Modeling Systemic Risk: Time-Varying Tail Dependence When Forecasting Marginal Expected Shortfall

Eckernkemper, Tobias

doi:10.1093/jjfinec/nbx026

Cited by 35 publications

(24 citation statements)

References 35 publications

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“…The second part briefly discusses the Dynamic Mixture Copula Marginal Expected Shortfall (DMC-MES) employed to estimate our systemic risk measures. Thereafter, we detail the steps involved in estimating DMC-MES proposed by Eckernkemper (2018).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, stricter regulation of these institutions in the form of higher capital and loss absorbency could be required based on the individual business activities undertaken by the company. To this end, we apply the Dynamic-Mixture-Copula to estimate the marginal expected (DMC-MES) proposed by Eckernkemper(2018). The DMC-MES is based on a dynamic two component mixture copula for the market and each insurance institution.…”

Section: Introductionmentioning

confidence: 99%

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Modeling System Risk in the South African Insurance sector: A Dynamic Mixture Copula Approach

Mwamba¹,

Angaman²

2021

Preprint

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In this study, a dynamic mixture copula is used to estimate the marginal expected shortfall in the South African insurance sector. While other studies assumed nonlinear dependence to be static over time, our model capture time-varying nonlinear dependence between institutions and the market. In order to capture time-varying nonlinear dependence, the generalized autoregressive score (GAS) is used to model the dynamic copula parameters. Furthermore, our study implements a ranking that expresses to what degree individual insurers are systemically important in South Africa. We use daily stock return of five South African insurers listed in the Johannesburg Stock Exchange (JSE) from November 13, 2007 to June 15, 2020. We find that Sanlam and Discovery contribute the most to systemic risk, while Santam is found to be the least contributor to the overall systemic risk in the South African insurance sector. Our findings would be of paramount importance for the South African regulators as they would be informed that not only banks are systemically important, but some insurers also are systemically important financial institutions. Hence, stricter regulation of these institutions in the form of higher capital and loss absorbency requirements could be required based on the individual business activities undertaken by the company.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling System Risk in the South African Insurance sector: A Dynamic Mixture Copula Approach

Mwamba¹,

Angaman²

2021

Preprint

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“…The expression

μ_{t, c}^{*}

is determined by modeling the distribution of the yield conditional on the realisations of the considered indices. Following Jiang (2012) and Eckernkemper (2018), in the case of a positive index‐yield correlation

μ_{t, c}^{*}

is derived from the copula approach as follows:

μ_{t, c}^{*} = \frac{1}{p} {false\int}_{0}^{1} q_{u} (Y_{c}) \frac{\partial C (u, p; θ)}{\partial u} d u,

where

q_{u} (Y_{c})

is the quantile function,

u

stands for the marginal distribution of the yield

Y

p

is the probability that corresponds to the

p

‐quntile of the index

W

with

W \leq q_{p} (W)

and

θ

denotes the copula parameter.…”

Section: Copula Approach For the Design Of Weather Index Insurancementioning

confidence: 99%

Can satellite‐based weather index insurance improve the hedging of yield risk of perennial non‐irrigated olive trees in Spain?*

Kölle¹,

Salgueiro

Buchholz³

et al. 2020

Aus J Agri & Res Econ

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Olive oil yields fluctuate strongly due to their dependence on sufficient precipitation. An interesting option to hedge the yield risk in olive cultivation could be satellite‐based weather index insurance. Therefore, we implement index insurance as a hedging alternative for non‐irrigated olive groves using MODerate‐resolution Imaging Spectroradiometer (MODIS) satellite data. For this purpose, we focus on the Spanish region of Andalusia, given its importance in olive production at the international level. We calculate three satellite indices: the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) and the Vegetation Health Index (VHI). Meteorological indices related to temperature and precipitation are used as benchmarks. Firstly, we estimate the periods that have the greatest influence on the critical vegetative phase of olives, which extends from March to September. Based on the indices, insurance contracts are designed using a copula approach, which is then employed to evaluate their hedging effectiveness. On average, the hedging effectiveness of VCI‐, VHI‐ and TCI‐based weather index insurance contracts amounts to 38 per cent, 38 per cent and 29 per cent, respectively. Moreover, VCI‐ and VHI‐based weather index insurance contracts outperform traditional weather index insurance contracts based on precipitation (by 29 per cent) and temperature (by 16 per cent) indices.

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“…Specifically, they proposed four dynamic semiparametric models for VaR and ES based on the generalized autoregressive score (GAS) framework introduced by Creal, Koopman, and Lucas (2013). This model has been successfully applied in risk measure estimation (Patton, Ziegel, & Chen, 2019), credit default swap spread modelling (Lange, Lucas, & Siegmann, 2017;Oh & Patton, 2018), systemic risk modelling (Bernardi & Catania, 2019;Cerrato, Crosby, Kim, & Zhao, 2017;Eckernkemper, 2017) and high-frequency data modelling (Gorgi, Hansen, Janus, & Koopman, 2018;Lucas & Opschoor, 2018). 2 However, no studies on risk measures incorporating realized volatilities into the GAS framework have been considered so far.…”

Section: Introductionmentioning

confidence: 99%

“…Meng and Taylor (2018) extended the CAViaR model and the quantile regression heterogeneous autoregressive model (HAR) model with realized volatility, overnight return and intraday range. In terms of ES estimation, the CARE models of Taylor (2008) have been extended to allow intraday measures as explanatory variables (Gerlach & Chen, 2014, 2017Gerlach & Wang, 2016a;Wang, Gerlach, & Chen, 2018).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Risk Measures Using Intraday Data in a Generalized Autoregressive Score (GAS) Framework

Lazar

Xue

2019

SSRN Journal

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A new framework for the joint estimation and forecasting of dynamic value at risk (VaR) and expected shortfall (ES) is proposed by our incorporating intraday information into a generalized autoregressive score (GAS) model introduced by Patton et al., 2019 to estimate risk measures in a quantile regression setup. We consider four intraday measures: the realized volatility at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized volatilities. In a forecasting study, the set of newly proposed semiparametric models are applied to four international stock market indices (S&P 500, Dow Jones Industrial Average, Nikkei 225 and FTSE 100) and are compared with a range of parametric, nonparametric and semiparametric models, including historical simulations, generalized autoregressive conditional heteroscedasticity (GARCH) models and the original GAS models. VaR and ES forecasts are backtested individually, and the joint loss function is used for comparisons. Our results show that GAS models, enhanced with the realized volatility measures, outperform the benchmark models consistently across all indices and various probability levels.

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Modeling Systemic Risk: Time-Varying Tail Dependence When Forecasting Marginal Expected Shortfall

Cited by 35 publications

References 35 publications

Modeling System Risk in the South African Insurance sector: A Dynamic Mixture Copula Approach

Modeling System Risk in the South African Insurance sector: A Dynamic Mixture Copula Approach

Can satellite‐based weather index insurance improve the hedging of yield risk of perennial non‐irrigated olive trees in Spain?*

Forecasting Risk Measures Using Intraday Data in a Generalized Autoregressive Score (GAS) Framework

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