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In this chapter, we aim to analyze the housing market development in Czechia, in particular the development of housing prices over the last 25 years. We quantify and discuss three distinct periods of excessive growth of regional Czech housing prices, identified through the formation of large positive GAPs – (1) before the entrance of Czechia to the European Union (EU), (2) at the onset of the Global Financial Crisis GFC, (3) in 2021. In all these periods, we identify significant differences among regions. We find that GAPs above 15% may be considered an indication of unsustainable long-term housing price growth that will be followed by a correction.

We then employ fixed effect panel data model to determine the drivers of flat and house prices in 14 Czech regions. Our results show that wage growth, migration and crime rate are significant factors affecting the prices of both flats and houses. Nevertheless, the impact of GDP per capita and job market indicators differs between flats and houses. Moreover, we find that higher migration into the region increases the difference between the prices of houses and flats, while increasing GDP per capita growth and crime rate mitigate this difference significantly.

The paper constructs an annual price series for English net agricultural output in the years 1209–1914 using 26 component series: wheat, barley, oats, rye, peas, beans, potatoes, hops, straw, mustard seed, saffron, hay, beef, mutton, pork, bacon, tallow, eggs, milk, cheese, butter, wool, firewood, timber, cider, and honey. I also construct sub-series for arable, pasture and wood products. The main innovation is in using a consistent method to form series from existing published sources. But fresh archival data is also incorporated. The implications of the movements of these series for agrarian history are explored.

We study how price dispersion varies with product characteristics at a popular online price comparison site – Shopper.com. Our primary finding suggests that price dispersion in online markets varies with product characteristics and firm behavior. We also find evidence that the level of dispersion varies with the percent of firms listing price information in multiple categories. When the percent of firms listing prices in multiple categories is relatively high (low), price dispersion is low (high).

This chapter examines the price impact of large trades in futures markets across 14 stock index futures contracts in 11 different international markets. On the balance, we find that part of the initial price effect of futures trades is temporary. These initial price effects are partially reversed, implying that they incur a liquidity premium; though there is some variation in this finding across markets. We also find strong evidence that large buyer- and seller-initiated trades have positive and negative permanent effects on prices, implying they convey information. We conclude, similar to research based on equities markets, that traders in futures markets are informed.

If the only difference between cities lies in their initial housing prices, the initially lower-price cities should eventually catch up with the initially higher-price ones, i.e., “absolute convergence.” Alternatively, if the major determinants of housing prices are city-specific, cities will converge to parallel growth paths of housing prices, i.e., “conditional convergence.” This study tests for the existence of absolute and conditional convergence in house prices among cities in China. The strong evidence for conditional convergence suggests that each city possesses its own steady-state housing price to which it is converging, which depends on the city's own socio-economic characteristics. In other words, differences in these socio-economic characteristics among cities can create permanent differences in housing price among them. The differences in steady-states house price across cities reflect differences in the level of socio-economic development among them. The findings inform the kinds of interventions and resources that are most likely to be effective in reducing income disparity.

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.

Purpose – To examine how drug prices for specific diseases vary across payers in the United States and how insurer and patient out-of-pocket (OOP) costs vary by payer type.

Methodology – This study uses data from the Medical Expenditure Panel Survey (MEPS) from 1996 to 2006. We estimate multivariate price regressions for four major drug product classes (antihypertensive, antidepressant, antiasthma drugs, and non-steroidal anti-inflammatory drugs (NSAIDs)). Separate models are estimated for brand and generic drugs within each of these drug product classes. In addition to estimating overall transaction price equations for brands and generics, the study estimates patient OOP payments and insurer payments for drugs.

Findings – We find relatively modest differences among payers in terms of total prices (e.g., insurer plus OOP). The main difference is in terms of how prices were shared between insurers and patients. Medicaid paid significantly more than other payers for each drug class, while Medicaid beneficiaries paid significantly less.

Research implications – Our results shed light on how drug prices vary by different payers and how drug prices are shared by third party payers and patients. The relatively modest differences in total drug prices across payer type suggest that these payers do not differ greatly in terms of their ability to negotiate price concessions from their suppliers. Instead, larger differences emerge in terms of how total costs are shared among the payer and their patients. Understanding the reasons for these variations, and their implications for health outcomes, are important directions for further research.

The subject for study at present is the manner in which the prices of consumption goods and services are fixed under competition. The theory of price under monopoly is treated as a separate topic (Ely, Chapter XIII); and the theory of the prices of productive goods, that is, the theory of wages, rent, and interest, which are the prices of labor and the use of land and capital, are also treated separately under the head of Distribution. (See Ely, pp. 177–178, and Chapters XX–XXVII.)

Many companies have made progress in pricing – instituting pricing rules, creating pricing organizations, and getting high-level visibility on pricing performance. Few, however, have truly turned pricing into a competitive advantage. Unfortunately, the growing complexity of pricing demands that companies take their pricing capabilities to the next level of performance. This chapter outlines some of the ways that companies can take their pricing capabilities to a higher level of performance. Managers should look at these ideas and identify one or two areas where they will take their capabilities not just to good but to a level of world class. Those that do will increasingly be able to outperform their competitors and drive significant value to the company's bottom line.