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Book part
Publication date: 31 December 2010

Rania Hentati and Jean-Luc Prigent

Purpose – In this chapter, copula theory is used to model dependence structure between hedge fund returns series.Methodology/approach – Goodness-of-fit tests, based on the…

Abstract

Purpose – In this chapter, copula theory is used to model dependence structure between hedge fund returns series.

Methodology/approach – Goodness-of-fit tests, based on the Kendall's functions, are applied as selection criteria of the “best” copula. After estimating the parametric copula that best fits the used data, we apply previous results to construct the cumulative distribution functions of the equally weighted portfolios.

Findings – The empirical validation shows that copula clearly allows better estimation of portfolio returns including hedge funds. The three studied portfolios reject the assumption of multivariate normality of returns. The chosen structure is often of Student type when only indices are considered. In the case of portfolios composed by only hedge funds, the dependence structure is of Franck type.

Originality/value of the chapter – Introducing goodness-of-fit bootstrap method to validate the choice of the best structure of dependence is relevant for hedge fund portfolios. Copulas would be introduced to provide better estimations of performance measures.

Details

Nonlinear Modeling of Economic and Financial Time-Series
Type: Book
ISBN: 978-0-85724-489-5

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Book part
Publication date: 1 December 2008

Wenbo Hu and Alec N. Kercheval

Portfolio credit derivatives, such as basket credit default swaps (basket CDS), require for their pricing an estimation of the dependence structure of defaults, which is…

Abstract

Portfolio credit derivatives, such as basket credit default swaps (basket CDS), require for their pricing an estimation of the dependence structure of defaults, which is known to exhibit tail dependence as reflected in observed default contagion. A popular model with this property is the (Student's) t-copula; unfortunately there is no fast method to calibrate the degree of freedom parameter.

In this paper, within the framework of Schönbucher's copula-based trigger-variable model for basket CDS pricing, we propose instead to calibrate the full multivariate t distribution. We describe a version of the expectation-maximization algorithm that provides very fast calibration speeds compared to the current copula-based alternatives.

The algorithm generalizes easily to the more flexible skewed t distributions. To our knowledge, we are the first to use the skewed t distribution in this context.

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Econometrics and Risk Management
Type: Book
ISBN: 978-1-84855-196-1

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Abstract

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Structural Models of Wage and Employment Dynamics
Type: Book
ISBN: 978-0-44452-089-0

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Article
Publication date: 26 May 2021

Wuyi Ye and Ruyu Zhao

The stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market…

Abstract

Purpose

The stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market from crashing by accurately estimating the risk on continuously rising returns (CRR) and continuously falling returns (CFR).

Design/methodology/approach

The authors add an exogenous variable into Log-autoregressive conditional duration (Log-ACD) model, and then apply our extended Log-ACD model and Archimedean copula to estimate the marginal distribution and conditional distribution of CRR and CFR. Plus, the authors analyze the conditional value at risk (CVaR) and present back-test results of the CVaR. The back-test shows that our proposed risk estimation method has a good estimation power for the risk of the CRR and CFR, especially the downside risk. In addition, the authors detect whether the dependent structure between the CRR and CFR changes using the change point test method.

Findings

The empirical results indicate that there is no change point here, suggesting that the results on the dependent structure and risk analysis mentioned above are stable. Therefore, major financial events will not affect the dependent structure here. This is consistent with the point that the CRR and CFR can be analyzed to obtain the trend of stock returns from a more macro perspective than daily stock returns scholars usually study.

Practical implications

The risk estimation method of this paper is of great significance in understanding stock market risk and can provide corresponding valuable information for investment advisors and public policy regulators.

Originality/value

The authors defined a new stock returns, CRR and CFR, since it is difficult to analyze and predict the trend of stock returns according to daily stock returns because of the small autocorrelation among daily stock returns.

