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Second-order expansions for maxima of dynamic bivariate normal copulas

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  • Wang, Rui
  • Liao, Xin
  • Peng, Zuoxiang

Abstract

In this paper, we establish the second-order distributional expansions of normalized maxima of n independent observations, where the ith observation follows from a normal copula with its correlation coefficient being a monotone continuous function. These expansions can be used to deduce the convergence rates of distributions of normalized maxima to their limits.

Suggested Citation

  • Wang, Rui & Liao, Xin & Peng, Zuoxiang, 2017. "Second-order expansions for maxima of dynamic bivariate normal copulas," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 275-283.
    • Handle: RePEc:eee:stapro:v:129:y:2017:i:c:p:275-283
    DOI: 10.1016/j.spl.2017.06.011
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    References listed on IDEAS

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    1. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    2. Frick, Melanie & Reiss, Rolf-Dieter, 2013. "Expansions and penultimate distributions of maxima of bivariate normal random vectors," Statistics & Probability Letters, Elsevier, vol. 83(11), pages 2563-2568.
    3. Wang, Chou-Wen & Yang, Sharon S. & Huang, Hong-Chih, 2015. "Modeling multi-country mortality dependence and its application in pricing survivor index swaps—A dynamic copula approach," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 30-39.
    4. Xiaojun Chu, 2015. "Modelling impact of monetary policy on stock market liquidity: a dynamic copula approach," Applied Economics Letters, Taylor & Francis Journals, vol. 22(10), pages 820-824, July.
    5. Hüsler, Jürg & Reiss, Rolf-Dieter, 1989. "Maxima of normal random vectors: Between independence and complete dependence," Statistics & Probability Letters, Elsevier, vol. 7(4), pages 283-286, February.
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