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Convolution-t Distributions (2404.00864v1)

Published 1 Apr 2024 in econ.EM, math.ST, and stat.TH

Abstract: We introduce a new class of multivariate heavy-tailed distributions that are convolutions of heterogeneous multivariate t-distributions. Unlike commonly used heavy-tailed distributions, the multivariate convolution-t distributions embody cluster structures with flexible nonlinear dependencies and heterogeneous marginal distributions. Importantly, convolution-t distributions have simple density functions that facilitate estimation and likelihood-based inference. The characteristic features of convolution-t distributions are found to be important in an empirical analysis of realized volatility measures and help identify their underlying factor structure.

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References (64)
  1. Amemiya, T. (1985). Advanced Econometrics. Harvard University Press, Cambridge, MA.
  2. Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility. The Review of Economics and Statistics, 89(4):701–720.
  3. A canonical representation of block matrices with applications to covariance and correlation matrices. Forthcoming in Review of Economics and Statistics.
  4. Spanning and derivative-security valuation. Journal of Financial Economics, 55:205–238.
  5. Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise. Econometrica, 76:1481–536.
  6. Realized kernels in practice: trades and quotes. Econometrics Journal, 12:C1–C32.
  7. Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. Jounal of Econometrics, 162:149–169.
  8. On the density of the sum of two independent Student t-random vectors. Statistics & Probability Letters, 80:1043–1055.
  9. Properties of multivariate cauchy and poly-cauchy distributions with bayesian g-prior applications. Technical report, University of Minnesota.
  10. Bingham, N. H. (1996). A conversation with David Kendall. Statistical Science, 11:159–188.
  11. Bohman, H. (1970). A method to calculate the distribution function when the characteristic function is known. BIT Numerical Mathematics, 10:237–242.
  12. Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics, 192(1):1–18.
  13. Chapman, D. G. (1950). Some two sample tests. The Annals of Mathematical Statistics, pages 601–606.
  14. Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7:174–196.
  15. Realizing smiles: Options pricing with realized volatility. Journal of Financial Economics, 107:284–304.
  16. A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations. Journal of Business and Economic Statistics, 29:552–563.
  17. High dimensional dynamic stochastic copula models. Journal of Econometrics, 189:335–345.
  18. Davies, R. B. (1973). Numerical inversion of a characteristic function. Biometrika, 60:415–417.
  19. Dayal, H. H. (1976). Representations of the Behrens-Fisher density. Sankhyā: The Indian Journal of Statistics, Series B, pages 95–99.
  20. The t copula and related copulas. International Statistical Review, 73:111–129.
  21. Multivariate scale mixture of gaussians modeling. In International Conference on Independent Component Analysis and Signal Separation, pages 799–806.
  22. Modelling Extremal Events for Insurance and Finance. Springer.
  23. Exact convolution of t𝑡titalic_t distributions, with applications to Bayesian inference for a normal mean with t𝑡titalic_t prior distributions. Journal of Statistical Computation and Simulation, 36:209–228.
  24. The meta-elliptical distributions with given marginals. Journal of multivariate analysis, 82:1–16.
  25. Robust graphical modeling of gene networks using classical and alternative t-distributions. The Annals of Applied Statistics, 5:1057–1080.
  26. New tables of Behrens’ test of significance. Journal of the Royal Statistical Society Series B: Statistical Methodology, 18:212–216.
  27. A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: application to robust clustering. Statistics and computing, 24:971–984.
  28. Forchini, G. (2008). The distribution of the sum of a normal and a t random variable with arbitrary degrees of freedom. Metron-International Journal of Statistics, 66:205–208.
  29. Gaunt, R. E. (2021). A simple proof of the characteristic function of Student’s t-ddistribution. Communications in Statistics – Theory and Methods, 50:3380–3383.
