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1 DETERMINANTS OF VIOLATION OF PUT
1 DETERMINANTS OF VIOLATION OF PUT

Speculative Investor Behavior in a Stock Market with
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Using futures and options to manage price volatility in food imports: practice
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... The term "derivatives” refers to financial instruments, the prices and settlements of which depend on the evolution of value of an underlying asset or commodity. There is a broad spectrum of derivatives, ranging from very simple "plain vanilla” products to the more exotic types. In commodity risk ma ...
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Can the Black-Scholes-Merton Model Survive Under Transaction
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... This paper generalizes the Black-Scholes-Merton (BSM) option pricing model to incorporate proportional transaction costs. It derives a lower bound on the price of a call option in a discrete time setting that, if violated, will create superior returns for investors under realistic trading conditions ...
The Investment Return from a Constantly Rebalancing Asset Mix
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... To manage the market volatility risk, say of the S&P500 index, a new instrument, a straddle option or STO (KSTO, T1, T2) with the following specifications, could be introduced. At the maturity date T1 of this contract, the buyer has the option to buy a then at-the-money-forward straddle with a pre sp ...
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... volatility for a particular maturity depends only on the moneyness (that is, the ratio of the price of the underlying asset to the strike price). The first attempts to model the volatility surface were by Rubinstein (1994), Derman and Kani (1994), and Dupire (1994). These authors show how a one-fact ...
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Risk-neutral Density Extraction from Option Prices

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Black–Scholes model

The Black–Scholes /ˌblæk ˈʃoʊlz/ or Black–Scholes–Merton model is a mathematical model of a financial market containing derivative investment instruments. From the model, one can deduce the Black–Scholes formula, which gives a theoretical estimate of the price of European-style options. The formula led to a boom in options trading and legitimised scientifically the activities of the Chicago Board Options Exchange and other options markets around the world. lt is widely used, although often with adjustments and corrections, by options market participants. Many empirical tests have shown that the Black–Scholes price is ""fairly close"" to the observed prices, although there are well-known discrepancies such as the ""option smile"".The Black–Scholes model was first published by Fischer Black and Myron Scholes in their 1973 paper, ""The Pricing of Options and Corporate Liabilities"", published in the Journal of Political Economy. They derived a partial differential equation, now called the Black–Scholes equation, which estimates the price of the option over time. The key idea behind the model is to hedge the option by buying and selling the underlying asset in just the right way and, as a consequence, to eliminate risk. This type of hedging is called delta hedging and is the basis of more complicated hedging strategies such as those engaged in by investment banks and hedge funds.Robert C. Merton was the first to publish a paper expanding the mathematical understanding of the options pricing model, and coined the term ""Black–Scholes options pricing model"". Merton and Scholes received the 1997 Nobel Memorial Prize in Economic Sciences for their work. Though ineligible for the prize because of his death in 1995, Black was mentioned as a contributor by the Swedish Academy.The model's assumptions have been relaxed and generalized in many directions, leading to a plethora of models that are currently used in derivative pricing and risk management. It is the insights of the model, as exemplified in the Black-Scholes formula, that are frequently used by market participants, as distinguished from the actual prices. These insights include no-arbitrage bounds and risk-neutral pricing. The Black-Scholes equation, a partial differential equation that governs the price of the option, is also important as it enables pricing when an explicit formula is not possible.The Black–Scholes formula has only one parameter that cannot be observed in the market: the average future volatility of the underlying asset. Since the formula is increasing in this parameter, it can be inverted to produce a ""volatility surface"" that is then used to calibrate other models, e.g. for OTC derivatives.
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