ASSESSING WORST WEATHER BY ESTIMATING VALUE-AT-RISK USING HETEROSCEDASTIC PROCESS
AbstractA substantial issue in modern risk management is the measurement of risks. Specify, the requirement to quantify risk discovers in many different contexts. For instance, a regulator measures the risk exposure of a government institution in order determining the maximum value from any phenomenon occurred as a tool against unexpected losses. Particularly attention will be given to Value-at-Risk (V@R). Mostly, implementation of V@R is in financial cases, as potential alarm of institution to anticipate the magnitude of risk. Combining V@R with the forecast function of AR-ARCH processes, this paper proposes a new implementation of estimative-V@R and improved-V@R to compute heavy rain as representation of worst weather, which has the same future goal providing funds to anticipate financial losses. There are limited researches related to heavy rain forecast based on constructing a process by considering risk of with modifying some mathematics equations. We consider an overview of the existing approaches to measure V@R of weather data involving time series process and some stochastic expansion. We present V@R using AR and heteroscedastic processes ARCH considering the changes of data volatility. We consider an estimative prediction limit to determine an improved prediction limit with better conditional coverage properties. The parameter estimator of AR-ARCH is assumed to have the same asymptotic distribution as the conditional maximum likelihood estimator. This paper deals with calculation coverage probability to validate Î±-V@R performance.
Christoffersen, P. and Concalves, S. (2005). â€œEstimation risk in financial risk managementâ€. Journal of Risk 7(3), page 1-28.
Nhita, F., Adiwijaya, Annisa, S., Kinasih, S. â€œComparative study of grammatical evolution and adaptive neuroÂfuzzy Inference system on rainfall forecasting in Bandungâ€. In3rdInternational Conference on Information and Communication Technology. 2015.
S. W. Pratama, Nhita, F. and Adiwijaya,. â€œImplementation of local regression smoothing and fuzzy-grammatical evolution on rainfall forecasting for rice planting calendarâ€ In4rdInternational Conference on Information and Communication Technology. 2016.
Barndorff-Nielsen, O. E. and Cox, D. R. (1994).Inference and Asymptotics. London: Chapman and Hall.
Huang, J. J, et al. (2009). â€œEstimating value at risk of portfolio by conditional copula-GARCH methodâ€. Insurance: Mathematics and Economics 45 (3), page 315-324.
Vidoni, P. (2004). â€œImproved prediction intervals for stochastic process modelsâ€. Journal of Time Series Analysis25( 1), page 137-154.
Rohmawati, A. A. and Syuhada, K. (2015). â€œValue-at-Risk and expected shortfall relationshipâ€. International Journal of Applied Mathematics and Statistics53(5), page 211-215.
McNeil, A.J, Frey, R. and Embrechts , P. (2005). Quantitative Risk Management. Pricenton University Press.
Kabaila, P. and Syuhada, K. (2010). â€œThe asymptotic efficiency of improved prediction intervalsâ€. Statistics and Probability Letters 80, page 1348-1353.
Champavat, V. R., Patel, J. K., Patel, A. P., & Patel, G. P. (2014). MEMS : Novel Means of Smart Drug Delivery 32 | IJPRT | January â€“ March Champavat et al / International Journal of Pharmacy Research & Technology 2014 4 ( 1 ) 32-37, 4(1), 32â€“37.
Kabaila, P. and Syuhada, K. (2007). â€œImproved prediction limits for AR(p) and ARCH(p) processesâ€. Journal of Time Series Analysis 29(2), page 213-22.
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