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Managers often do research to help them determine the optimum price for a new product. Several different price‐points are ordinarily tested in order to determine the impact of price on sales of the product. Aside from its impact on demand, price also has been studied for its effect on consumers' perceptions of products. For example, research has indicated that people use price as a cue for evaluating the quality of a complex product such as stereophonic equipment for the home. That is, price is used in lieu of knowledge of the technical aspects of the product. Research presented in this paper reveals a yet deeper aspect of price's effect on perception. In this case, variation in price was associated with changes in the way people perceived a new product's function, perceptions that differed from the manufacturer's intended positioning for the product.

The payoffs of exotic options (e.g., up‐and‐out call options) are dependent on the time‐path of asset prices rather than the price of the asset at a fixed point in time. The authors of this article compare various models for calibrating volatility surfaces in order to price up‐and‐out call options.

The Black‐Scholes (B‐S) model in its various formulations has been the mainstay paradigm on option pricing since its basic formulation in 1973. The model has generally been proven empirically robust, despite the well documented empirical evidence of mispricing deep‐in‐the‐money, deep out‐of‐the‐money and, occasionally, at‐the‐money options with near maturities [see Galai (1983)]. Research on explaining the observed pricing anomalies has focused on the variance of the return of the underlying asset, which, in the case of the B‐S model, is assumed to remain invariant over time. The variance term is not directly observable, leading researchers to speculate that pricing discrepancies may be caused by misspecification of this variable. More specifically, interest in the volatility variable has centered about the implied standard deviation (ISD).

This study proposes and demonstrates a novel approach to analyzing customer channel preferences and willingness-to-pay (WTP) in the dual sales channel (DSC) system involving direct online channels and conventional offline retailers, and to how the pricing decisions are made under specific game competition.

Questionnaire survey based on central composite experiment design was utilized to obtain primary data. The model for customer channel preferences and WTP was then built by using multinomial logistic regression. The propensity of a customer to make purchases in either channel estimated by using the logit model was inserted in the bilevel programming model to formulate and solve for the Stackelberg competition where the conventional retailer acted as a leader.

The study found that channel prices have nonlinear impacts on WTP and channel preference. The empirical results complement the mathematical formulation well where high-order own-price and cross-price effects on channel selection are generally not analytical tractable. Under the Stackelberg competition, the traditional retailer (as the leader) still achieves higher profits than the online facility.

The proposed framework provides an empirical approach that can easily address the competition model in the sales channel when complicated own-price or cross-price effects are present.

The present work provides a novel approach to analyze customer preference and WTP of the DSC systems. This alternative method simplifies the procedure for investigating and estimating price sensitivity, especially when the online and offline prices affect customer WTP and channel preferences nonlinearly. This model is also utilized in the game competition to facilitate data-driven price decision making to better formulate and understand real-world DSC problems.

This paper examines the price setting behaviour over time and space in the Athens residential market. In periods of house price inflation asking prices are often based upon the last observed highest selling price achieved for a similar property in the same micro-location. However, in a falling market, prices may be rigid downwards and less sensitive to the most recent transaction prices, weakening spatial effects. Furthermore, the paper considers whether future price expectations affect price setting behaviour.

The paper employs a dataset of approximately 24,500 property values from 2007 until 2014 in Athens incorporating characteristics and locational variables. The authors begin by estimating a baseline hedonic price model using property characteristics, neighbourhood amenities and location effects. Following this, a spatio-temporal autoregressive (STAR) model is estimated. Running separate models, the authors account for spatial dependence from historic valuations, contemporaneous peer effects and expectations effects.

The initial STAR model shows significant spatial and temporal effects, the former remaining important in a falling market contrasting with previous literature findings. In the second STAR model, whilst past sales effects remain significant although smaller, contemporaneous and price expectations effects are also found to be significant, the latter capturing anchoring and slow adjustment heuristics in price setting behaviour.

As valuations used in the database are based upon comparable sales, then in the recessionary periods covered in the dataset, finding comparables may have become more difficult, and hence this, in turn, may have impacted on valuation accuracy.

In addition to past effects, contemporaneous transactions and expected future values need to be taken in consideration in analysing spatial interactions in housing markets. These factors will influence housing markets in different cities and countries.

The information content of property valuations should more carefully consider the relative importance of different components of asking prices.

