ML Values Autocall Derivatives
Produktnummer:
182a85a94384d54e1db3d3c442f78a4792
| Autor: | Shah |
|---|---|
| Themengebiete: | AI in finance Algorithmic pricing Barrier options Deep learning Derivative valuation Exotic options Financial engineering Financial modeling Path-dependent derivatives Quantitative finance |
| Veröffentlichungsdatum: | 08.05.2024 |
| EAN: | 9783384222558 |
| Sprache: | Englisch |
| Seitenzahl: | 110 |
| Produktart: | Kartoniert / Broschiert |
| Verlag: | tredition |
Produktinformationen "ML Values Autocall Derivatives"
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.
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