Markovian Projection Method for Volatility Calibration

By Vladimir V. Piterbarg. 2006

75 citations recorded by the source corpus.

Subfields: Demography, Finance

View Paper: https://www.semanticscholar.org/paper/e798df461acd2720fa1b7f6c9189b329657b02c0

How researchers see this paper

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

In Demography

1. S. Violante
2. Antoine Jacquier
3. Jean-Dominique Deuschel
4. Peter K. Friz
5. A. Lopatin
6. T. Misirpashaev
7. Ismaël F. Bailleul

In Finance

1. Antoine Jacquier
2. Peter K. Friz
3. S. Violante
4. Jean-Dominique Deuschel
5. A. Lopatin
6. T. Misirpashaev
7. Nicolas Victoir
8. T. Cass

Citations in the corpus

• Marginal Density Expansions for Diffusions and Stochastic Volatility I: Theoretical Foundations (2011, methodology): “As a final note, we recall that the (in general, non-Markovian) R-valued Itô-process (Yt : t ≥ 0) admits - subject to some technical assumptions [28, 45] - a Markovian (or Gyöngy) projection.”
• Two-Dimensional Markovian Model for Dynamics of Aggregate Credit Loss (2008, methodology): “Piterbarg (2006) gave an intuitive proof and numerous applications of this result to various problems of mathematical finance.”