Narrow Results

The problem of determining the European-style option price in incomplete markets is examined within the framework of stochastic optimization. An analytic method based on the stochastic optimization is developed that gives the general formalism for determining the option price and the optimal trading strategy (optimal feedback control) that reduces the total risk inherent in writing the option. The cases involving transaction costs, the stochastic volatility with uncertainty, stochastic adaptive process, and forecasting process are considered. A software package for the option pricing for incomplete markets is developed and the results of numerical simulations are presented.

We consider a general discrete-time dynamic financial market with three assets: a riskless bond, a security and a derivative. The market is incomplete (a priori) and at equilibrium. We assume also that the agents of the economy have short-sales constraints on the stock and that the payoff at the expiry of the derivative asset is a monotone function of the underlying security price. The derivative price process is not identified ex ante. This leads the agents to act as if there were no market for this asset at the intermediary dates. Using some nice properties of the pricing probabilities, which are admissible at the equilibrium, we prove that it suffices to consider the subset of the risk-neutral probabilities that overestimate the low values of the security and underestimate its high values with respect to the true probability. This approach greatly reduces the interval of admissible prices for the derivative asset with respect to no-arbitrage, as showed numerically.

An informationally efficient price keeps investors as a group in the state of maximum uncertainty about the next price change. The Entropy Pricing Theory (EPT) captures this intuition and suggests that, in informationally efficient markets, perfectly uncertain market beliefs must prevail. When the entropy functional is used to index collective market uncertainty, then the entropy-maximizing consensus beliefs must prevail. The EPT resolves the ambiguity of arbitrage-free valuation in incomplete markets. The EPT produces a new bond option model that is similar to Black–Scholes' with the lognormal distribution replaced by a beta distribution. Unlike alternative models, the beta model is valid for arbitrary term structure dynamics and for arbitrary credit risk of the underlying bonds. Option replication and hedging under the beta model accounts for random changes in the underlying bond price, price volatility and short-term interest rates.

In this paper, we propose a numerical option pricing method based on an arbitrarily given stock distribution. We first formulate a European call option pricing problem as an optimal hedging problem by using a lattice based incomplete market model. A dynamic programming technique is then applied to solve the mean square optimal hedging problem for the discrete time multi-period case by assigning suitable probabilities on the lattice, where the underlying stock price distribution is derived directly from empirical stock price data which may possess "heavy tails". We show that these probabilities are obtained from a network flow optimization which can be solved efficiently by quadratic programming. A computational complexity analysis demonstrates that the number of iterations for dynamic programming and the number of parameters in the network flow optimization are both of square order with respect to the number of periods. Numerical experiments illustrate that our methodology generates the implied volatility smile.

In this paper we analyze the mean-variance hedging approach in an incomplete market under the assumption of additional market information, which is represented by a given, finite set of observed prices of non-attainable contingent claims. Due to no-arbitrage arguments, our set of investment opportunities increases and the set of possible equivalent martingale measures shrinks. Therefore, we obtain a modified mean-variance hedging problem, which takes into account the observed additional market information. Solving this we obtain an explicit description of the optimal hedging strategy and an admissible, constrained variance-optimal signed martingale measure, that generates both the approximation price and the observed option prices.

We consider a problem of minimization of a hedging error, measured by a positive convex random function, in an incomplete financial market model, where the dynamics of asset prices is given by an R^d-valued continuous semimartingale. Under some regularity assumptions we derive a backward stochastic PDE for the value function of the problem and show that the strategy is optimal if and only if the corresponding wealth process satisfies a certain forward-SDE. As an example the case of mean-variance hedging is considered.

We model spot prices in energy markets with exponential non-Gaussian Ornstein–Uhlenbeck processes. We generalize the classical geometric Brownian motion and Schwartz' mean-reversion model by introducing Lévy processes as the driving noise rather than Brownian motion. Instead of modelling the spot price dynamics as the solution of a stochastic differential equation with jumps, it is advantageous from a statistical point of view to model the price process directly. Imposing the normal inverse Gaussian distribution as the statistical model for the Lévy increments, we obtain a superior fit compared to the Gaussian model when applied to spot price data from the oil and gas markets. We also discuss the problem of pricing forwards and options and outline how to find the market price of risk in an incomplete market.

In the incomplete market setting, we define a generalized Kullback-Leibler relative entropy in terms of an investor's expected utility. We motivate, from an economic point of view, this quantity — the relative U-entropy. Relative U-entropy measures the discrepancy from a set of pricing measures to a single probability measure. We show that the relative U-entropy shares a number of important properties with the usual Kullback-Leibler relative entropy, and establish the link between this quantity and the pricing measure corresponding to the least favorable market completion. We also describe an economic performance measure for probabilistic models that may be used by an investor in an incomplete market setting. We then introduce a statistical learning paradigm suitable for investors who learn models and base investment decisions, in an incomplete market, on these models.

A canonical problem in real option pricing, as described in the classic text of Dixit and Pindyck [2], is to determine the optimal time to invest at a fixed cost, to receive in return a stochastic cashflow. In this paper we are interested in this problem in an incomplete market where the cashflow is not spanned by the traded assets. We follow the formulation in Miao and Wang [21]; our contribution is to show that significant progress can be made in solving the Hamilton-Jacobi-Bellman equation and that the optimal exercise threshold can be characterized quite precisely.

