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Given a European derivative security with an arbitrary payoff function and a corresponding set of" underlying securities on which the derivative security is based, we solve the dynamic replication problem: find a" self-financing dynamic portfolio strategy involving only the underlying securities that most closely" approximates the payoff function at maturity. By applying stochastic dynamic programming to the minimization of a" mean-squared-error loss function under Markov state-dynamics, we derive recursive expressions for the optimal-replication strategy that are readily implemented in practice. The approximation error or " " of the optimal-replication strategy is also given recursively and may be used to quantify the "degree" of market incompleteness." To investigate the practical significance of these -arbitrage strategies examples including path-dependent options and options on assets with stochastic volatility and jumps."
Given a European derivative security with an arbitrary payoff function and a corresponding set ofquot; underlying securities on which the derivative security is based, we solve the dynamic replication problem: find aquot; self-financing dynamic portfolio strategy involving only the underlying securities that most closelyquot; approximates the payoff function at maturity. By applying stochastic dynamic programming to the minimization of aquot; mean-squared-error loss function under Markov state-dynamics, we derive recursive expressions for the optimal-replication strategy that are readily implemented in practice. The approximation error or quot; quot; of the optimal-replication strategy is also given recursively and may be used to quantify the quot;degreequot; of market incompleteness. quot; To investigate the practical significance of these -arbitrage strategies examples including path-dependent options and options on assets with stochastic volatility and jumps. quot.
Arbitrage Theory provides the foundation for the pricing of financial derivatives and has become indispensable in both financial theory and financial practice. This textbook offers a rigorous and comprehensive introduction to the mathematics of arbitrage pricing in a discrete-time, finite-state economy in which a finite number of securities are traded. In a first step, various versions of the Fundamental Theorem of Asset Pricing, i.e., characterizations of when a market does not admit arbitrage opportunities, are proved. The book then focuses on incomplete markets where the main concern is to obtain a precise description of the set of “market-consistent” prices for nontraded financial contracts, i.e. the set of prices at which such contracts could be transacted between rational agents. Both European-type and American-type contracts are considered. A distinguishing feature of this book is its emphasis on market-consistent prices and a systematic description of pricing rules, also at intermediate dates. The benefits of this approach are most evident in the treatment of American options, which is novel in terms of both the presentation and the scope, while also presenting new results. The focus on discrete-time, finite-state models makes it possible to cover all relevant topics while requiring only a moderate mathematical background on the part of the reader. The book will appeal to mathematical finance and financial economics students seeking an elementary but rigorous introduction to the subject; mathematics and physics students looking for an opportunity to get acquainted with a modern applied topic; and mathematicians, physicists and quantitatively inclined economists working or planning to work in the financial industry.
Consider a non-spanned security C_T in an incomplete market. We study the risk/return trade-offs generated if this security is sold for an arbitrage-free price 'c0' and then hedged. We consider recursive one-period optimal self-financing hedging strategies, a simple but tractable criterion. For continuous trading, diffusion processes, the one-period minimum variance portfolio is optimal. Let C_0(0) be its price. Self-financing implies that the residual risk is equal to the sum of the one-period orthogonal hedging errors, sum Y_t(0) . To compensate the residual risk, a risk premium y_t ?t is associated with every Y_t. Now let C_0(y) be the price of the hedging portfolio, and sum (Y_t(y) + y_t ?t) is the total residual risk. Although not the same, the one-period hedging errors Y_t (0) and Y_t (y) are orthogonal to the trading assets, and are perfectly correlated. This implies that the spanned option payoff does not depend on y. Let c0=C_0(y). A main result follows. Any arbitrage-free price, c0, is just the price of a hedging portfolio (such as in a complete market), C_0(0), plus a premium, c0-C_0(0). That is, C_0(0) is the price of the option's payoff which can be spanned, and c0-C_0(0) is the premium associated with the option's payoff which cannot be spanned (and yields a contingent risk premium of sum y_t ?t at maturity). We study other applications of option-pricing theory as well.
We propose a nonparametric method for estimating the pricing formula of a derivative asset using learning networks. Although not a substitute for the more traditional arbitrage-based pricing formulas, network pricing formulas may be more accurate and computationally more efficient alternatives when the underlying asset's price dynamics are unknown, or when the pricing equation associated with no-arbitrage condition cannot be solved analytically. To assess the potential value of network pricing formulas, we simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis function networks, multilayer perceptron networks, and projection pursuit. To illustrate the practical relevance of our network pricing approach, we apply it to the pricing and delta-hedging of Samp;P 500 futures options from 1987 to 1991.
The Derivatives Sourcebook is a citation study and classification system that organizes the many strands of the derivatives literature and assigns each citation to a category. Over 1800 research articles are collected and organized into a simple web-based searchable database. We have also included the 1997 Nobel lectures of Robert Merton and Myron Scholes as a backdrop to this literature.