Simulation and estimation of point process models in finance
Abstract/Contents
- Abstract
- Stochastic models of event timing are popular in many applications because of their ability to capture random event arrivals and their impacts. For the particular settings of point processes with stochastic intensities, this dissertation develops a simulation and estimation methodologies. Specifically, this dissertation constructs efficient importance sampling estimators of certain rare-event probabilities involving affine point processes under a large pool regime. The proposed computational approach is based on dynamic importance sampling, and the design of the estimators extends past literature to accommodate the point process settings. In particular, the state-dependent change of measure is performed not at event arrivals but over a deterministic time grid. Several common criteria for optimality of the estimators under limited computational resources are analyzed. Numerical results illustrate the advantages of the proposed estimators. Additionally, this dissertation establishes accurate estimators of bond illiquidity from bond transaction records. The measure of bond illiquidity is defined as the sum of a round-trip transaction cost, i.e., a bid-ask spread, and a carrying cost. The carrying cost is the compound cost monetizing the waiting time for counterparties while locked in or holding a bond. Transaction times and their bid-ask spreads are modeled by arrivals and marks of a marked point process so that bond illiquidity is estimated by an effective bid-ask spread and a certain function of its intensity. Numerical results illustrate the advantages of the proposed measure and volume-related trends.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Ra, Moojoong |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. |
| Primary advisor | Giesecke, Kay |
| Thesis advisor | Giesecke, Kay |
| Thesis advisor | Glynn, Peter W |
| Thesis advisor | Haas, Peter |
| Advisor | Glynn, Peter W |
| Advisor | Haas, Peter |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Moojoong Ra. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Moojoong Ra
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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