scholarly journals Reward-Risk Momentum Strategies Using Classical Tempered Stable Distribution

2014 ◽  
Author(s):  
Jaehyung Choi ◽  
Aaron Kim ◽  
Ivan Mitov
Keyword(s):  
Stable Distribution ◽  
Momentum Strategies ◽  
Tempered Stable Distribution

Markov regime-switching autoregressive model with tempered stable distribution: simulation evidence

2019 ◽  
Vol 24 (1) ◽  
Author(s):  
Lingbing Feng ◽  
Yanlin Shi
Keyword(s):  
Regime Switching ◽  
Autoregressive Model ◽  
Stable Distribution ◽  
Financial Data ◽  
Markov Regime Switching ◽  
Student’S T Distribution ◽  
Tempered Stable Distribution ◽  
Student’S T ◽  
Student's T Distribution ◽  
T Distribution

Abstract Markov regime-switching (MRS) autoregressive model is a widely used approach to model the economic and financial data with potential structural breaks. The innovation series of such MRS-type models are usually assumed to follow a Normal distribution, which cannot accommodate fat-tailed properties commonly present in empirical data. Many theoretical studies suggest that this issue can lead to inconsistent estimates. In this paper, we consider the tempered stable distribution, which has the attractive stability under aggregation property missed in other popular alternatives like Student’s t-distribution and General Error Distribution (GED). Through systematically designed simulation studies with the MRS autoregressive models, our results demonstrate that the model with tempered stable distribution uniformly outperforms those with Student’s t-distribution and GED. Our empirical study on the implied volatility of the S&P 500 options (VIX) also leads to the same conclusions. Therefore, we argue that the tempered stable distribution could be widely used for modelling economic and financial data in general contexts with an MRS-type specification.

scholarly journals On the Transition Law of Tempered Stable Ornstein–Uhlenbeck Processes

2009 ◽  
Vol 46 (3) ◽  
pp. 721-731 ◽  
Author(s):  
Shibin Zhang ◽  
Xinsheng Zhang
Keyword(s):  
Stable Distribution ◽  
Stochastic Integral ◽  
Random Variables ◽  
Transition Density ◽  
Random Variable ◽  
Independent Random Variables ◽  
Poisson Random Variable ◽  
Compound Poisson ◽  
Tempered Stable Distribution ◽  
Transition Distribution

In this paper, a stochastic integral of Ornstein–Uhlenbeck type is represented to be the sum of two independent random variables: one has a tempered stable distribution and the other has a compound Poisson distribution. In distribution, the compound Poisson random variable is equal to the sum of a Poisson-distributed number of positive random variables, which are independent and identically distributed and have a common specified density function. Based on the representation of the stochastic integral, we prove that the transition distribution of the tempered stable Ornstein–Uhlenbeck process is self-decomposable and that the transition density is a C∞-function.

Modeling overdispersion with the normalized tempered stable distribution

2011 ◽  
Vol 55 (7) ◽  
pp. 2288-2301 ◽  
Author(s):  
M. Kolossiatis ◽  
J.E. Griffin ◽  
M.F.J. Steel
Keyword(s):  
Stable Distribution ◽  
Tempered Stable Distribution

A New Tempered Stable Distribution and Its Application to Finance

2008 ◽  
pp. 77-109 ◽  
Author(s):  
Young Shin Kim ◽  
Svetlozar T. Rachev ◽  
Michele Leonardo Bianchi ◽  
Frank J. Fabozzi
Keyword(s):  
Stable Distribution ◽  
Tempered Stable Distribution

scholarly journals A Modified Tempered Stable Distribution with Volatility Clustering

2015 ◽  
pp. 344-365 ◽  
Author(s):  
Young Shin Kim ◽  
Svetlozar T. Rachev ◽  
Dong Myung Chung ◽  
Michele Leonardo Bianchi
Keyword(s):  
Stable Distribution ◽  
Volatility Clustering ◽  
Tempered Stable Distribution

scholarly journals On the Transition Law of Tempered Stable Ornstein–Uhlenbeck Processes

2009 ◽  
Vol 46 (03) ◽  
pp. 721-731 ◽  
Author(s):  
Shibin Zhang ◽  
Xinsheng Zhang
Keyword(s):  
Stable Distribution ◽  
Stochastic Integral ◽  
Random Variables ◽  
Transition Density ◽  
Random Variable ◽  
Independent Random Variables ◽  
Poisson Random Variable ◽  
Compound Poisson ◽  
Tempered Stable Distribution ◽  
Transition Distribution

In this paper, a stochastic integral of Ornstein–Uhlenbeck type is represented to be the sum of two independent random variables: one has a tempered stable distribution and the other has a compound Poisson distribution. In distribution, the compound Poisson random variable is equal to the sum of a Poisson-distributed number of positive random variables, which are independent and identically distributed and have a common specified density function. Based on the representation of the stochastic integral, we prove that the transition distribution of the tempered stable Ornstein–Uhlenbeck process is self-decomposable and that the transition density is aC∞-function.

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