Kamm, Kevin
  
(2023)
A Unified Model for XVA, including Interest Rates and Rating, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. 
 Dottorato di ricerca in 
Matematica, 35 Ciclo. DOI 10.48676/unibo/amsdottorato/10543.
  
 
  
  
        
        
        
  
  
  
  
  
  
  
    
  
    
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      Abstract
      We start in Chapter 2 to investigate linear matrix-valued SDEs and the Itô-stochastic Magnus expansion. 
The Itô-stochastic Magnus expansion provides an efficient numerical scheme to solve matrix-valued
SDEs. We show convergence of the expansion up to a stopping time τ and provide an asymptotic estimate of the cumulative distribution function of τ. Moreover, we show how
to apply it to solve SPDEs with one and two spatial dimensions by combining it with the method of lines with high accuracy. We will see that the Magnus expansion allows us to use GPU techniques leading to major performance improvements compared to a standard Euler-Maruyama scheme.
In Chapter 3, we study a short-rate model in a Cox-Ingersoll-Ross (CIR) framework for
negative interest rates. We define
the short rate as the difference of two independent CIR processes and add a deterministic shift to guarantee a perfect fit to the market term structure. We show how to use the Gram-Charlier expansion to efficiently calibrate the model to the market swaption surface and price Bermudan swaptions with good accuracy.
We are taking two different perspectives for rating transition modelling. In Section 4.4, we study inhomogeneous
continuous-time Markov chains (ICTMC) as a candidate for a rating model with deterministic rating transitions. We extend this model by taking a Lie group perspective in Section 4.5, to allow for stochastic rating transitions.
In both cases, we will compare the most popular choices for a change of measure technique and show how to efficiently calibrate both models to the available historical rating data and market default probabilities.
At the very end, we apply the techniques shown in this thesis to minimize the collateral-inclusive Credit/ Debit Valuation Adjustments under the constraint of small collateral postings by using a collateral account dependent on rating trigger.
     
    
      Abstract
      We start in Chapter 2 to investigate linear matrix-valued SDEs and the Itô-stochastic Magnus expansion. 
The Itô-stochastic Magnus expansion provides an efficient numerical scheme to solve matrix-valued
SDEs. We show convergence of the expansion up to a stopping time τ and provide an asymptotic estimate of the cumulative distribution function of τ. Moreover, we show how
to apply it to solve SPDEs with one and two spatial dimensions by combining it with the method of lines with high accuracy. We will see that the Magnus expansion allows us to use GPU techniques leading to major performance improvements compared to a standard Euler-Maruyama scheme.
In Chapter 3, we study a short-rate model in a Cox-Ingersoll-Ross (CIR) framework for
negative interest rates. We define
the short rate as the difference of two independent CIR processes and add a deterministic shift to guarantee a perfect fit to the market term structure. We show how to use the Gram-Charlier expansion to efficiently calibrate the model to the market swaption surface and price Bermudan swaptions with good accuracy.
We are taking two different perspectives for rating transition modelling. In Section 4.4, we study inhomogeneous
continuous-time Markov chains (ICTMC) as a candidate for a rating model with deterministic rating transitions. We extend this model by taking a Lie group perspective in Section 4.5, to allow for stochastic rating transitions.
In both cases, we will compare the most popular choices for a change of measure technique and show how to efficiently calibrate both models to the available historical rating data and market default probabilities.
At the very end, we apply the techniques shown in this thesis to minimize the collateral-inclusive Credit/ Debit Valuation Adjustments under the constraint of small collateral postings by using a collateral account dependent on rating trigger.
     
  
  
    
    
      Tipologia del documento
      Tesi di dottorato
      
      
      
      
        
      
        
          Autore
          Kamm, Kevin
          
        
      
        
          Supervisore
          
          
        
      
        
          Co-supervisore
          
          
        
      
        
          Dottorato di ricerca
          
          
        
      
        
      
        
          Ciclo
          35
          
        
      
        
          Coordinatore
          
          
        
      
        
          Settore disciplinare
          
          
        
      
        
          Settore concorsuale
          
          
        
      
        
          Parole chiave
          Magnus expansion, SPDE, SDE, CIR, Interest Rates, Rating, XVA, Lie Groups, Deep Learning
          
        
      
        
          URN:NBN
          
          
        
      
        
          DOI
          10.48676/unibo/amsdottorato/10543
          
        
      
        
          Data di discussione
          29 Marzo 2023
          
        
      
      URI
      
      
     
   
  
    Altri metadati
    
      Tipologia del documento
      Tesi di dottorato
      
      
      
      
        
      
        
          Autore
          Kamm, Kevin
          
        
      
        
          Supervisore
          
          
        
      
        
          Co-supervisore
          
          
        
      
        
          Dottorato di ricerca
          
          
        
      
        
      
        
          Ciclo
          35
          
        
      
        
          Coordinatore
          
          
        
      
        
          Settore disciplinare
          
          
        
      
        
          Settore concorsuale
          
          
        
      
        
          Parole chiave
          Magnus expansion, SPDE, SDE, CIR, Interest Rates, Rating, XVA, Lie Groups, Deep Learning
          
        
      
        
          URN:NBN
          
          
        
      
        
          DOI
          10.48676/unibo/amsdottorato/10543
          
        
      
        
          Data di discussione
          29 Marzo 2023
          
        
      
      URI
      
      
     
   
  
  
  
  
  
    
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