Optimal Hedging Strategy of Asset Returns on Target in Finance Logistics using the Law of Iterated Logarithm (LIL) Measure

Authors

  • Bright O. Osu.

  • Jonathan O. Egemba

  • Philip U. Uzoma

law of iterated logarithm (lil), hedging, sublinear expectation, cvar, capital requirement, expected returns

Abstract

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.

How to Cite

Optimal Hedging Strategy of Asset Returns on Target in Finance Logistics using the Law of Iterated Logarithm (LIL) Measure. (2014). Global Journal of Science Frontier Research, 14(7), 21-29. https://journalofscience.org/index.php/GJSFR/article/view/

References

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Optimal Hedging Strategy of Asset Returns on Target In Finance Logistics using the  Law of Iterated Logarithm (LIL) Measure

Published

2014-02-25

How to Cite

Optimal Hedging Strategy of Asset Returns on Target in Finance Logistics using the Law of Iterated Logarithm (LIL) Measure. (2014). Global Journal of Science Frontier Research, 14(7), 21-29. https://journalofscience.org/index.php/GJSFR/article/view/