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This paper studies the empirical application of an asset pricing model derived from the irrational individual behavior of loss aversion. Previous research using loss aversion asset pricing finds conclusive evidence that estimations match market equity premium and volatility using simulation data. We find that within its empirical application, the estimated errors are comparable to errors estimated from the capital asset pricing model. This study of the correlations between rational and irrational asset pricing model from the empirical results finds validity for both estimated values. Finally, we see the importance of cultures, economic development and financial development on asset pricing through an empirical examination of five pacific-basin countries in the estimation of asset pricing models.

Digitalization and marketing technologies have made it possible to overcome some barriers to pricing – a multidisciplinary field between marketing, finance and IT – and have set the stage for a paradigm shift in the pricing profession. Value creation, the pricing process, and price communication have been transformed by innovative business models and advanced algorithmic and human–machine solutions. This chapter synthesizes the literature to date and provides a comprehensive framework for an all-encompassing 360° pricing approach that broadens the understanding of pricing in the context of digital business across all steps of the price management process. Starting from product attributes and motivational beliefs in consumers' value assessment and adoption of (technological or digital) products or services, new business models and pricing models emerge in the digital economy, human–machine solutions for price implementation and repricing are increasingly applied, and price search and communication take place through a variety of digital communication channels. Each stage of this framework discusses concrete examples, highlighting the freemium strategy, the subscription model, price tracking and repricing tools, and digital price information channels such as e-commerce, marketplace, or price comparison platforms. The implications for price management in a digital, technology-driven landscape are discussed from the executive level to the analyst level.

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.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

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.

This paper includes an examination of two key issues on price decisions: (1) how should price decisions be made (the strategic and normative issue) within market contexts, and (2) how are price decisions actually made (the execution and implementation of price decisions). The paper closes with some observations useful for applied research and strategies for making effective pricing decisions. The propositions and literature review show that one pricing strategy does not fit a brand in all market contexts that brand executives experience annually in managing brands. Setting specific price points requires continuing deliberate management responses to dynamic market contexts. This paper provides useful sense-making conjunctive steps to accomplish such deliberate thinking effectively relevant for different market contexts.

The pricing kernel puzzle of Jackwerth (2000) concerns the fact that the empirical pricing kernel implied in S&P 500 index options and index returns is not monotonically decreasing in wealth as standard economic theory would suggest. Thus, those options are currently priced in a way such that any risk-averse investor would increase his/her utility by trading in them. We provide a representative agent model where volatility is a function of a second momentum state variable. This model is capable of generating the empirical patterns in the pricing kernel, albeit only for parameter constellations that are not typically observed in the real world.