Details

The Journal of Risk Finance, vol. 22 no. 1
Type: Research Article
ISSN: 1526-5943

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Book part
Publication date: 16 December 2009

Chinman Chui and Ximing Wu

Knowledge of the dependence structure between financial assets is crucial to improve the performance in financial risk management. It is known that the copula completely…

Abstract

Knowledge of the dependence structure between financial assets is crucial to improve the performance in financial risk management. It is known that the copula completely summarizes the dependence structure among multiple variables. We propose a multivariate exponential series estimator (ESE) to estimate copula densities nonparametrically. The ESE has an appealing information-theoretic interpretation and attains the optimal rate of convergence for nonparametric density estimations in Stone (1982). More importantly, it overcomes the boundary bias of conventional nonparametric copula estimators. Our extensive Monte Carlo studies show the proposed estimator outperforms the kernel and the log-spline estimators in copula estimation. It also demonstrates that two-step density estimation through an ESE copula often outperforms direct estimation of joint densities. Finally, the ESE copula provides superior estimates of tail dependence compared to the empirical tail index coefficient. An empirical examination of the Asian financial markets using the proposed method is provided.

Details

Nonparametric Econometric Methods
Type: Book
ISBN: 978-1-84950-624-3

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Article
Publication date: 27 January 2020

Hemant Kumar Badaye and Jason Narsoo

This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to…

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Abstract

Purpose

This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY.

Design/methodology/approach

By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent intraday portfolio VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation.

Findings

In terms of VaR forecasting performance, the backtesting results indicated that four out of the five models implemented could not be rejected at 5 per cent level of significance. However, when the models were further evaluated for their ES forecasting power, only the Student’s t and Clayton models could not be rejected. The fact that some ES models were rejected at 5 per cent significance level highlights the importance of selecting an appropriate copula model for the dependence structure.

Originality/value

To the best of the authors’ knowledge, this is the first study to use the MC-GARCH and copula models to forecast, for the next 1 min, the VaR and ES of an equally weighted portfolio of foreign currencies. It is also the first study to analyse the performance of the MC-GARCH model under seven distributional assumptions for the innovation term.

Details

The Journal of Risk Finance, vol. 21 no. 5
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 31 July 2020

Atina Ahdika, Dedi Rosadi, Adhitya Ronnie Effendie and Gunardi

Farmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use…

Abstract

Purpose

Farmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use in risk modeling in crop insurance. This study seeks to propose a design for a household margin insurance scheme of the agricultural sector based on FER.

Design/methodology/approach

This research employs various risk modeling concepts, i.e. value at risk, loss models and premium calculation, to construct the proposed model. The standard linear, static and time-varying copula models are used to identify the dependency between variables involved in calculating FER.

Findings

First, FER can be considered as the primary variable for risk modeling in agricultural household margin insurance because it demonstrates farmers’ financial ability. Second, temporal dependence estimated using the time-varying copula can minimize errors, reduce the premium rate and result in a tighter guarantee's level of security.

Originality/value

This research extends the previous similar studies related to the use of index ratio in margin insurance loss modeling. Its authenticity is in the use of FER, which represents the farmers' trading capability. FER determines farmers’ losses by considering two aspects: the farmers’ income rate and their ability to fulfill their life and farming needs. Also, originality exists in the use of the time-varying copulas in identifying the dependence of the indices involved in calculating FER.

Details

Agricultural Finance Review, vol. 81 no. 2
Type: Research Article
ISSN: 0002-1466

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Article
Publication date: 4 May 2020

A. Ford Ramsey, Sujit K. Ghosh and Barry K. Goodwin

Revenue insurance is the most popular form of insurance available in the US federal crop insurance program. The majority of crop revenue policies are sold with a harvest…

Abstract

Purpose

Revenue insurance is the most popular form of insurance available in the US federal crop insurance program. The majority of crop revenue policies are sold with a harvest price replacement feature that pays out on lost crop yields at the maximum of a realized or projected harvest price. The authors introduce a novel actuarial and statistical approach to rate revenue insurance policies with exotic price coverage: the payout depends on an order statistic or average of prices. The authors examine the price implications of different dependence models and demonstrate the feasibility of policies of this type.

Design/methodology/approach

Hierarchical Archimedean copulas and vine copulas are used to model dependence between prices and yields and serial dependence of prices. The authors construct several synthetic exotic price coverage insurance policies and evaluate the impact of copula models on policies covering different types of risk.

Findings

The authors’ findings show that the price of exotic price coverage policies is sensitive to the choice of dependence model. Serial dependence varies across the growing season. It is possible to accurately price exotic coverage policies and we suggest these add-ons as a possible avenue for developing private crop insurance markets.

