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Recently, there has been much progress in developing Markov switching stochastic volatility (MSSV) models for financial time series. Several studies consider various MSSV specifications and document superior forecasting power for volatility compared to the popular generalized autoregressive heteroscedasticity (GARCH) models. However, their application to option pricing remains limited, partially due to the lack of convenient closed-form option pricing formulas which integrate MSSV volatility estimates. We develop such a closed-form option pricing formula and the corresponding hedging strategy for a broad class of MSSV models. We then present an example of application to two of the most popular MSSV models: Markov switching multifractal (MSM) and component-driven regime switching (CDRS) models. Our results establish that these models perform well in one-day-ahead forecasts of option prices.

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.

This research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.

A feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.

The findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.

Based on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.

The present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

In Canada, grain handling is an important agri-business that has traditionally been cooperative in nature (for example, Saskatchewan Wheat Pool). At the same time the industry is heavily regulated. There has been a dramatic change in the structure of the industry over the past 20 years and there are currently no major cooperatives present in the market. If the “yardstick effect” hypothesis of the role of cooperatives in an imperfectly competitive market is true, the disappearance of cooperatives could result in the ability of remaining firms to exercise market power over producers. To investigate the impact of changes in ownership structure in the market, we estimated two types of pricing games that might have been played between a cooperative, Saskatchewan Wheat Pool (SWP) and an investor-owned firm (IOF), Pioneer Grain (PG) in the Saskatchewan wheat-handling market over the period 1980–2004, with different assumptions about their pricing behavior imposed. We find that SWP and PG have likely been playing a Bertrand pricing game in the market over the period. We thus conclude that SWP, as the largest cooperative in the market, likely played a “yardstick effect” role in the market.

The purpose of this study is to analyze structural changes that took place in the cotton industry and develop a statistical model that reflects the current drivers of U.S. upland cotton prices. This study concludes that a structural break in the U.S. cotton industry occurred in 1999, and that world cotton supply has become an important determinant of U.S. cotton prices. The model developed here forecasts changes in U.S. cotton price based on changes in U.S. cotton supply, changes in U.S. stocks-to-use ratio (S/U), changes in China's net imports as a share of world consumption, the proportion of U.S. cotton engaged in the loan program, and changes in world supply of cotton.

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the aggregate macro behavior they co-create. The aim of this article is to provide a comprehensive literature survey on laboratory experiments on expectations in macroeconomics and finance. In particular, we discuss the extent to which expectations are rational or may be described by simple forecasting heuristics, at the individual as well as the aggregate level.

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.