to the tail dependence function and discuss implications for credit portfolio modelling
Validierung von Konzepten zur Messung des Marktrisikos insbesondere des Value at Risk und des Expected Shortfall by Fabian Mehmke(
Book
)
1 edition published in 2012 in German and held by 2 WorldCat member libraries worldwide
1 edition published in 2012 in German and held by 2 WorldCat member libraries worldwide
Latin hypercube sampling with dependence and applications in finance by
Natalie Packham(
)
1 edition published in 2008 in English and held by 1 WorldCat member library worldwide
In Monte Carlo simulation, Latin hypercube sampling (LHS) [McKay et al. (1979)] is a wellknown variance reduction technique for vectors of independent random variables. The method presented here, Latin hypercube sampling with dependence (LHSD), extends LHS to vectors of dependent random variables. The resulting estimator is shown to be consistent and asymptotically unbiased. For the bivariate case and under some conditions on the joint distribution, a central limit theorem together with a closed formula for the limit variance are derived. It is shown that for a class of estimators satisfying some monotonicity condition, the LHSD limit variance is never greater than the corresponding Monte Carlo limit variance. In some valuation examples of financial payoffs, when compared to standard Monte Carlo simulation, a variance reduction of factors up to 200 is achieved. LHSD is suited for problems with rare events and for highdimensional problems, and it may be combined with QuasiMonte Carlo methods
1 edition published in 2008 in English and held by 1 WorldCat member library worldwide
In Monte Carlo simulation, Latin hypercube sampling (LHS) [McKay et al. (1979)] is a wellknown variance reduction technique for vectors of independent random variables. The method presented here, Latin hypercube sampling with dependence (LHSD), extends LHS to vectors of dependent random variables. The resulting estimator is shown to be consistent and asymptotically unbiased. For the bivariate case and under some conditions on the joint distribution, a central limit theorem together with a closed formula for the limit variance are derived. It is shown that for a class of estimators satisfying some monotonicity condition, the LHSD limit variance is never greater than the corresponding Monte Carlo limit variance. In some valuation examples of financial payoffs, when compared to standard Monte Carlo simulation, a variance reduction of factors up to 200 is achieved. LHSD is suited for problems with rare events and for highdimensional problems, and it may be combined with QuasiMonte Carlo methods
Credit dynamics in a first passage time model with jumps by
Natalie Packham(
)
1 edition published in 2009 in English and held by 0 WorldCat member libraries worldwide
The payoff of many credit derivatives depends on the level of credit spreads. In particular, the payoff of credit derivatives with a leverage component is sensitive to jumps in the underlying credit spreads. In the framework of first passage time models we extend the model introduced in [Overbeck and Schmidt, 2005] to address these issues. In the extended a model, a credit quality process is driven by an Itô integral with respect to a Brownian motion with stochastic volatility. Using a representation of the credit quality process as a timechanged Brownian motion, we derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Lévydriven OrnsteinUhlenbeck process. We show that jumps in the volatility translate into jumps in credit spreads. We examine the dynamics of the OSmodel and the extended model and provide examples
1 edition published in 2009 in English and held by 0 WorldCat member libraries worldwide
The payoff of many credit derivatives depends on the level of credit spreads. In particular, the payoff of credit derivatives with a leverage component is sensitive to jumps in the underlying credit spreads. In the framework of first passage time models we extend the model introduced in [Overbeck and Schmidt, 2005] to address these issues. In the extended a model, a credit quality process is driven by an Itô integral with respect to a Brownian motion with stochastic volatility. Using a representation of the credit quality process as a timechanged Brownian motion, we derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Lévydriven OrnsteinUhlenbeck process. We show that jumps in the volatility translate into jumps in credit spreads. We examine the dynamics of the OSmodel and the extended model and provide examples
Credit gap risk in a first passage time model with jumps by
Natalie Packham(
)
1 edition published in 2009 in English and held by 0 WorldCat member libraries worldwide
The payoff of many credit derivatives depends on the level of credit spreads. In particular, credit derivatives with a leverage component are subject to gap risk, a risk associated with the occurrence of jumps in the underlying credit default swaps. In the framework of first passage time models, we consider a model that addresses these issues. The principal idea is to model a credit quality process as an Itô integral with respect to a Brownian motion with a stochastic volatility. Using a representation of the credit quality process as a timechanged Brownian motion, one can derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Lévydriven OrnsteinUhlenbeck process. The model can be implemented efficiently using a technique called Panjer recursion. Calibration to a wide range of dynamics is supported. We illustrate the effectiveness of the model by valuing a leveraged creditlinked note
1 edition published in 2009 in English and held by 0 WorldCat member libraries worldwide
The payoff of many credit derivatives depends on the level of credit spreads. In particular, credit derivatives with a leverage component are subject to gap risk, a risk associated with the occurrence of jumps in the underlying credit default swaps. In the framework of first passage time models, we consider a model that addresses these issues. The principal idea is to model a credit quality process as an Itô integral with respect to a Brownian motion with a stochastic volatility. Using a representation of the credit quality process as a timechanged Brownian motion, one can derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Lévydriven OrnsteinUhlenbeck process. The model can be implemented efficiently using a technique called Panjer recursion. Calibration to a wide range of dynamics is supported. We illustrate the effectiveness of the model by valuing a leveraged creditlinked note
Latin hypercube sampling with dependence and applications in finance by
Natalie Packham(
)
1 edition published in 2008 in English and held by 0 WorldCat member libraries worldwide
In Monte Carlo simulation, Latin hypercube sampling (LHS) [McKay et al. (1979)] is a wellknown variance reduction technique for vectors of independent random variables. The method presented here, Latin hypercube sampling with dependence (LHSD), extends LHS to vectors of dependent random variables. The resulting estimator is shown to be consistent and asymptotically unbiased. For the bivariate case and under some conditions on the joint distribution, a central limit theorem together with a closed formula for the limit variance are derived. It is shown that for a class of estimators satisfying some monotonicity condition, the LHSD limit variance is never greater than the corresponding Monte Carlo limit variance. In some valuation examples of financial payoffs, when compared to standard Monte Carlo simulation, a variance reduction of factors up to 200 is achieved. LHSD is suited for problems with rare events and for highdimensional problems, and it may be combined with QuasiMonte Carlo methods
1 edition published in 2008 in English and held by 0 WorldCat member libraries worldwide
In Monte Carlo simulation, Latin hypercube sampling (LHS) [McKay et al. (1979)] is a wellknown variance reduction technique for vectors of independent random variables. The method presented here, Latin hypercube sampling with dependence (LHSD), extends LHS to vectors of dependent random variables. The resulting estimator is shown to be consistent and asymptotically unbiased. For the bivariate case and under some conditions on the joint distribution, a central limit theorem together with a closed formula for the limit variance are derived. It is shown that for a class of estimators satisfying some monotonicity condition, the LHSD limit variance is never greater than the corresponding Monte Carlo limit variance. In some valuation examples of financial payoffs, when compared to standard Monte Carlo simulation, a variance reduction of factors up to 200 is achieved. LHSD is suited for problems with rare events and for highdimensional problems, and it may be combined with QuasiMonte Carlo methods
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