ML Values Autocall Derivatives

(Autor)

Buch | Softcover
110 Seiten
2024
tredition (Verlag)
978-3-384-22255-8 (ISBN)
26,59 inkl. MwSt
Machine learning (ML) is transforming the way we value complex financial instruments like Phoenix autocalls. These options come with a unique twist - if the underlying asset doesn't reach a certain price by a specific time (expiry), the option automatically resets, extending the expiry and offering another chance for a payout.Traditionally, valuing such options relied on complex calculations that struggled to account for market volatility and potential resets. Here's where ML steps in.By analyzing vast datasets of historical option prices and market behavior, ML algorithms can capture the nuances of Phoenix autocalls. This allows for a more accurate assessment of their value, considering factors like the likelihood of a reset and the time value of the option.This newfound precision empowers investors to make informed decisions about buying, selling, or issuing Phoenix autocalls. ML paves the way for a more efficient market for these options, benefiting both issuers seeking optimal pricing and investors seeking attractive returns.

Dr. Shah, a distinguished neurologist and healthcare economist, spearheads the "Neuro Care Revolution" initiative, merging clinical expertise with financial acumen to optimize patient outcomes and reduce healthcare costs. With extensive experience in neurology and healthcare management, Dr. Shah is committed to reshaping the landscape of neurological care.

Erscheint lt. Verlag 8.5.2024
Verlagsort mn
Sprache englisch
Maße 155 x 234 mm
Gewicht 198 g
Themenwelt Sachbuch/Ratgeber Beruf / Finanzen / Recht / Wirtschaft Geld / Bank / Börse
Wirtschaft Betriebswirtschaft / Management Finanzierung
Wirtschaft Betriebswirtschaft / Management Rechnungswesen / Bilanzen
Schlagworte AI in finance • algorithmic bias • Algorithmic Pricing • Barrier Options • Deep learning • derivative valuation • exotic options • Explainable AI (XAI) pen_spark • Explainable AI (XAI) pen_spark • Financial Engineering • financial modeling • fintech innovation • Machine learning ethics • Path-dependent derivatives • Quantitative Finance • Regulatory Considerations
ISBN-10 3-384-22255-5 / 3384222555
ISBN-13 978-3-384-22255-8 / 9783384222558
Zustand Neuware
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