본문 바로가기
KERD
논문KCI 등재

A Review of Copula Methods for Measuring Uncertainty in Finance and Economics

2

조회

0

다운로드

1

인용

0

참고문헌

키워드

초록

This paper reviews copula methods used for economic and finance. Copula allows researchers to relax the traditional linear model assumptions so that researchers can specify the marginal distributions and look at the dependence structure linking the marginal distributions to form a joint distribution. In this review, we focus on the copula dynamic correlation coefficient, copula directional dependence and copula applications in economics and finance.

Abstract

This paper reviews copula methods used for economic and finance. Copula allows researchers to relax the traditional linear model assumptions so that researchers can specify the marginal distributions and look at the dependence structure linking the marginal distributions to form a joint distribution. In this review, we focus on the copula dynamic correlation coefficient, copula directional dependence and copula applications in economics and finance.

참고문헌 (69)

  1. Kim JM, "On the relationship of cryptocurrency price with US stock and gold price using copula models", Mathematics, 8, pp.1859~ (2020)
  2. Cherubini U, "Dynamic copula methods in finance" (2011), Wiley
  3. Haseb T, "Copula-based maximum-likelihood estimation of sample-selection models", Stata J, 13, pp.547~573 (2013)
  4. Sklar A, "Fonctions de repartition à n dimensions et leurs marges", Publ Inst Stat Univ Paris, 8, pp.229~231 (1959)
  5. Masarotto G, "Gaussian copula marginal regression", Electron J Stat, 6, pp.1517~1549 (2012)
  6. Guolo A, "Beta regression for time series analysis of bounded data, with application to Canada Google flu trends", Ann Appl Stat, 8, pp.74~88 (2014)
  7. Kim JM, "Directional dependence via Gaussian copula beta regression model with asymmetric GARCH marginals", Commun Stat-Simul C, 46, pp.7639~7653 (2017)
  8. Kim JM, "The copula directional dependence by stochastic volatility models", Commun Stat-Simul C, 48, pp.1153~1175 (2019)
  9. Hyun S, "What coins lead in the cryptocurrency market? using copula and neural networks model", J Risk Financ Manag, 12, pp.132~ (2019)
  10. Kim JM, "Sustainable causal interpretation with board characteristics : Caveat emptor", Sustainability, 12, pp.3429~ (2020)
  11. Kim JM, "Modeling non-normal corporate bond yield spreads by copula", North Am J Econ Financ, 53, pp.101210~ (2020)
  12. Engle RF, "Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation", Econometrica, 50, pp.987~1007 (1982)
  13. Bollerslev T, "Generalized autoregressive conditional heteroskedasticity", J Econometrics, 31, pp.307~328 (1986)
  14. Engle RF, "Dynamic conditional correlation-A simple class of multivariate GARCH models", J Bus Econ Stat, 20, pp.339~350 (2002)
  15. Patton AJ, "Applications of copula theory in financial econometrics", Ph.D., pp.2002
  16. Engle R, "The econometrics of ultra-high frequency data", Econometrica, 68, pp.1~22 (2000)
  17. Alexander C., "Market models: A guide to financial data analysis" (2001), John Wiley & Sons
  18. Aïft-Sahalia Y, "High-frequency financial econometrics" (2014), Princeton University Press
  19. Hörmann S, "functional version of the ARCH model", Econ Theory, 29, pp.267~288 (2013)
  20. Aue A, "Functional generalized autoregressive conditional heteroskedasticity", J Time Ser Anal, 38, pp.3~21 (2017)
  21. 윤재은, "Functional ARCH (fARCH) for high-frequency time series: illustration", 응용통계연구, 30(6), pp.983~991 (2017), 한국통계학회
  22. 윤재은, "함수적 변동성 fGARCH(1,1)모형을 통한 초고빈도 시계열 변동성", 응용통계연구, 31(5), pp.667~675 (2018), 한국통계학회
  23. Yoon JE, "FPCA for volatility from highfrequency time series via R-function", Korean J Appl Stat (2020)
  24. Kim JM, "Relationship between oil price and exchange rate by FDA and copula", Appl Econ, 50, pp.2486~2499 (2018)
  25. Kim JM, "Functional ARCH directional dependence via copula for intraday volatility with high-frequency financial time series", Appl Econ (2020)
  26. Alqawbaa M, "Copula directional dependence of discrete time series marginals", Commun Stat-Simul C (2020)
  27. Kim JM, "Patent keyword analysis using time series and copula models", Appl Sci, 9, pp.4071~ (2019)
  28. Nelsen RB, "An introduction to copulas" (2013), Springer
  29. Tse YK, "A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations", J Bus Econ Stat, 20, pp.351~362 (2002)
  30. Kim JM, "Linear time varying regression with a DCC-GARCH model for volatility", Appl Econ, 48, pp.1573~1582 (2016)
  31. Kim JM, "Linear time varying regression with copula DCC-GARCH model for volatility", Econ Lett, 145, pp.262~265 (2016)