Originality/value

The authors apply hierarchical Archimedean copulas and vine copulas that allow for flexibility in the modeling of multivariate dependence. Unlike previous research, which has primarily considered dependence across space, the form of exotic price coverage requires modeling serial dependence in relative prices. Results are important for this segment of the agricultural insurance market: one of the main areas that insurers can develop private products around the federal program.

Details

Agricultural Finance Review, vol. 80 no. 5
Type: Research Article
ISSN: 0002-1466

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Article
Publication date: 2 October 2020

Xiu Wei Yeap, Hooi Hooi Lean, Marius Galabe Sampid and Haslifah Mohamad Hasim

This paper investigates the dependence structure and market risk of the currency exchange rate portfolio from the Malaysian ringgit perspective.

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266

Abstract

Purpose

This paper investigates the dependence structure and market risk of the currency exchange rate portfolio from the Malaysian ringgit perspective.

Design/methodology/approach

The marginal return of the five major exchange rates series, i.e. United States dollar (USD), Japanese yen (JPY), Singapore dollar (SGD), Thai baht (THB) and Chinese Yuan Renminbi (CNY) are modelled by the Bayesian generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model with Student's t innovations. In addition, five different copulas, such as Gumbel, Clayton, Frank, Gaussian and Student's t, are applied for modelling the joint distribution for examining the dependence structure of the five currencies. Moreover, the portfolio risk is measured by Value at Risk (VaR) that considers the extreme events through the extreme value theory (EVT).

Findings

The finding shows that Gumbel and Student's t are the best-fitted Archimedean and elliptical copulas, for the five currencies. The dependence structure is asymmetric and heavy tailed.

Research limitations/implications

The findings of this paper have important implications for diversification decision and hedging problems for investors who involving in foreign currencies. The authors found that the portfolio is diversified with the consideration of extreme events. Therefore, investors who are holding an individual currency with VaR higher than the portfolio may consider adding other currencies used in this paper for hedging.

Originality/value

This is the first paper estimating VaR of a currency exchange rate portfolio using a combination of Bayesian GARCH model, EVT and copula theory. Moreover, the VaR of the currency exchange rate portfolio can be used as a benchmark of the currency exchange market risk.

Details

International Journal of Emerging Markets, vol. 16 no. 5
Type: Research Article
ISSN: 1746-8809

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Article
Publication date: 20 October 2020

Hong Shen, Yue Tang, Ying Xing and Pin Ng

This paper aims to examine the evidence of risk spillovers between Shanghai and London non-ferrous futures markets using a dynamic Copula-CoVaR approach.

Abstract

Purpose

This paper aims to examine the evidence of risk spillovers between Shanghai and London non-ferrous futures markets using a dynamic Copula-CoVaR approach.

Design/methodology/approach

With daily data, the marginal distributions and optimal Copula functions are determined using the kernel estimation method and squared Euclidean distance test. The conditional value-at-risk and the conditional value-at-risk spillover rate are computed from the Copula estimated parameters based on the Copula-CoVaR model. Also, the dynamic correlation coefficient between the two futures markets is investigated.

Findings

The empirical results are as follows: overall, the risk spillover effect exerted by the London Metal Exchange on the Shanghai Futures Exchange is more significant than vice versa. Moreover, the degree of risk spillovers exerted by the London Metal Exchange on the Shanghai Futures Exchange for zinc and copper are more significant when they are depressed in the London Metal Exchange. Moreover, the dynamic of the correlation between the Shanghai and London futures markets is attributed to be largely due to changes in the global economy.

Research limitations/implications

The Copula-CoVaR model used in this paper is suitable for measuring the risk spillovers between two different markets, while the risk spillovers across multiple markets or the consideration of multiple risk factors cannot be accurately captured using this framework. Multiple state variables to capture time variation in the conditional moments of return series will be a topic in future research.

Practical implications

The results provide theoretical support for risk management and monitoring of the non-ferrous futures markets.

Originality/value

The ability of the Copula function to accurately describe a nonlinear relationship and tail correlation is harnessed to measure the risk spillovers, explore the degree and direction of risk spillovers and identify the source of risk spillovers. The global economy is incorporated as a macro factor to explore its inner connection with the dynamic of risk spillovers in the non-ferrous metal futures market.

Details

International Journal of Emerging Markets, vol. 16 no. 5
Type: Research Article
ISSN: 1746-8809

Keywords

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