Shape-constrained semiparametric additive stochastic volatility models

References

• Andrieu, C., Doucet, A., & Holenstein, R. (2010). Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72, 269–342. doi: 10.1111/j.1467-9868.2009.00736.x
   Web of Science ®Google Scholar
• Bauwens, L., & Veredas, D. (2004). The stochastic conditional duration model: A latent variable model for the analysis of financial durations. Journal of Econometrics, 119, 381–412. doi: 10.1016/S0304-4076(03)00201-X
   Web of Science ®Google Scholar
• Brezger, A., & Steiner, W. J. (2008). Monotonic regression based on Bayesian P–splines. Journal of Business & Economic Statistics, 26, 90–104. doi: 10.1198/073500107000000223
   Web of Science ®Google Scholar
• Cai, B., & Dunson, D. (2007). Bayesian multivariate isotonic regression splines. Journal of the American Statistical Association, 102, 1158–1171. doi: 10.1198/016214506000000942
   Web of Science ®Google Scholar
• Chib, S., & Greenberg, E. (1994). Bayes inference in regression models with ARMA(p,q) errors. Journal of Econometrics, 64, 183–206. doi: 10.1016/0304-4076(94)90063-9
   Web of Science ®Google Scholar
• Chib, S., & Greenberg, E. (1995). Understanding the Metropolis-Hastings algorithm. The American Statistician, 49, 327–335.
   Web of Science ®Google Scholar
• Comte, F. (2004). Kernel deconvolution of stochastic volatility models. Journal of Time Series Analysis, 25, 563–582. doi: 10.1111/j.1467-9892.2004.01825.x
   Web of Science ®Google Scholar
• Eddelbuettel, D., & Sanderson, C. (2014). RcppArmadillo: Accelerating R with high-performance C++ linear algebra. Computational Statistics & Data Analysis, 71, 1054–1063. doi: 10.1016/j.csda.2013.02.005
   Web of Science ®Google Scholar
• Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007. doi: 10.2307/1912773
   Web of Science ®Google Scholar
• Engle, R., & Gonzalez-Rivera, G. (1991). Semiparametric ARCH models. Journal of Business & Economic Statistics, 9, 345–359.
   Web of Science ®Google Scholar
• Engle, R., & Ng, V. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48, 1749–1778. doi: 10.1111/j.1540-6261.1993.tb05127.x
   Web of Science ®Google Scholar
• Godsill, S. J., Doucet, A., & West, M. (2004). Monte Carlo smoothing for nonlinear time series. Journal of the American Statistical Association, 99, 156–168. doi: 10.1198/016214504000000151
   Web of Science ®Google Scholar
• Gordon, N. J., Salmond, D. J., & Smith, A. F. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proceedings F (Radar and Signal Processing), 140, 107–113. doi: 10.1049/ip-f-2.1993.0015
   Web of Science ®Google Scholar
• Grenander, U., & Miller, M. I. (1994). Representations of knowledge in complex systems. Journal of the Royal Statistical Society. Series B (Methodological), 56, 549–603. doi: 10.1111/j.2517-6161.1994.tb02000.x
   Web of Science ®Google Scholar
• Harvey, A. C., & Shephard, N. (1996). Estimation of an asymmetric stochastic volatility model for asset returns. Journal of Business & Economic Statistics, 14, 429–434.
   Web of Science ®Google Scholar
• Hull, J., & White, A. (1987). The pricing of options on assets with stochastic volatilities. The Journal of Finance, 42, 281–300. doi: 10.1111/j.1540-6261.1987.tb02568.x
   Web of Science ®Google Scholar
• Kim, S., Shephard, N., & Chib, S. (1998). Stochastic volatility: Likelihood inference and comparison with ARCH models. The Review of Economic Studies, 65, 361–393. doi: 10.1111/1467-937X.00050
   Web of Science ®Google Scholar
• Liu, J. S., & Chen, R. (1998). Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association, 93, 1032–1044. doi: 10.1080/01621459.1998.10473765
   Web of Science ®Google Scholar
• Meyer, M. C., Hackstadt, A. J., & Hoeting, J. A. (2011). Bayesian estimation and inference for generalised partial linear models using shape-restricted splines. Journal of Nonparametric Statistics, 23, 867–884. doi: 10.1080/10485252.2011.597852
   Web of Science ®Google Scholar
• Nakajima, J., & Omori, Y. (2009). Leverage, heavy-tails and correlated jumps in stochastic volatility models. Computational Statistics & Data Analysis, 53, 2335–2353. doi: 10.1016/j.csda.2008.03.015
   Web of Science ®Google Scholar
• Neelon, B., & Dunson, D. (2004). Bayesian isotonic regression and trend analysis. Biometrics, 60, 398–406. doi: 10.1111/j.0006-341X.2004.00184.x
   PubMed Web of Science ®Google Scholar
• Nelson, D. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347–370. doi: 10.2307/2938260
   Web of Science ®Google Scholar
• Omori, Y., Chib, S., Shephard, N., & Nakajima, J. (2007). Stochastic volatility with leverage: Fast and efficient likelihood inference. Journal of Econometrics, 140, 425–449. doi: 10.1016/j.jeconom.2006.07.008
   Web of Science ®Google Scholar
• R Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
   Google Scholar
• Ramsay, J. (1988). Monotone regression splines in action. Statistical Science, 3, 425–441. doi: 10.1214/ss/1177012761
   Google Scholar
• Roberts, G. O., & Tweedie, R. L. (1996). Exponential convergence of Langevin distributions and their discrete approximations. Bernoulli, 2, 341–363. doi: 10.2307/3318418
   Google Scholar
• Rydberg, T. (2000). Realistic statistical modelling of financial data. International Statistical Review, 68, 233–258. doi: 10.1111/j.1751-5823.2000.tb00329.x
   Web of Science ®Google Scholar
• Taylor, S. (1994). Modeling stochastic volatility: A review and comparative study. Mathematical Finance, 4, 183–204. doi: 10.1111/j.1467-9965.1994.tb00057.x
   Google Scholar
• Yu, J. (2005). On leverage in a stochastic volatility model. Journal of Econometrics, 127, 165–178. doi: 10.1016/j.jeconom.2004.08.002
   Web of Science ®Google Scholar
• Yu, J. (2012). A semiparametric stochastic volatility model. Journal of Econometrics, 167, 473–482. doi: 10.1016/j.jeconom.2011.09.029
   Web of Science ®Google Scholar