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A Computationally More Efficient Distance based VaR Methodology for Real Time Market Risk Measurement
Background/Objectives: The main objective of this paper is to compute VaR (Value at risk) which requires minimal resources and the computing is done in real-time with utmost accuracy. Method/Statistical Analysis: The paper presents a methodology which helps in computing VaR in real time and with most accuracy. Very less computational resources are required from computing VaR. The VaR computing methodology proposed in this paper converges as the returns on the portfolio ranges increases. Findings: It has been presented in the paper that the number of valuations required for computing the VaR is dependent on the number of instruments added to the portfolio and is independent of the number of instruments already existing at the time computing VaR. The method proposed in this paper can be used for computing VaR in real time.
Market Risk, Portfolio Instruments, Risk Assessment, Real-Time Market Risk Measurement, VaR
- Rezaei Mojtaba, Jafari Seyedeh Mahbobeh, Identifying the Relationship between Financial Leverage and Cash Flows of the Companies Listed in Tehran Stock Exchange. Indian Journal of Science and Technology. 2015; 8(27):82942(1-13).
- Nakhaei Maryam, Jafari Seyedeh Mahbobeh, Survey of the Relationship between Capital Structure and Free Cash Flow with Financial Performance of Companies Listed in Tehran Stock Exchange. Indian Journal of Science and Technology. 2015; 8(27):82942(1-13).
- Leavens, Dickson H. Diversification of investments. Trusts and Estates. 1945; 80(5):469-73.
- Markowitz, Harry M. Portfolio Selection. Journal of Finance. 1952; 7(1):77-91. Available from: http://dx.doi.org/10.2307/2975974.
- James T. Liquidity preference as behaviour towards risk. The Review of Economic Studies. 1958; 25(2):65-86. Available from: http://dx.doi.org/10.2307/2296205.
- Sharpe, William F. A simplified model for portfolio analysis. Management Science. 1963; 9(1):277-93; Available from: http://dx.doi.org.
- Sharpe, William F. Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance. 1964; 19(3):425-42; Available from: http://dx.doi.org/10.2307/2977928.
- Halton GA. History of Value at Risk: 1922-1998. Working Paper. 2002.
- Tse YK. Stock Returns Volatility in the Tokyo Stock Exchange. Japan and the World Economy. 1991; 3(3):285-98; Available from: http://dx.doi.org/10.1016/0922-1425(91)90011-Z.
- Tse YK, Tung SH. Forecasting Volatility in the Singapore Stock Market. Asia Pacific Journal of Management. 1992; 9(1):1-13; Available from: http://dx.doi.org/10. 10.1007/BF01732034.
- Pafka S, Kondor I. Evaluating the Risk Metrics methodology in measuring volatility and Value-at-Risk in financial markets. Physica A: Statistical Mechanics and its Applications. 2001; 299(1-2):305-10; Available from: http://dx.doi.org/10.1016/S0378-4371(01)00310-7.
- Fan Y, Wei YM, Xu WX. Application of VaR methodology to risk management in the stock market in China. Computers & Industrial Engineering. 2004; 46(2):383-88; Available from: http://dx.doi.org/10.1016/j.cie.2003.12.018.
- So MKP, Yo PLH. Empirical Analysis of Garch Models in Value at Risk Estimation. Journal of international Financial Markets Institutions and Money. 2006; 16(2):180-97; Available from: http://dx.doi.org/10.1016/j.intfin.2005.02.001.
- Galdi FC, Pereira LM. Value at Risk (VaR) using volatility forecasting models: EWMA, GARCH and stochastic volatility. Brazilian Business Review. 2007; 4(1):74-94.
- Patev P, Kanaryan N, Lyroudi K. Modelling and forecasting the volatility of thin emerging stock markets: the case of Bulgaria. Comparative Economic Research. 2009; 12(4):47-60; Available from: http://dx.doi.org/10.2478/v10103-009-0021-8.
- Jorion P. Value at risk. MC Graw-Hill: The New Benchmark for Managing Financial Risk, Second Edition. 2000.
- Dowd K. John Wiley & Sons: England; Beyond Value at Risk, the New Science of Risk Management. 1998.
- Danielsson J, de Vries CG. Value at Risk and Extreme Returns. Annales D’economie et de Statistque. 2000; 60(1):239-70.
- Van den Goorbergh RWJ, Vlaar PJG. Value-at-Risk analysis of stock returns. Historical simulation, tail index estimation? De Nederlandse Bank-Staff Report. 40.1999.
- Giot P, Laurent S. Market risk in commodity markets: A VaR approach. Energy Econ. 2003; 25(5):435-57. Available from: http://dx.doi.org/10.1016/S0140-9883(03)00052-5.
- Vlaar PJG. Capital requirements and competition in the banking industry. WO&E, No. 634, Netherlands Central Bank, Research Department. 2000.
- Kupiec PH. Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives. 1995; 3(1):73-84; Available from: http://dx.doi.org/10.3905/jod.1995.407942.
- Haas M. New Methods in Back testing. Bonn: Financial Engineering, Research Centre Caesar. 2001.
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