Nonparametric Inference of Value-at-Risk for Dependent Financial Returns

By S. Chen, Cheng Yong Tang. 2005

176 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/97721cbfb6b125be4b368cad251ec82ff76e9532

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. James W. Taylor
2. Zongwu Cai
3. Carol Alexander
4. Esther Ruiz
5. Xian Wang
6. M. Nieto
7. Elizabeth Sheedy
8. Wing-Keung Wong

In Economics and Econometrics

1. M. Nieto
2. Esther Ruiz
3. James W. Taylor
4. Zongwu Cai
5. Xian Wang
6. Carol Alexander
7. Elizabeth Sheedy
8. Qi Li

In Finance

1. M. Nieto
2. Esther Ruiz
3. James W. Taylor
4. Xian Wang
5. Qi Li
6. Carlos Martins-Filho
7. Maximo Torero
8. Feng Yao

Citations in the corpus

• Developing a stress testing framework based on market risk models (2008, methodology): “Following Sheather and Marron (1990), Butler and Schachter (1998) and Chen and Tang (2005) we therefore smooth the empirical density by fitting the Epanechnikov kernel to a sample of daily returns.”
• Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall (2008, methodology): “interesting extension of the theoretical work of Chen and Tang (2005) would be to derive standard”
• NONPARAMETRIC ESTIMATION OF CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL BASED ON EXTREME VALUE THEORY (2016, background): “Also, for the non-extreme case, there exists a large literature on nonparametric estimation of uncon- ditional value-at-risk and expected shortfall, see e.g., Scaillet (2004), Chen and Tang (2005), Chen (2008), Linton and Xiao (2013) and Hill (2015), that is indirectly related to our work.”
• Hybrid Historical Simulation VaR and ES: Performance in Developed and Emerging Markets (2009, unknown): “found by Chen and Tang (2005). It is useful to analyze the averages of VaR forecasts for the models that satisfy the”
• Nonparametric estimation of conditional VaR and expected shortfall (2008, methodology): “5 See, e.g., Pötscher and Prucha (1997), Cai (2002, 2007), Carrasco and Chen (2002), Meitz and Saikkonen (2004) and Chen and Tang (2005).”
• Functional-coefficient models for nonstationary time series data (2009, background): “Stationary linear and nonlinear time series or Markov chains fulfilling certain (mild) conditions are α-mixing with exponentially decaying coefficients; see discussions and examples in Cai (2002a), Carrasco and Chen (2002) and Chen and Tang (2005).”
• Nonparametric Estimation of Expected Shortfall (2007, methodology): “Following the lines of Fan and Gijbels (1996) and Chen and Tang (2005), a Nadaraya–Waston kernel estimator of m (0) based on a symmetric kernel K 1 and a smoothing bandwidth λ is where λ → 0 and n λ → ∞ as n → ∞ .”
• Frontiers in VaR forecasting and backtesting (2016, methodology): “In the context of nonparametric procedures to compute directly the quantile of the distribution of returns, Chen and Tang (2005) emphasize the importance of the uncertainty of VaR forecasts and develop a procedure for its estimation based on a kernel estimation of the spectral density function of a…”
• On Testing the Equality of the Multiple Sharpe Ratios, with Application on the Evaluation of Ishares (2008, background): “Chen (2005) considers nonparametric estimation of VaR and associated standard error estimation for dependent financial returns.”