Abstract
In this study, we investigate the performance of different parametric models with stable and tempered stable distributions for capturing the tail behaviour of log-returns (financial asset returns). First, we define and discuss the properties of stable and tempered stable random variables. We then show how to estimate their parameters and simulate them based on their characteristic functions. Finally, as an illustration, we conduct an empirical analysis to explore the performance of different models representing the distributions of log-returns for the S&P500 and DAX indexes.
| Original language | English |
|---|---|
| Journal | Annals of Operations Research |
| DOIs | |
| Publication status | Accepted/In press - 1 Jan 2019 |
Keywords
- Lévy process
- Stable distribution
- Tail risk
- Tempered stable distribution
Cite this
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Modelling tail risk with tempered stable distributions : an overview. / Fallahgoul, Hasan; Loeper, Gregoire.
In: Annals of Operations Research, 01.01.2019.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Modelling tail risk with tempered stable distributions
T2 - an overview
AU - Fallahgoul, Hasan
AU - Loeper, Gregoire
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In this study, we investigate the performance of different parametric models with stable and tempered stable distributions for capturing the tail behaviour of log-returns (financial asset returns). First, we define and discuss the properties of stable and tempered stable random variables. We then show how to estimate their parameters and simulate them based on their characteristic functions. Finally, as an illustration, we conduct an empirical analysis to explore the performance of different models representing the distributions of log-returns for the S&P500 and DAX indexes.
AB - In this study, we investigate the performance of different parametric models with stable and tempered stable distributions for capturing the tail behaviour of log-returns (financial asset returns). First, we define and discuss the properties of stable and tempered stable random variables. We then show how to estimate their parameters and simulate them based on their characteristic functions. Finally, as an illustration, we conduct an empirical analysis to explore the performance of different models representing the distributions of log-returns for the S&P500 and DAX indexes.
KW - Lévy process
KW - Stable distribution
KW - Tail risk
KW - Tempered stable distribution
UR - http://www.scopus.com/inward/record.url?scp=85064250017&partnerID=8YFLogxK
U2 - 10.1007/s10479-019-03204-3
DO - 10.1007/s10479-019-03204-3
M3 - Article
JO - Annals of Operations Research
JF - Annals of Operations Research
SN - 0254-5330
ER -