  30. Ghosh, B. K. (1975). On the distribution of the difference of two t-variables. Journal of the American Statistical Association, 70:463–467.
  31. Gil-Pelaez, J. (1951). Note on the inversion theorem. Biometrika, 38:481–482.
  32. Table of integrals, series, and products. Academic press.
  33. Gurland, J. (1948). Inversion formulae for the distribution of ratios. The Annals of Mathematical Statistics, pages 228–237.
  34. Harvey, A. (2013). Dynamic Models for Volatility and Heavy Tails: with Applications to Financial and Economic Time Series. Cambridge University Press.
  35. Heckman, J. (1979). Sample selection bias as a specification error. Econometrica, 47:153–161.
  36. 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.
  37. A closed-form GARCH option valuation model. Review of Financial Studies, 13:585–625.
  38. Hurst, S. (1995). The characteristic function of the Student t distribution. Financial Mathematics Research Report No. FMRR006-95, Statistics Research Report No. SRR044-95.
  39. Heavy-Tailed Distributions and Robustness in Economics and Finance. Springer.
  40. Imhof, J.-P. (1961). Computing the distribution of quadratic forms in normal variables. Biometrika, 48:419–426.
  41. Joarder, A. H. (1995). The characteristic function of the univariate T-distribution. Dhaka University Journal of Science, 43:117–25.
  42. Kawata, T. (1972). Fourier analysis in probability theory. Academic Press.
  43. Kendall, D. G. (1938). The effect of radiation damping and doppler broadening on the atomic absorption coefficient. Zeitschrift für Astrophysik, 16:308.
  44. Mitchell, A. E. (1989). The information matrix, skewness tensor and a-connections for the general multivariate elliptic distribution. Annals of the Institute of Statistical Mathematics, 41:289–304.
  45. Convolutions of the T distribution. Computers & Mathematics with Applications, 49:715–721.
  46. Nason, G. P. (2006). On the sum of t and Gaussian random variables. Statistics & Probability Letters, 76:1280–1286.
  47. Large sample estimation and hypothesis testing. In Engle, R. and McFadden, D., editors, Handbook of Econometrics, volume IV, chapter 36, pages 2111–2245. Elsevier, Amsterdam, The Netherlands.
  48. Modeling dependence in high dimensions with factor copulas. Journal of Business and Economic Statistics, 35:139–154.
  49. Time-varying systemic risk: Evidence from a dynamic copula model of cds spreads. Journal of Business and Economic Statistics, 36:181–195.
  50. Dynamic factor copula models with estimated cluster assignments. Journal of Econometrics, 237. 105374.
  51. Closed-form multi-factor copula models with observation-driven dynamic factor loadings. Journal of Business and Economic Statistics, 39:1066–1079.
  52. Patil, V. H. (1965). Approximation to the Behrens-Fisher distributions. Biometrika, 52:267–271.
  53. Patton, A. (2012). A review of copula models for economic time series. Journal of Multivariate Analysis, 110:4–18.
  54. Patton, A. (2014). Copulas in econometrics. Annual Review of Economics, 6:179–200.
  55. Integrals and series: direct Laplace transforms, volume 4. CRC Press.
  56. Explicit form of the distribution of the Behrens-Fisher d-statistic. Journal of the Royal Statistical Society: Series B (Methodological), 36:54–60.
  57. Ruben, H. (1960). On the distribution of the weighted difference of two independent Student variables. Journal of the Royal Statistical Society Series B: Statistical Methodology, 22:188–194.
  58. Linear regression analysis, volume 330. John Wiley & Sons.
  59. Shephard, N. (1991a). From characteristic function to distribution function: A simple framework for the theory. Econometric Theory, 7:519–529.
  60. Shephard, N. (1991b). Numerical integration rules for multivariate inversions. Journal of Statistical Computation and Simulation, 39:37–46.
  61. Stone, J. V. (2004). Independent component analysis: a tutorial introduction. MIT press, Cambridge, Massachusetts, London, England.
  62. The distribution of linear combinations of t-variables. Journal of the American Statistical Association, 73:876–878.
  63. Obtaining distribution functions by numerical inversion of characteristic functions with applications. American Statistician, pages 346–350.
  64. A generalized asymmetric student-t distribution with application to financial econometrics. Journal of Econometrics, 157:297–305.

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