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In this paper we reconsider the pricing of options in incomplete continuous time markets. We first discuss option pricing with idiosyncratic stochastic volatility. This leads, of course, to an averaged Black-Scholes price formula. Our proof of this result uses a new formalization of idiosyncrasy which encapsulates other definitions in the literature. Our method of proof is subsequently generalized to other forms of incompleteness and systematic (i.e. non-idiosyncratic) information. Generally this leads to an option pricing formula which can be expressed as the average of a complete markets formula.
This volume offers the reader practical methods to compute the option prices in the incomplete asset markets. The [GLP & MEMM] pricing models are clearly introduced, and the properties of these models are discussed in great detail. It is shown that the geometric L(r)vy process (GLP) is a typical example of the incomplete market, and that the MEMM (minimal entropy martingale measure) is an extremely powerful pricing measure. This volume also presents the calibration procedure of the [GLP \& MEMM] model that has been widely used in the application of practical problem
This paper reconsiders the predictions of the standard option pricing models in the context of incomplete markets. We relax the completeness assumption of the Black-Scholes (1973) model and as an immediate consequence we can no longer construct a replicating portfolio to price the option. Instead, we use the good-deal bounds technique to arrive at closed-form solutions for the option price. We determine an upper and a lower bound for this price and find that, contrary to Black-Scholes (1973) options theory, increasing the volatility of the underlying asset does not necessarily increase the option value. In fact, the lower bound prices are always a decreasing function of the volatility of the underlying asset, which cannot be explained by a Black-Scholes (1973) type of argument. In contrast, this is consistent with the presence of unhedgeable risk in the incomplete market. Furthermore, in an incomplete market where the underlying asset of an option is either infrequently traded or non-traded, early exercise of an American call option becomes possible at the lower bound, because the economic agent wants to lock in value before it disappears as a result of increased unhedgeable risk.
New international accounting standards requires insurers to reflect the value of embedded options and guarantees in their products. Pricing techniques based on the Black amp; Scholes paradigm are often used, however, the hypotheses underneath this model are rarely met.We propose a framework that encompasses the most known sources of incompleteness. We show that the surrender option, joined with a wide range of claims embedded in insurance contracts, can be priced through our tool, and deliver hedging portfolios to mitigate the risk arising from their positions. We provide extensive empirical analysis to highlight the effect of incompleteness on the fair value of the option.
This paper develops an approach to tighten the bounds on asset pricing in an incomplete market that combines no-arbitrage pricing and preference-based pricing, and the approach is applied to call options without dynamic rebalancing. With the no-arbitrage pricing, it is straightforward to obtain the initial bounds, which are too wide to be of practical uses. By accepting that investors exhibit risk aversion from benchmark pricing kernels, it is possible to narrow the bounds considerably. Using the minimax deviation implicit in the parameters, one can restrict further the set of plausible values for call options on a stock.
It is often useful to price assets and other random payoffs by reference to other observed prices rather than construct full-fledged economic asset pricing models. This approach breaks down if one cannot find a perfect replicating portfolio. We impose weak economic restrictions to derive usefully tight bounds on asset prices in this situation. The bounds basically rule out high Sharpe ratios - `good deals' - as well as arbitrage opportunities. We present the method of calculation, we extend it to a multiperiod context by finding a recursive solution, and we apply it to option pricing examples including the Black-Scholes setup with infrequent trading, and a model with stochastic stock volatility and a varying riskfree rate.