On the discounted penalty function in a Markov-dependent risk model

By Hansjörg Albrecher *, Onno J. Boxma. 2005

126 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/cbb5b236c8ad17e43c39c16ce9c63ed88ec516bf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shuanming Li
2. Jürgen Hartinger
3. Yi Lu
4. Hansjörg Albrecher *
5. Andrei L. Badescu
6. Alexandru V. Asimit

In Management Science and Operations Research

1. Shuanming Li
2. Yi Lu
3. Andrei L. Badescu
4. Alexandru V. Asimit
5. Jürgen Hartinger
6. Hansjörg Albrecher *
7. Onno J. Boxma
8. Qihe Tang

In Mathematical Physics

1. Shuanming Li
2. Jürgen Hartinger
3. Yi Lu
4. Andrei L. Badescu
5. Alexandru V. Asimit
6. Hansjörg Albrecher *
7. Ronnie Loeffen

Citations in the corpus

• Extremes on the discounted aggregate claims in a time dependent risk model (2010, background): “A certain semi-Markov dependence structure is considered by Albrecher & Boxma ( 2004 , 2005 ).”
• The Decompositions of the Discounted Penalty Functions and Dividends-Penalty Identity in a Markov-Modulated Risk Model (2008, methodology): “Albrecher and Boxma (2005) study the expected discounted penalty function in a semi-Markovian dependent risk model in which at each instant of a claim, the underlying Markov chain jumps to a new state and the distribution of claim depends on this state.”
• A Risk Model with Multilayer Dividend Strategy (2007, methodology): “36)) (for the renewal model see Dickson and Drekic (2004), and for a more general semi-Markovian framework, see Albrecher and Boxma (2005)).”
• A Markovian growth-collapse model (2006, background): “In [2] a more general, semi-Markovian dependence structure is introduced in the risk model.”