The Level and Quality of Value-at-Risk Disclosure by Commercial Banks

2009 ◽  
Author(s):  
Christophe Perignon ◽  
Daniel R. Smith
Keyword(s):  
At Risk ◽  
Commercial Banks ◽  
Value At Risk ◽  
Risk Disclosure

The level and quality of Value-at-Risk disclosure by commercial banks

2010 ◽  
Vol 34 (2) ◽  
pp. 362-377 ◽  
Author(s):  
Christophe Pérignon ◽  
Daniel R. Smith
Keyword(s):  
At Risk ◽  
Commercial Banks ◽  
Value At Risk ◽  
Risk Disclosure

scholarly journals Evaluasi Kualitas Pengungkapan Value at Risk Perbankan Indonesia

2019 ◽  
pp. 276
Author(s):  
Buddi Wibowo ◽  
Hasna Fadhila
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Simulation Method ◽  
Market Risk ◽  
Risk Measurement ◽  
Risk Level ◽  
Risk Disclosure ◽  
Historical Simulation ◽  
Risk Disclosures

Market risk measurement of bank investment portfolios is a still problem not only among practitioners, but  also among academicians. The accuracy and quality of market risk disclosures are important issues because  transparency of the bank risk level encourages market control in the form of market discipline and it also  improve the quality of risk management carried out internally by the bank. This research measures the quality of Value at Risk disclosures carried out by Indonesian banks. The accuracy of Value at Risk in this research is measured from the Value at Risk component which contains information of yield volatility of bank trading treasury activities. To measure Value at Risk disclosure, this research runs various methods of Value at Risk measurement. This research shows Historical Simulation is a Value at Risk method that is most widely used by Indonesia banks. The empirical test results show that the Value at Risk parametric method using asymmetric volatility have better quality than the Value at Risk Historical Simulation method. This research shows that Value at Risk as measured by Historical Simulation method contains the least information of bank trading treasury yields. Keywords: value at risk; disclosure; market risk; volatility

scholarly journals Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

2017 ◽  
Vol 65 (5) ◽  
pp. 1687-1694
Author(s):  
Tomáš Konderla ◽  
Václav Klepáč
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Listed Company ◽  
Mixture Distribution ◽  
Normal Mixture ◽  
Development Characteristic ◽  
The Common

The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA‑GARCH model based VaR estimates. The common testing via Kupiec and Christoffersen procedures offer generalization that HMM model performs better that volatility based VaR estimation technique in terms of accuracy, even with the simpler HMM with normal‑mixture distribution against previously used GARCH with many types of non‑normal innovations.

Value relevance of value-at-risk disclosure

2007 ◽  
Vol 29 (4) ◽  
pp. 353-370 ◽  
Author(s):  
Chee Yeow Lim ◽  
Patricia Mui-Siang Tan
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Value Relevance ◽  
Risk Disclosure

Incorporating the Time-Varying Tail-Fatness into the Historical Simulation Method for Portfolio Value-at-Risk

2006 ◽  
Vol 09 (02) ◽  
pp. 257-274 ◽  
Author(s):  
Chu-Hsiung Lin ◽  
Chang-Cheng Chang Chien ◽  
Sunwu Winfred Chen
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Moving Average ◽  
Simulation Method ◽  
Estimation Accuracy ◽  
Time Varying ◽  
Historical Simulation ◽  
Extreme Risk ◽  
Weighted Moving Average

This study extends the method of Guermat and Harris (2002), the Power EWMA (exponentially weighted moving average) method in conjunction with historical simulation to estimating portfolio Value-at-Risk (VaR). Using historical daily return data of three hypothetical portfolios formed by international stock indices, we test the performance of this modified approach to see if it can improve the precise forecasting capability of historical simulation. We explicitly highlight the extended Power EWMA owns privileged flexibilities to capture time-varying tail-fatness and volatilities of financial returns, and therefore may promote the quality of extreme risk management. Our empirical results, derived from the Kupiec (1995) tests and failure ratios, show that our proposed method indeed offers substantial improvements on capturing dynamic returns distributions, and can significantly enhance the estimation accuracy of portfolio VaR.

How Informative Are Value-at-Risk Disclosures?

2002 ◽  
Vol 77 (4) ◽  
pp. 911-931 ◽  
Author(s):  
Philippe Jorion
Keyword(s):  
At Risk ◽  
Commercial Banks ◽  
Value At Risk ◽  
Financial Risk ◽  
Preliminary Evidence ◽  
Small Sample ◽  
Financial Reports ◽  
Empirical Results ◽  
Dollar Amount ◽  
Risk Disclosures

Value at Risk (VAR), a measure of the dollar amount of potential loss from adverse market moves, has become a standard benchmark for measuring financial risk. Spurred by regulators and competitive pressures, more institutions are reporting VAR numbers in annual and quarterly financial reports. To provide preliminary evidence on the informativeness of these new disclosures, I investigate the relation between the trading VAR disclosed by a small sample of U.S. commercial banks and the subsequent variability of their trading revenues. The empirical results suggest that VAR disclosures are informative in that they predict the variability of trading revenues. Thus, analysts and investors can use VAR disclosures to compare the risk profiles of banks' trading portfolios.

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