  32. Kim JM, "Directional time-varying partial correlation with the Gaussian copula DCC-GARCH model", Appl Econ, 50, pp.4418~4426 (2018)
  33. Lee N, "Dynamic functional connectivity analysis of resting-state FMRI based on copula time-varying correlation", J Neurosci Meth, 323, pp.32~47 (2019)
  34. Lee N, "Dynamic functional connectivity analysis based on time-varying partial correlation with a copula-DCC-GARCH model", Neurosci Res (2020)
  35. "Copula-based dynamic conditional correlation multiplicative error processes" (2012)[URL]
  36. Ghalanos A, "The rmgarch models: Background and properties. (Version 1.2-8)", ~ (2015)
  37. Sungur EA, "A note on directional dependence in regression setting", Commun Stat-Theor M, 34, pp.1957~1965 (2005)
  38. Rodrìguez-Lallena JA, "Úbeda-Flores MA new class of bivariate copulas", Stat Probabil Lett, 66, pp.315~325 (2004)
  39. Kim JM, "Copula method for modeling directional dependence of genes", Bmc Bioinformatics, 9, pp.225~ (2008)
  40. Kim D, "Analysis of directional dependence using asymmetric copula-based regression models", J Stat Comput Sim, 84, pp.1990~2010 (2014)
  41. Liebscher E, "Construction of asymmetric multivariate copulas", J Multivariate Anal, 99, pp.2234~2250 (2008)
  42. Durante F, "Construction of non-exchangeable bivariate distribution functions", Stat Pap, 50, pp.383~391 (2009)
  43. Kojadinovic I, "Modeling multivariate distributions with continuous margins using the copula R Package", J Stat Softw, 34, pp.1~20 (2010)
  44. Taylor SJ, "Modelling financial time series" (1986), Wiley
  45. Kim S, "Stochastic volatility: Likelihood inference and comparison with ARCH models", Rev Econ Stud, 65, pp.361~393 (1998)
  46. Kastner G, "Dealing with stochastic volatility in time series using the R Package stochvol", J Stat Softw, 69, pp.1~30 (2016)
  47. Masarotto G, "Gaussian copula regression in R", J Stat Softw, 77, pp.1~26 (2017)
  48. Patton AJ, "A review of copula models for economic time series", J Multivariate Anal, 110, pp.4~18 (2012)
  49. Aas K., "Pair-copula constructions for financial applications: A review", Econometrics, 4, pp.43~ (2016)
  50. Oh D, "Modelling dependence in high dimensions with factor copulas", J Bus Econ Stat, 35, pp.139~154 (2017)
  51. Bonhomme S, "Assessing the equalizing force of mobility using short panels : France, 1990-2000", Rev Econ Stud, 76, pp.63~92 (2009)
  52. Lee TH, "Copula-based multivariate GARCH model with uncorrelated dependent standardized returns", J Econom, 150, pp.207~218 (2009)
  53. Kim JM, "Dependence structure between oil prices, exchange rates, and interest rates", Energy J, 39, pp.259~280 (2018)
  54. Liu L, "Analysis of tail dependence between sovereign debt distress and bank non-performing loans", Sustainability, 12, pp.747~ (2020)
  55. Kim JM, "Copula nonlinear Granger causality", Econ Model, 88, pp.420~430 (2020)
  56. Joe H, "Multivariate models and dependence concepts" (1997), Chapman & Hall
  57. Bedford T, "Probability density decomposition for conditionally dependent random variables modeled by vines", Ann Math Artif Intel, 32, pp.245~268 (2001)
  58. Kurowicka D, "Uncertainty analysis with high dimensional dependence modelling" (2006), Wiley
  59. Aas K, "Pair-copula constructions of multiple dependence", Insur Math Econ, 44, pp.182~198 (2009)
  60. Berg D, "Models for construction of multivariate dependence", Eur J Financ, 15, pp.639~659 (2009)
  61. Czado C, "Pair-copula constructions of multivariate copulas", Workshop on copula theory and its applications (2010), Springer
  62. Min A, "Bayesian inference for multivariate copulas using pair-copula constructions", J Financ Econ, 8, pp.450~480 (2010)
  63. Smith MS, "Modeling longitudinal data using a pair-copula decomposition of serial dependence", J Am Stat Assoc, 105, pp.1467~1479 (2010)
  64. Czado C, "Maximum likelihood estimation of mixed C-vines with application to exchange rates", Stat Model, 12, pp.229~255 (2012)
  65. Brechmann E, "Truncated regular vines in high dimensions with application to financial data", Can J Stat, 40, pp.68~85 (2012)
  66. Brechmann EC, "Dependence modeling with Cand D-vine copulas : The R-package CDVine", J Stat Softw, 52, pp.1~27 (2013)
  67. Pourkhanali A, "Measuring systemic risk using vine copula", Econ Model, 53, pp.63~74 (2016)
  68. Kim JM, "Quantile-copula approach to dependence between financial assets", North Am J Econ Financ, 51, pp.101066~ (2020)
  69. Kim JM, "The changing dynamics of board independence: A copula based quantile regression approach", J Risk Financ Manag, 13, pp.93~ (2020)

관련 논문AI추천

이 간행물의 다른 논문 (8건)