Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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The world of finance works better through logistics and there are more to a risk measurement and hedging than being coherent. Thus, several predictable assumptions hast been made in other to make risk calculation and hedging tractable which both Value-at-risk (VaR) and Conditional tail expectation (CTE or CVAR) ignore useful information on target . The question is can the classical law of iterated logarithm(LIL)be centralized for risky and contingent hedging capacities? Here we find the imposition of the law of iterated logarithm (LIL) constraint unique and complete, hence continuous for the QUEST as it utilizes information in the whole distribution, curbs rate of returns on target, provides incentives for risk management and raises challenges of performances and cost.
Bright O. Osu.. 2014. \u201cOptimal Hedging Strategy of Asset Returns on Target In Finance Logistics using the Law of Iterated Logarithm (LIL) Measure\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 14 (GJSFR Volume 14 Issue F1): .
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
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Total Score: 103
Country: Nigeria
Subject: Global Journal of Science Frontier Research - F: Mathematics & Decision
Authors: Bright O. Osu., Jonathan O. Egemba, Philip U. Uzoma (PhD/Dr. count: 0)
View Count (all-time): 155
Total Views (Real + Logic): 4726
Total Downloads (simulated): 2321
Publish Date: 2014 05, Sat
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Neural Networks and Rules-based Systems used to Find Rational and
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The world of finance works better through logistics and there are more to a risk measurement and hedging than being coherent. Thus, several predictable assumptions hast been made in other to make risk calculation and hedging tractable which both Value-at-risk (VaR) and Conditional tail expectation (CTE or CVAR) ignore useful information on target . The question is can the classical law of iterated logarithm(LIL)be centralized for risky and contingent hedging capacities? Here we find the imposition of the law of iterated logarithm (LIL) constraint unique and complete, hence continuous for the QUEST as it utilizes information in the whole distribution, curbs rate of returns on target, provides incentives for risk management and raises challenges of performances and cost.
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