This paper is a contribution to the pricing and hedging of options in a market where the volatility is stochastic. The new concept of relative indifference pricing is further developed. This relative price is the price at which an option trader is indifferent to trade in an additional option, given that he is currently holding and dynamically hedging a portfolio of options. We find that the appropriate volatility risk premium depends on the trader's risk aversion coefficient and his portfolio position before selling or buying the additional option. We suggest two asymptotic expansions which relate the volatility risk premium to the Vega of the option portfolio. This approach provides a tool for traders to (i) integrate option pricing with risk management and (ii) quote competitive prices that depend on their aggregate risk exposure.

We propose, in this paper, a new valuation method for contingent claims, which approximates to the exponential utility indifference valuation. In particular, we treat both ask and bid valuations. In the definition of the exponential utility indifference valuation, we require strong integrability for the underlying contingent claim. The new valuation in this paper succeeds in reducing it by using a kind of power functions instead of the exponential function. Furthermore, we shall investigate some basic properties and an asymptotic behavior of the new valuation.

In the present paper we give some preliminary results for option pricing and hedging in the framework of the Bates model based on quadratic risk minimization. We provide an explicit expression of the mean-variance hedging strategy in the martingale case and study the Minimal Martingale measure in the general case.

We hedge options on electricity spot prices by cross hedging, i.e., by using another financial asset. We calculate hedging strategies by quadratic minimization and local risk minimization. In our model of energy markets, we have done a deep study of no arbitrage and of the existence of martingale measures with square integrable density. Then we have established tools for efficient hedges. Nevertheless, we have clearly proved possible limitations of the expiry of options with quadratic criteria.

In this paper, I study the equilibrium implications when some investors in the economy overweight a subset of stocks within their portfolio. I find that the excess returns for the overweighted stocks are lower, all else being equal. This has strong testable implications for stock returns. In the special case of logarithmic preferences, the riskfree rate increases and the market price of risk for the overweighted stock decreases, which create extra incentive for unconstrained agents to exit the stock market and hold bonds, hence clearing the market. The changes of stocks' volatilities are ambiguous. Finally, I provide an accurate quantification for agents' welfare. I also discuss the implications of my model in the context of defined contribution pension plans where workers hold large shares of their employer.

We study the problem of determination of asset prices in an incomplete market proposing three different but related scenarios, based on utility pricing. One scenario uses a market game approach whereas the other two are based on risk sharing or regret minimizing considerations. Dynamical schemes modeling the convergence of the buyer and seller prices to a unique price are proposed. The case of exponential utilities is treated in detail, in the simplest possible example of an incomplete market, the trinomial model.

We introduce the notion of κ-entropy (κ ∈ ℝ, |κ| ≤ 1), starting from Kaniadakis' (2001, 2002, 2005) one-parameter deformation of the ordinary exponential function. The κ-entropy is in duality with a new class of utility functions which are close to the exponential utility functions, for small values of wealth, and to the power law utility functions, for large values of wealth. We give conditions on the existence and on the equivalence to the basic measure of the minimal κ-entropy martingale measure. Moreover, we provide characterizations of its density as a κ-exponential function. We show that the minimal κ-entropy martingale measure is closely related to both the standard entropy martingale measure and the well known q-optimal martingale measures. We finally establish the convergence of the minimal κ-entropy martingale measure to the minimal entropy martingale measure as κ tends to 0.

CDS (credit default swap) contracts that were initiated some time ago frequently have spreads and/or maturities that are not available on the current market of CDSs, and are thus illiquid. This article introduces an incomplete-market approach to valuing illiquid CDSs that, in contrast to the risk-neutral approach of current market practice, allows a dealer who buys an illiquid CDS from an investor to determine ask and bid prices (which differ) in such a way as to guarantee a minimum positive expected rate of return on the deal. An alternative procedure, which replaces the expected rate of return by an analogue of the Sharpe ratio, is also discussed. The approach to pricing just described belongs to the good-deal category of approaches, since the dealer decides what it would take to make an appropriate expected rate of return, and sets the bid and ask prices accordingly. A number of different hedges are discussed and compared within the general framework developed in the article. The approach is implemented numerically, and example plots of important quantities are given. The paper also develops a useful result in linear programming theory in the case that the cost vector is random.

We discuss utility based pricing and hedging of jump diffusion processes with emphasis on the practical applicability of the framework. We point out two difficulties that seem to limit this applicability, namely drift dependence and essential risk aversion independence. We suggest to solve these by a re-interpretation of the framework. This leads to the notion of an implied drift. We also present a heuristic derivation of the marginal indifference price and the marginal optimal hedge that might be useful in numerical computations.

We consider the strategic interaction between two firms competing for the opportunity to invest in a project with uncertain future values. Starting in complete markets, we provide a rigorous characterization of the strategies followed by each firm in continuous time in the context of a timing/coordination game. In particular, the roles of leader and follower emerge from the resulting symmetric, Markov, sub-game perfect equilibrium. Comparing the expected value obtained by each firm in this case with that obtained when the roles of leader and follower are predetermined, we are able to calculate the amount of money that a firm would be willing to spend in advance (either by paying a license or acquiring market power) to have the right to be the leader in a subsequent game — what we call the priority option. We extend these results to incomplete markets by using utility-indifference arguments.