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Importance Sampling for Least-Square Monte Carlo Methods
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Importance Sampling för Least-Square Monte Carlo-metoder (Swedish)
Abstract [en]

Pricing American style options is challenging due to early exercise opportunities. The conditional expectation in the Snell envelope, known as the continuation value is approximated by basis functions in the Least-Square Monte Carlo-algorithm, giving robust estimation for the options price. By change of measure in the underlying Geometric Brownain motion using Importance Sampling, the variance of the option price can be reduced up to 9 times. Finding the optimal estimator that gives the minimal variance requires careful consideration on the reference price without adding bias in the estimator. A stochastic algorithm is used to find the optimal drift that minimizes the second moment in the expression of the variance after change of measure. The usage of Importance Sampling shows significant variance reduction in comparison with the standard Least-Square Monte Carlo. However, Importance Sampling method may be a better alternative for more complex instruments with early exercise opportunity.

Abstract [sv]

Prissättning av amerikanska optioner är utmanande på grund av att man har rätten att lösa in optionen innan och fram till löptidens slut. Det betingade väntevärdet i Snell envelopet, känd som fortsättningsvärdet approximeras med basfunktioner i Least-Square Monte Carlo-algoritmen som ger robust uppskattning av optionspriset. Genom att byta mått i den underliggande geometriska Browniska rörelsen med Importance Sampling så kan variansen av optionspriset minskas med upp till 9 gånger. Att hitta den optimala skattningen som ger en minimal varians av optionspriset kräver en noggrann omtanke om referenspriset utan att skattningen blir för skev. I detta arbete används en stokastisk algoritm för att hitta den optimala driften som minimerar det andra momentet i uttrycket av variansen efter måttbyte. Användningen av Importance Sampling har visat sig ge en signifikant minskning av varians jämfört med den vanliga Least-Square Monte Carlometoden. Däremot kan importance sampling vara ett bättre alternativ för mer komplexa instrument där man har rätten at lösa in instrumentet fram till löptidens slut.

Place, publisher, year, edition, pages
TRITA-MAT-E, 2016:64
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-193080OAI: diva2:1033615
External cooperation
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Available from: 2016-10-07 Created: 2016-09-28 Last updated: 2016-10-07Bibliographically approved

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