The rest of the paper is organized as follows: Section II presents relevant existing work related to business models in a multi-operator environment. Section III describes the proposed pricing scenarios for the transaction cost in a sharing environment, andthedecision algorithm used for selectionis presented in section IV. The simulated business models are depicted in section V. Section VI shows the simulation results for the efficiency of the selection algorithm, and elaborates the results for the pricing scenarios profitability, in each business model. Finally, in section VII, a conclusion is made for the best pricing scenario in function of capacity and service price settings.
G. BUTTAZZO AND G. CARLIER
Abstract. We consider an optimization problem in a given region Q where an agent has to decide the price p(x) of a product for every x ∈ Q. The customers know the pricing pattern p and may shop at any place y, paying the cost p(y) and additionally a transportation cost c(x, y) for a given trans- portation cost function c. We will study two models: the first one where the agent operates everywhere on Q and a second one where the agent op- erates only in a subregion. For both models we discuss the mathematical framework and we obtain an existence result for a pricing strategy which maximizes the total profit of the agent. We also present some particular cases where more detailed computations can be made, as the case of con- cave costs, the case of quadratic cost, and the onedimensional case. Finally we discuss possible extensions and developments, as for instance the case of Nash equilibria when more agents operate on the same market.
† Information Technology and Electrical Engineering Department, ETH Zurich, Switzerland
Abstract—We propose a pricing model to study the economic incentives for caching and sharing content in the interconnection of Information-Centric Networks (ICNs). While collaborative caching in ICNs is a key feature to success in improving network performance and reducing delivery costs in content distribution, the current pricingstrategies in the Internet are not incentive- compatible with ICN interconnection. In this paper, we clarify the issue by considering the existing value and money flows in today’s Content Delivery Networks (CDNs) and studying their possible evolution in a future ICN interconnection scenario. We model and analyze the interactions in price and caching investments among transit ISPs, access ISPs and content providers in ICN interconnection. Under the assumptions of our economic model, it is proven that ICN interconnection is feasible from an economic point of view and a stable state can be reached. Our numerical results show the case where an access ISP is profitable from caching investment. Interestingly, we observe that in the market- place described by our model there are more opportunities for competition in ICN interconnection thanks to caching.
It is worth to note that our new definition does not answer all criticisms. Indeed, for the Jouini’s example, the marginal pricing rule with the intermediate normal cone is again the whole positive orthant. But, we illustrate another open question with our example. Indeed, Cornet-Czarnecki (2001) consider external smooth approximation of the sets. It is also possible for the production sets to consider internal smooth approximation since they are epi-lipschitz. We show that the intermediate normal cone is not the same when we consider external or internal approximations. At this stage, we do not know what is the right approximation process leading to the smallest possible normal cone.
In addition there are many ways to tackle the pricing of options via nonparametric methods. Moreover, there are many dierent types of option contracts, some of which require discussion of special features like early exercise decisions in the case of American options. Given the large number of possibilities and the multitude of methods we have to be selective in our survey of methods and applications. The literature is also rapidly growing. Recent papers include At-Sahalia (1993, 1996), At- Sahalia and Lo (1995), Baum and Barkoulas (1996), Bossaerts, Hafner and Hardle (1995), Broadie, Detemple, Ghysels and Torres (1995,1996), Elsheimer et al. (1995), Ghysels and Ng (1996), Gourieroux, Monfort and Tenreiro (1994, 1995), Gourieroux and Scaillet (1995), Hutchinson, Lo and Poggio (1994), Stutzer (1995), among others. To focus the survey we will restrict our attention only to options on equity.
Many linkages have been established between linear factor models in Arbitrage Pricing Theory (APT) and higher-order moments, as additional factors from CAPM can capture some of these moments. CAPM assumes that investors only care about means and variances, since systematic risks in that model are measured as contributions to the variance of the market returns. But we know from Scott & Horvath ( 1980 ) that preferences for higher-order moments also matter, particularly when the distribution of the expected utility is not fully determined by means and variances, when the returns have asymmetric distributions, or in the presence of tail risks. Harvey & Siddique ( 2000 ) explain in this vein how portfolios’ skewness influence asset returns, while Chung, Johnson & Schill ( 2006 ) (hereafter CJS) show that the size and the value factors (SMB and HML) from the Fama-French’s three-factor model become insignificant when systematic comoments of orders 3-10 are added in the model
By matching a SemiNonParametric (SNP) score generator, EMM aims at correcting for this e±ciency loss. The EMM procedure allows estimating the model parameters under both objective and risk-neutral probability measures if one uses implied volatilites and the underlying asset data jointly. Time series of the underlying asset provide estimators under the objective probability measure while risk-neutral parameters can be retrieved from options. Chernov and Ghysels (2000) adopt the Heston model, which has a closed-form option pricing formula, and compare univariate and multivariate models in terms of pricing and hedging performance. An extension of the SNP/EMM methodology introduced in Gallant and Tauchen (1998) which allows one to ¯lter spot volatilities via reprojection, i.e. compute the expected value of the latent volatility process using a SNP density conditioned on the observable processes such as returns and/or options data. The results in Chernov and Ghysels (2000) show that the univariate approach only involving options by and large dominates. A by-product of this ¯nding is that they uncover a remarkably simple volatility extraction ¯lter based on a polynomial lag structure of implied volatilities. The bivariate approach appears useful when the information from the cash market provides support via the conditional kurtosis to price options. This is the case for some long-term options. Another solution to the e±ciency problem may be provided by Markov Chain Monte Carlo techniques as described by Johannes and Polson in this handbook.
