Score-driven copula models for portfolios of two risky assets

Ayala, Astrid; Blazsek, Szabolcs

doi:10.1080/1351847x.2018.1464488

Cited by 12 publications

(6 citation statements)

References 45 publications

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“…对于动态Copula在其他金融衍生品定价中的应用包括，李平等 [155] 采用具有DCC 时变的二元t Copula模型刻画了四种外汇两两间的动态相关性，在此基础上对于又该四种外汇为标的的抵押外汇契约(Collateralized Foreign Exchange Obligation, CFXO)进行定价。Wu等 [156] 采用动态单因子Copula构架了一篮子抵押贷款信用违约互换(Mortgage Loan Credit Default Swaps，LCDS)的定价模型。Hu等 [157] 将动态因子Copula模型用于人造CDO(Synthetic CDO) 的定价模型中。此外，王珺和梁冯珍 [158] 考虑了变结构的Copula模型来刻画资产之间的违约相关性，并用于债务抵押债券的定价模型。类似地， Li和Li [16] 构建了变结构的Copula模型，并用于包含六种资产在内BDS 的定价。而刘向华和肖雪平 [159] 采用带有马尔科夫机制转换的Copula函数构建了对于BDS的定价模型。Elouerkhaoui [160] 将随机Copula模型应用于CDO等多种信用衍生品的定价模型中。 3.4 投资组合选择如何将财富合理地分配于各种资产以及如何平衡投资组合的风险与收益是市场投资者面临的主要问题。Markowitz [161] 首次用方差来量化投资组合的风险，提出了均值-方差模型，奠定了现代金融学研究的理论基础。均值-方差模型假设资产收益服从多元正态分布。而许多实证表明(参见 [162], [163], [164]) ，金融市场的收益不服从正态分布，且它们的相依结构具有尾部相依性、不对称性和时变性。动态Copula方法可以较好地解决这些问题。Lee [165] 采用机制转换Gumbel-Clayton Copula模型刻画农产品现货和期货收益之间的动态相依结构，并在独立机制转换GARCH过程的假设下，计算了最小方差套期保值比率。Salvatierra和Patton [166] 在GAS模型的基础上，加入高频测度，提出了一类利用高频(日内)数据信息的动态Copula(GRAS)模型。实证表明，与GAS模型相比，GRAS模型提高了美国股票收益率的样本内拟合和样本外密度预测，基于这些密度预测构建的投资组合策略能获得较高的收益。Avdulaj和Barunik [167] 结合高频数据和GAS Copula模型刻画了捷克与德国股票市场之间的动态相关性，并基于Christoffersen 等 [19] 提出的条件分散化收益测度分析了国际多元化带来的好处，研究发现该好处随着时间的变化而变化。采用同样的方法，Avdulaj和Barunik [168]…”

Section: 多元信用衍生品定价unclassified

Dynamic copulas and applications in finance

Li¹,

Li²,

Han³

2021

Sci. Sin.-Math.

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Copula functions can describe the complex dependence structure between variables comprehensively and flexibly, which make them widely used in finance. The dynamic development of financial markets causes the dependence structure between financial variables to change over time, which can be modeled by dynamic copulas by changing the parameters or type of copula function. This paper reviews systematically the main literatures on dynamic copula models and their applications in finance, from the development of dynamic copula models, several commonly-used dynamic copula models, and their main applications in finance. In the conclusion we present the summary and future research directions of dynamic copulas.

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Section: 多元信用衍生品定价unclassified

Dynamic copulas and applications in finance

Li¹,

Li²,

Han³

2021

Sci. Sin.-Math.

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“…Another example of DCS models is QAR (Harvey 2013), which is a nonlinear and outlier-robust alternative to the AR Moving Average (ARMA) model (Box and Jenkins 1970). An additional recent example of DCS models is QVAR (Blazsek et al , 2018b, which is a nonlinear and outlier-robust alternative to the VARMA model (see, for example, Lütkepohl 2005).…”

Section: Review Of the Literature On Dcs Modelsmentioning

confidence: 99%

Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar

Ayala

Blazsek

2018

SERIEs

Self Cite

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In this paper we introduce new Dynamic Conditional Score (DCS) models for the Skew-Gent (Skewed Generalized t) and NIG (Normal-Inverse Gaussian) distributions as alternatives to the recent DCS models for the Student's-t and EGB2 (Exponential Generalized Beta of the second kind) distributions, respectively. The DCS models we propose include stochastic local level, stochastic seasonality, and irregular components with DCS-EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) volatility dynamics. DCS models are robust to extreme observations, whereas standard financial time series models are not. We use data from the Guatemalan Quetzal (GTQ) to United States Dollar (USD) exchange rate for the period of 4th January 1994-30th June 2017. This dataset exhibits significant rises and falls in the GTQ/USD that lead to extreme observations, stochastic seasonality with dynamic amplitude, and volatility dynamics. These seasonality dynamics of the GTQ/USD are related to the Guatemalan trade-related currency movements, receipt and payment of foreign loans, and remittance payments of Guatemalans working abroad. We show that the in-sample statistical performance of the DCS-Skew-Gent and the DCS-NIG models is superior to that of the DCS-t and the DCS-EGB2 models, respectively. Furthermore, we show that the statistical performance of all DCS models is superior to that of the standard financial time series model. Keywords Dynamic Conditional Score (DCS) models • Guatemalan Quetzal (GTQ) to United States Dollar (USD) exchange rate • Stochastic seasonality component JEL Classification C22 • C52 • C58 • F31 B Szabolcs Blazsek

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“…Two of the main advantages of the GAS framework are its ability to consider different non-Gaussian distributions and its flexibility with respect to the updating mechanism, which is determined by the chosen distribution determines the model updating mechanism. These properties have led GAS models to be applied in numerous fields, such as finance (Harvey and Thiele 2016;Ayala and Blazsek 2018), actuaries (Neves et al 2017;de Melo et al 2018), risk analysis (Patton et al 2019;Nani et al 2019), and renewable generation (Saavedra 2017; Hoeltgebaum et al 2018). We also refer the interested reader to a large online repository of works on GAS models at http://www.gasmodel.com.…”

Section: Introductionmentioning

confidence: 99%

ScoreDrivenModels.jl: a Julia Package for Generalized Autoregressive Score Models

Bodin¹,

Saavedra²,

Fernandes³

et al. 2020

Preprint

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Score-driven models, also known as generalized autoregressive score (GAS) models, represent a class of observation-driven time series models. They possess powerful properties, such as the ability to model different conditional distributions and to consider timevarying parameters within a flexible framework. In this paper, we present ScoreDriven-Models.jl, an open-source Julia package for modeling, forecasting, and simulating time series using the framework of score-driven models. The package is flexible with respect to model definition, allowing the user to specify the lag structure and which parameters are time-varying or constant. It is also possible to consider several distributions, including Beta, Exponential, Gamma, Lognormal, Normal, Poisson, Student's t, and Weibull. The provided interface is flexible, allowing interested users to implement any desired distribution and parametrization.

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Score-driven copula models for portfolios of two risky assets

Cited by 12 publications

References 45 publications

Dynamic copulas and applications in finance

Dynamic copulas and applications in finance

Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar

ScoreDrivenModels.jl: a Julia Package for Generalized Autoregressive Score Models

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