1 |
Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities, The Journal of Political Economy, 637-654.
|
2 |
Duffie, D. and Pan, J. (2001). Analytical value-at-risk with jumps and credit risk, Finance and Stochastics, 5, 155-180.
DOI
|
3 |
Frame, S. J. and Ramezani, C. A. (2014). Bayesian estimation of asymmetric jump-diffusion processes, Annals of Financial Economics, 9, 1450008.
DOI
|
4 |
Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences, Statistical Science, 467-472.
|
5 |
Heston, S. L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options, Review of Financial Studies, 6, 327-343
DOI
|
6 |
Huang, Z. and Kou, S. G. (2006). First passage times and analytical solutions for options on two assets with jump risk, Columbia University.
|
7 |
Hull, J. and White, A. (1987). The pricing of options on assets with stochastic volatilities, The Journal of Finance, 42, 281-300
DOI
|
8 |
Jacquier, E., Johannes, M., and Polson, N. (2007). MCMC maximum likelihood for latent state models, Journal of Econometrics, 137, 615-640.
DOI
|
9 |
Johannes, M. and Polson, N. (2002). MCMC methods for financial econometrics, The Handbook of Financial Econometrics, 65.
|
10 |
Kotz, S., Kozubowski, T., and Podgorski, K. (2012). The Laplace Distribution and Generalizations: A Revisit with Applications to Communications, Economics, Engineering, and Finance, Springer Science & Business Media, New York.
|
11 |
Kou, S. G. (2002). A jump-diffusion model for option pricing, Management Science, 48, 1086-1101.
DOI
|
12 |
Kou, S. G. (2007). Jump-diffusion models for asset pricing in financial engineering, Handbooks in Operations Research and Management Science, 15, 73-116
|
13 |
Liu, J. S. (1994). The collapsed Gibbs sampler in Bayesian computations with applications to gene regulation problem, Journal of the American Statistical Association, 89, 958-966.
DOI
|
14 |
Liu, J. S., Wong, W. H., and Kong, A. (1994). Covariance structure of the Gibbs sampler with applications to comparisons of estimators and augmentation schemes, Biometricka, 81, 27-40.
DOI
|
15 |
Liu, J. S. and Wu, Y. N. (1999). Parameter expansion for data augmentation, Journal of the American Statistical Association, 94, 1264-1274.
DOI
|
16 |
Meng, X.-L. and van Dyk, D. A. (1999). Seeking efficient data augmentation schemes via conditional and marginal augmentation, Biometrika, 86, 301-320.
DOI
|
17 |
Park, T. and van Dyk, D. A. (2009). Partially collapsed Gibbs samplers: Illustrations and applications, Journal of Computational and Graphical Statistics, 18, 283-305.
DOI
|
18 |
Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous, Journal of Financial Economics, 3, 125-144.
DOI
|
19 |
Park, T. and Lee, Y. (2014). Efficient Bayesian inference on asymmetric jump-diffusion models, Korean Journal of Applied Statistics, 27, 959-973
DOI
|
20 |
Park, T. and Min, S. (2016). Partially collapsed Gibbs sampling for linear mixed-effects models, Communi- cations in Statistics - Simulation and Computation, 45, 165-180
DOI
|
21 |
Ramezani, C. A. and Zeng, Y. (1998). Maximum likelihood estimation of asymmetric jump-diffusion process: Application to security prices, Working Paper, Department of Mathematics and Statistics, University of Missouri, Kansas City, Available from: http://ssrn.com/abstract=606361.
|
22 |
Ramezani, C. A. and Zeng, Y. (2007). Maximum likelihood estimation of the double exponential jump diffusion process, Annals of Finance, 3, 487-507.
DOI
|
23 |
Tanner, M. A. and Wong, W. H. (1987). The calculation of posterior distributions by data augmentation, Journal of the American Statistical Association, 82, 528-540.
DOI
|
24 |
van Dyk, D. A. and Park, T. (2008). Partially collapsed Gibbs samplers: theory and methods, Journal of the American Statistical Association, 193, 790-796.
|
25 |
van Dyk, D. A. (2000). Nesting EM algorithms for computational efficiency, Statistical Sinica, 10, 203-225.
|
26 |
van Dyk, D. A. and Park, T. (2011). Partially collapsed Gibbs sampling and path-adaptive Metropolis-Hastings in high-energy astrophysics, Handbook of Markov Chain Monte Carlo (383-400), Chapman & Hall/CRCPress, New York.
|