Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Black-Scholes-Merton -optiohinnan malli× | Realisoitu volatiliteetti ja HAR-malli× | |
|---|---|---|
| Tieteenala | Rahoitus | Rahoitus |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1973 | 2009 |
| Kehittäjä≠ | Fischer Black, Myron Scholes & Robert Merton | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| Tyyppi≠ | Continuous-time option-pricing model | Time-series regression of realized variance |
| Alkuperäislähde≠ | Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654. DOI ↗ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. DOI ↗ |
| Rinnakkaisnimet≠ | Black-Scholes formula, Black-Scholes-Merton model, BSM model, Black-Scholes opsiyon fiyatlama modeli | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | The Black-Scholes-Merton model, published by Fischer Black and Myron Scholes in 1973 with the theoretical framework extended by Robert Merton, gives a closed-form no-arbitrage price for European options. By assuming the underlying asset follows geometric Brownian motion with constant volatility, it derives a partial differential equation whose solution expresses the option price in terms of the stock price, strike, time to maturity, risk-free rate, and volatility — transforming option pricing from intuition into a rigorous, tractable formula. | Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction. |
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