ML Values Autocall Derivatives

Erscheinungsjahr 2024
ISBN: 978-3-384-22255-8
Verlag: tredition

Buch, Englisch, 112 Seiten, Format (B × H): 155 mm x 234 mm, Gewicht: 203 g

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.

Zielgruppe

Machine learning is shaking up how we value Phoenix autocalls, a unique option with expiry resets. Traditional methods struggle with their complexity. ML algorithms analyze vast data to precisely assess their value, considering factors like reset likelihood and time value. This empowers investors with better decision-making for Phoenix autocalls

Autoren/Hrsg.

Shah

Über Autor(innen)

Shah
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.