Replicating portfolio approach to capital calculation

The replicating portfolio (RP) approach to the calculation of capital for life insurance portfolios is an industry standard. The RP is obtained from projecting the terminal loss of discounted asset–liability cash flows on a set of factors generated by a family of financial instruments that can be efficiently simulated. We provide the mathematical foundations and a novel dynamic and path-dependent RP approach for real-world and risk-neutral sampling. We show that our RP approach yields asymptotically consistent capital estimators if the chaotic representation property holds. We illustrate the tractability of the RP approach by three numerical examples.     

Existence of Lévy term structure models

Lévy driven term structure models have become an important subject in the mathematical finance literature. This paper provides a comprehensive analysis of the Lévy driven Heath–Jarrow–Morton type term structure equation. This includes a full proof of existence and uniqueness in particular, which seems to have been lacking in the finance literature so far.      

Fed funds futures variance futures

We develop a novel contract design, the fed funds futures (FFF) variance futures, which reflects the expected realized basis point variance of an underlying FFF rate. The valuation of short-term FFF variance futures is completely model-independent in a general setting that includes the cases where the underlying FFF rate exhibits jumps and where the realized variance is computed by sampling the FFF rate discretely. The valuation of longer-term FFF variance futures is subject to an approximation error which we quantify and show is negligible. We also provide an illustrative example of the practical valuation and use of the FFF variance futures contract.     

A NOTE ON THE DAI–SINGLETON CANONICAL REPRESENTATION OF AFFINE TERM STRUCTURE MODELS*

Dai and Singleton (2000) study a class of term structure models for interest rates that specify the short rate as an affine combination of the components of an N‐dimensional affine diffusion process. Observable quantities in such models are invariant under regular affine transformations of the underlying diffusion process. In their canonical form, the models in Dai and Singleton (2000) are based on diffusion processes with diagonal diffusion matrices. This motivates the following question: Can the diffusion matrix of an affine diffusion process always be diagonalized by means of a regular affine transformation? We show that if the state space of the diffusion is of the form for integers satisfying or , there exists a regular affine transformation of D onto itself that diagonalizes the diffusion matrix. So in this case, the Dai–Singleton canonical representation is exhaustive. On the other hand, we provide examples of affine diffusion processes with state space whose diffusion matrices cannot be diagonalized through regular affine transformation. This shows that for ), the assumption of diagonal diffusion matrices may impose unnecessary restrictions and result in an avoidable loss of generality.     

The Jacobi stochastic volatility model

We introduce a novel stochastic volatility model where the squared volatility of the asset return follows a Jacobi process. It contains the Heston model as a limit case. We show that the joint density of any finite sequence of log-returns admits a Gram–Charlier A expansion with closed-form coefficients. We derive closed-form series representations for option prices whose discounted payoffs are functions of the asset price trajectory at finitely many time points. This includes European call, put and digital options, forward start options, and can be applied to discretely monitored Asian options. In a numerical study, we show that option prices can be accurately and efficiently approximated by truncating their series representations.     

MODEL UNCERTAINTY AND SCENARIO AGGREGATION

This paper provides a coherent method for scenario aggregation addressing model uncertainty. It is based on divergence minimization from a reference probability measure subject to scenario constraints. An example from regulatory practice motivates the definition of five fundamental criteria that serve as a basis for our method. Standard risk measures, such as value‐at‐risk and expected shortfall, are shown to be robust with respect to minimum divergence scenario aggregation. Various examples illustrate the tractability of our method.     

A general characterization of one factor affine term structure models

We give a complete characterization of affine term structure models based on a general nonnegative Markov short rate process. This applies to the classical CIR model but includes as well short rate processes with jumps. We provide a link to the theory of branching processes and show how CBI-processes naturally enter the field of term structure modelling. Using Markov semigroup theory we exploit the full structure behind an affine term structure model and provide a deeper understanding of some well-known properties of the CIR model. Based on these fundamental results we construct a new short rate model with jumps, which extends the CIR model and still gives closed form expressions for bond options. Manusript received: June 2000, final version received: October 2000     

The term structure of interbank risk

We infer a term structure of interbank risk from spreads between rates on interest rate swaps indexed to the London Interbank Offered Rate (LIBOR) and overnight indexed swaps. We develop a tractable model of interbank risk to decompose the term structure into default and non-default (liquidity) components. From August 2007 to January 2011, the fraction of total interbank risk due to default risk, on average, increases with maturity. At short maturities, the non-default component is important in the first half of the sample period and is correlated with measures of funding and market liquidity. The model also provides a framework for pricing, hedging, and risk management of interest rate swaps in the presence of significant basis risk.     

THE CANONICAL MODEL SPACE FOR LAW‐INVARIANT CONVEX RISK MEASURES IS L1

In this paper, we establish a one‐to‐one correspondence between law‐invariant convex risk measures on L^∞ and L¹. This proves that the canonical model space for the predominant class of law‐invariant convex risk measures is L¹.