This is the first paper to use transactions data over a period of falling house prices in Athens and to consider current and future values in addition to past values in a spatio-temporal context.

The purpose of this paper is to present a P2P network security pricing model that promotes more secure online information sharing in P2P networks through the creation of networks with increased resistance to malicious code propagation. Online information sharing is at an all‐time high partly due to the recent growth in, and use of, online peer‐to‐peer (P2P) networks.

The model integrates current research findings in incentive compatible network pricing with recent developments in complex network theory. File download prices in P2P networks are linked to network security using a graph theory measurement called the Pearson coefficient. The Pearson coefficient indicates a structural dimension of scale‐free networks (scale‐free networks like the internet) called preferential attachment. Preferential attachment refers to the network property where the probability for a node to connect to a new node is greater if the new node already has a high number of connections.

The P2P network security pricing model concept is illustrated to show how the model functions to create more secure P2P networks.

Future research in P2P network security pricing should focus on testing the model presented in this paper by numerical experiments and simulation including the tracking of malicious code propagation on networks grown under the pricing model.

The P2P network security pricing model demonstrated here is a different approach to network security that has a strong potential to impact on the future security of P2P and other computer based networks.

The purpose of this paper is to lay a current, research-based foundation for investigation of the concept of innovative price models and its connection to business models.

The design is composed of a structured literature review of articles on price models published in 22 journals during 42 years. This then serves as a base for a subsequent conceptual discussion about the foundation of innovative price models.

The literature review yields only very few results that are loosely scattered across various areas and mostly without any kind of deeper exploration of the concept of price models. The paper therefore goes on to conceptually explore some fundamental conditions that might influence or even determine price models. The final outcome of this exploration is the relation, intention, technology and environment (RITE) framework that is a meta-model for conceptualising innovative price models.

The literature review could include additional journals and areas, and empirical testing of the RITE framework as yet has been limited.

The RITE framework can be used by practitioners as a tool for investigating the potential and usefulness of developing the capability to handle innovative price models.

The RITE framework provides fundamental conditions, which influence, or even determine, how innovative price models are developed and applied.

The purpose of this paper is to examine the price discovery role of the Korea Composite Stock Price Index 200 (KOSPI 200) stock index options market in contrast to other developed options markets.

The price discovery roles of the stock and options markets using the error‐correction model derived from the co‐integration relationship are examined. Various analyses are conducted. First, Heston's stochastic volatility option pricing model is employed to confirm its usefulness, and compare the results with the Black and Scholes (BS) model. Second, whether the out of the money (OTM) options purchased by individual investors have a stronger price discovery role than options with other moneyness is examined. Finally, whether options have a stronger price discovery role in bullish or bearish markets than in normal markets is tested.

It is found that stock index prices lead implied index prices estimated from option prices using both BS and Heston models. In regards to the OTM options, the lead‐effect of real stock index to implied index prices holds. Also it is shown that there is a weak rise in the lead effect of the options to the stock index, but the lead effect of stock index market rules over that of the options market.

The paper examines the price discovery role of the KOSPI 200 stock index options market in contrast to other developed options markets and the results indicate that the consensus on the Korean financial markets may be incorrect.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

The purpose of this paper is to demonstrate how crop price insurance premium can be calculated using an option pricing model and how insurers can transfer underwriting risks in the futures market.

Based on data from spot and futures market in China, this paper develops an improved B-S model for the calculation of crop price insurance premium and tests the possibility of hedging underwriting risks by insurance firms in the futures market.

The authors find that spot price of crops in China can be estimated with agricultural commodity futures prices, and can be taken as the insured price for crop price insurance. The authors also find that improved B-S model yields better estimation of crop price insurance premium than traditional B-S model when spot price does not follow geometric Brownian motion. Finally, the authors find that hedging can be one good alternative for insurance firms to manage underwriting risks.

This paper develops an improved B-S model that is data-driven in nature. Insured price of the crop price insurance, or the exercise price used in the B-S model, is estimated from a co-integration model built on spot and futures market price series. Meanwhile, distributional patterns of spot price series, one important factor determining the applicability of B-S model, is factored into the improved B-S model so that the latter is more robust and friendly to data with varied distributions. This paper also verifies the possibility of hedging of underwriting risks by insurance firms in the futures market.