Publication

Generalizing the Affine Framework to HJM and Random Field Models

2003
Working Papers

Abstract

We identify a class of term structure models possessing a generalized affine-structure that significantly extends the class studied by Duffie, Pan, and Singleton (2000) and Chacko and Das (2002). This class of models, which includes both infinite-state-variable (i.e., HJM-type) and infinite-factor (random field) models, possesses analytic solutions for the characteristic function, which in turn provides closed-form solutions for many types of fixed income derivatives. In addition, the generalized affine framework provides analytic solutions to the optimal portfolio choice problem. In a random field setting, the optimal portfolio decision is unique, in turn providing a justification for 'preferred habitat' theories.

Official source

https://infoscience.epfl.ch/entities/publication/55926eb7-f3e1-42ce-b9a6-c8435d4b9846

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2003
Working Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/55926eb7-f3e1-42ce-b9a6-c8435d4b9846

About this result

Related concepts (32)

Markov random field

In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a Markov random field if it satisfies Markov properties. The concept originates from the Sherrington–Kirkpatrick model. A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic.

Intertemporal portfolio choice

Intertemporal portfolio choice is the process of allocating one's investable wealth to various assets, especially financial assets, repeatedly over time, in such a way as to optimize some criterion. The set of asset proportions at any time defines a portfolio. Since the returns on almost all assets are not fully predictable, the criterion has to take financial risk into account. Typically the criterion is the expected value of some concave function of the value of the portfolio after a certain number of time periods—that is, the expected utility of final wealth.

Modern portfolio theory

Modern portfolio theory (MPT), or mean-variance analysis, is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk. It is a formalization and extension of diversification in investing, the idea that owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return.

Related publications (3)

Generalizing the Affine Framework to HJM and Random Field Models

Chat with Graph Search

Systematically heterogeneous covariance in network GEV models

Variance Covariance Orders and Median Preserving Spreads

Generalized Bessel Functions in Incommensurate Structure Analysis