The exchange rate risk of Chinese yuan: Using VaR and ES based on extreme value theory

Wang, Zongrun; Wu, Wei; Chen, Chao; Zhou, Yanju

doi:10.1080/02664760902846114

Cited by 28 publications

(17 citation statements)

References 14 publications

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“…The GARCH‐EVT model proposed by and combines GARCH models to estimate the current volatility and EVT for estimating the tail of the innovation distribution of the GARCH model. The model has been widely used to estimate extreme financial risk, see .…”

Section: Methods For Estimating Financial Riskmentioning

confidence: 99%

“…The GARCH-EVT model proposed by [16] and [17] combines GARCH models to estimate the current volatility and EVT for estimating the tail of the innovation distribution of the GARCH model. The model has been widely used to estimate extreme financial risk, see [18]. As we know, the exponentially weighted moving average model is a special case of a generalized autoregressive conditional heteroscedasticity model proposed by [19] with the GARCH(1,1) form…”

Section: Garch-evt Modelmentioning

confidence: 99%

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Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk

Liu

Jiang

et al. 2016

Appl Stoch Models Bus & Ind

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The parametric conditional autoregressive expectiles (CARE) models have been developed to estimate expectiles, which can be used to assess value at risk and expected shortfall. The challenge lies in parametric CARE modeling is the specification of a parametric form. To avoid any model misspecification, we propose a nonparametric CARE model via neural network. The nonparametric CARE model can be estimated by a classical gradient based nonlinear optimization algorithm, and the consistency of nonparametric conditional expectile estimators is established. We then apply the nonparametric CARE model to estimating value at risk and expected shortfall of six stock indices. Empirical results for the new model is competitive with those classical models and parametric CARE models. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Methods For Estimating Financial Riskmentioning

confidence: 99%

Section: Garch-evt Modelmentioning

confidence: 99%

Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk

Liu

Jiang

et al. 2016

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

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“…To estimate the VaR with high peaks and heavy tails of exchange rate returns, it is ideal to assume that U(·) follows Student's t-distribution (Wang et al, 2010). When h is small, an approximate formula for the 100a per cent h-day Student's t VaR is given by…”

Section: Parametric Methodsmentioning

confidence: 99%

Value-at-Risk Estimation of Foreign Exchange Rate Risk in India

Swami

Pandey

Pancholy

2016

Asia-Pacific Journal of Management Research and Innovation

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Banks are required to maintain an appropriate level of capital which must commensurate with the riskiness of their portfolio. Recently, the Reserve Bank of India (RBI) issued a circular on Prudential Guidelines on Capital Adequacy-Implementation of Internal Models Approach (IMA) for Market Risk to select a suitable method for the banks to determine the regulatory capital requirement under the market risk exposure. Banks which adopt this approach are required to quantify market risk through their own Value-at-Risk (VaR) model. Therefore, it is a challenging task for risk managers of the bank to select an appropriate risk model which reasonably covers the risk of the bank's portfolio. Use of wrongly calibrated risk models may lead to undercapitalised banking system. This article aims at exploring the suitable risk model for measuring foreign exchange risk in banks' portfolio. The objective of present study is to empirically test the appropriate VaR model for foreign exchange rate risk. Value-at-Risk has been estimated for foreign exchange rate risk by using parametric variance-covariance method and non-parametric historical simulation (HS) method. Under parametric method, VaR is estimated by assuming that returns follow normal and Student's t-distribution. Backtesting results for various VaR models have been done based on Kupiec's proportion of failures (KPOF) test and regulatory 'traffic light' test. This article concludes that when returns are non-normal, VaR model based on the assumption of normality significantly underestimates the risk. Our empirical results based on backtesting show that most accurate VaR estimates are obtained from Student's t VaR model.

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“…Despite the plurality of articles that use VaR to measure the risk of different exchange rates, such as Zhan & Tian (2000), Wang et al (2010), Li et al (2007), among others, there are few studies that investigate the exchange rate risk of the brazilian real. Moreover, in the current scenario, in which the country has been facing several economic and political reforms in which the local currency has been showing strong volatility, the measurement of the risk of the real acquires a character of urgency, presenting, therefore, great importance.…”

Section: Introductionmentioning

confidence: 99%

Value-at-Risk (VaR) Real brasileiro e moedas de mercados emergentes e em desenvolvimento

2018

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Resumo A globalização tem sido responsável pela elevação da exposição de empresas e países a riscos relativos a fatores cambiais. A literatura tem abordado a questão da volatilidade da moeda em países emergentes e desenvolvidos sem um consenso sobre tal dinâmica, bem como são escassos os estudos que estimam e situam o risco do real brasileiro. Dessa forma, o presente estudo teve por objetivo mensurar o risco da taxa de câmbio brasileira, bem como testar a hipótese de que existe uma diferença significante na volatilidade entre moedas de países emergentes e em desenvolvimento. Por meio do VaR paramétrico ajustado para distribuições de valores extremos, observou-se que o real brasileiro apresentou taxa de câmbio de maior risco. Os resultados também revelam que os pressupostos distribucionais não parecem apresentar nenhum padrão específico quando confrontados países emergentes e desenvolvidos, e também que países emergentes apresentaram menor volatilidade, denotando a preponderância de fatores externos ao mercado cambial na determinação do risco de algumas moedas.

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The exchange rate risk of Chinese yuan: Using VaR and ES based on extreme value theory

Cited by 28 publications

References 14 publications

Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk

Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk

Value-at-Risk Estimation of Foreign Exchange Rate Risk in India

Value-at-Risk (VaR) Real brasileiro e moedas de mercados emergentes e em desenvolvimento

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