DYNAMIC VALUE-AT-RISK

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Published on March 1, 2014

Author: sureshnain

Source: authorstream.com

DYNAMIC VALUE-AT-RISK (Andrey Rogachev): DYNAMIC VALUE-AT-RISK (Andrey R ogachev) SURESH NAIN INDIAN INSTITUTE OF FINANCE GREATER NOIDA INTRODUCTION: INTRODUCTION Value-at-risk OBJECTIVES OF STUDY: OBJECTIVES OF STUDY To present , develop and empirically test different Value-at-Risk estimation models . Estimation method of risk calculation Down-side risk measure Volatility Liquidity Data collection USES OF VALUE-AT-RISK : USES OF VALUE-AT-RISK Tool to measure, gear and control market risk Builds an information report used to apprise senior management of the risk run by trading and investment operations Resource allocation To set position limits for traders and to decide how to allocate limited capital resources THE ESTIMATIONS BY ONE SWISS PRIVATE BANK: THE ESTIMATIONS BY ONE SWISS PRIVATE BANK Risk characteristic tool in the practice of the risk estimates. To relate the theory of risk management and the practice aspects of risk analysis. Analysis the connection between VaR Possible risk factors Trade limits PowerPoint Presentation: METHODOLOGY Wegelin & co. Use the three techniques Historical simulation Stress test Scenario simulation The risk policy based on Risk responsibility, Risk management, Risk control. Wegelin Value-at-Risk Scenarios: Wegelin Value-at-Risk Scenarios The Value-at-Risk concept scenarios are defined to calculate the changes in market risk factors and the potential losses, which would result with the occurrence of the scenarios. Share quotations, The volatility of the share quotations , Interests , Exchange rates, Price of raw materials . Value-at-Risk and Limitation: Value-at-Risk and Limitation Our empirical study is based on approximately 1500 Portfolios . For the past 3 years , 95% confidence level . The conversion for the one-day holding period applied in the RiskSys and 99% confidence level is based on using the Square-T-formula. THE STRUCTURE OF A VALUE-AT-RISK LIMIT SYSTEM: THE STRUCTURE OF A VALUE-AT-RISK LIMIT SYSTEM THE FIRST EMPIRICAL RESULTS: THE FIRST EMPIRICAL RESULTS Wegelin & Co. one hopes that the real losses more than the fixed Value-at-Risk for every Portfolio with a probability of 99 %. In reality it is only with about 28% . T he daily limit were crossed only in less than 5% of all cases It is significant to find the correct connection between the scaling rank and the number of the non-linear portfolio . The results show us a clear positive autocorrelation PowerPoint Presentation: Value-at-risk simulation with 10,000 simulations according to Monte-Carlo models for daily price changes during one year 250 active trade days a year. Price changes are normally distributed with Brown’s geometric fluctuation, the drift is 10% and volatility is 23%. We simulated portfolio value according to two strategies (i) with and (ii) without limits re-valuation: We simulated portfolio value according to two strategies ( i ) with and (ii) without limits re-valuation Normal distribution we have a situation when in the worst case the limits are fully used. For the fat tails study the Value-at-Risk limit can be significantly more implied: Normal distribution we have a situation when in the worst case the limits are fully used. For the fat tails study the Value-at-Risk limit can be significantly more implied Conclusion: Conclusion VaR can be used to quantify the market risks in different portfolios. Estimate current and future market value of portfolios The VaR models that are most relevant risk factors: interest rate risk, liquidity risk and prepayment risk.

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