The specification of commodity modeling is often reduced to an appro- priate representation of convenience yield, intrinsic seasonality and mean reversion of commodity price. As a matter of fact, convenience yield can be extracted from forward strip curve and then be added as a drift term into pricing models such as Black Scholes model, local volatility model and stochastic volatility model. Besides those common models, some specific commodity models specially emphasize on the importance of convenience yield, seasonality or mean reversion feature. By giving the stochasticity to convenience yield, Gibson Schwartz model interprets the term structure of convenience yield directly in its model parameters, which makes the model extremely popular amongst researchers and market practitioners in com- modity pricing. Gabillon model, in the other hand, focuses on the feature of seasonality and mean reversion, adding a stochastic long term price to correlate spot price.
Models of heterogeneous beliefs can generate rich implications for trading and asset pricing (see Suleyman Basak 2005 for a recent survey). When studying such models, aggregation often leads to difficulty in computing equilibrium outcomes. In this paper, we introduce a flex- ible framework to model heterogeneous beliefs in the economy, which we refer to as “affine’’ disagreement about fundamentals. Affine pro- cesses (see Darrel Duffie, Jun Pan, and Kenneth Singleton 2000 ) are appealing as they provide a large degree of flexibility in modeling the conditional means, volatilities, and jumps for various quantities of interest while remaining analytically tractable. Our affine heterogeneous beliefs framework allows further for stochastic disagreement among agents about growth rates, volatility dynamics, as well as the likelihood of jumps and the distribution of jump sizes.
3.1 Experimental design
A common research method in studies of fairness is to ask participants of experiments to imagine themselves in hypothetical situations. Five percent of the November 2009 flow of passengers (n=250) were conveniently chosen at the queues of outbound flights from Tahiti International Airport (PPT) 1 . A scenario concerning an airline which makes a pricing decision that questions the fairness of the transaction is offered to each participant. Price discriminations are manipulated within scenarios in a between-subject experiment 2 . The manipulation of a competitor‟s price along with a price increase decided by the airline leads to two situations expected to trigger fairness perceptions. First, the participant confronts a price increase during the buying process (manipulation to USD 2,000). Second, it provides a relative position, as the participant encounters a price increase higher or lower than what he would afford on the market (manipulation to USD 1,500 or 2,500). Having read a randomly assigned scenario, participants answered several questions about the fairness of their situation (Table 2).
Models for pricing and hedging defaultable claim have generated a large debates by academics and practitioners during the last subprime crisis. The challenge is to modelize the expected losses of derivatives portfolio by taking account the counterparties defaults since they have been affected by the crisis and their agreement on the derivatives contracts can potentially vanish. In the literature, models for pricing defaultable securities have been pioneered by Merton [ 27 ]. His approach consists of explicitly linking the risk of firm’s default and firm’s value. Although this model is a good issue to understand the default risk, it is less useful in practical applications since it is too difficult to capture the dynamics of the firm’s value which depends of many macroeconomics factors. In response of these difficulties, Duffie and Singleton [ 9 ] introduced the reduced form modeling which has been followed by Madan and Unal [ 26 ], Jeanblanc and Rutkowski [ 17 ] and others. In
4 Application to Option Pricing
4.1 European Options Data
The model described in the previous section was used for the prediction of the prices of European call options on the S&P 500 index. A European call option entitles the buyer of the option the right to buy a specific stock at a specific moment in the future at a smaller price equal to the strike price K agreed upon when the option was bought. In other terms, a person can buy at time t at the price p t the right, but not the obligation, to buy a stock at time t + τ (where τ is called the maturity) at a price K. If at time t + τ the actual stock price s t+τ > K, the option buyer can exercise the option to pay K rather than s t+τ , thus making a profit of s t+τ − K. On the other hand, if s t+τ < K, the buyer would not exercise the option, and would make no profit (but in both cases the buyer would have initially made a “loss” of p t for buying the option). The option price p t depends on several factors including the current stock price s t , the maturity τ , the strike price K, the underlying volatility (variability) of the price sequence, etc.
comme étant backward in time.
Les conditions aux limites sont les valeurs de V aux bornes du domaine étudié. Dans le cas du pricing d’une option européenne, le domaine est unidimensionnel et consiste dans les différentes valeurs possibles de S. La borne inférieur du domaine est S = 0 et à cet endroit il peut être supposé que V = 0. La borne supérieure est plus problématique car S n’est pas borné à droite. Il sera vu plus tard comment s’affranchir de cette difficulté.
methodology was being tested during Zara’s Winter Clearance Season.
On her way to the meeting, the Country Manager kept thinking about the Clearance Group in question and what had happened during the previous week’s meeting. She had followed the regular (legacy) procedure and determined that the Price Category in question needed to be further discount as their days-worth of inventory levels were clearly above the country’s average. However, when the MIT2 model’s pricing suggestion was reviewed, she was surprised to find that the model suggested that the Price Cluster’s current prices be maintained. With some convincing from the Pricing Team, she had reluctantly accepted the model’s suggestion, mostly for the sake of allowing the ongoing experiment to continue. Nevertheless, throughout the last week, sales for that Category continued to deteriorate and the days-worth of inventory for them surpassed the Country’s average values by a long shot: this week the prices had to be discounted by even more than what she had suggested on the previous week.
Av. Bernard Hirsch – 95021 Cergy-Pontoise – France
Current version : January 2003
This article is an empirical study dedicated to the GARCH Option pricing model of Duan (1995) applied to the FTSE 100 European style options for various maturities. The beauty of this model is to have used the standard GARCH theory in an option perspective and also it is its flexibility to adapt to different rich GARCH specifications. We analyze the valididy of the model given its ability to price one-day ahead out-of-sample call options and also its ability to capture the empirical dynamic of the volatility skew.