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The fashion market today is characterized by rapid change. Demands fluctuate over time and customer taste is rarely stable. As Christopher, Lowson and Peck (2004) mentioned, today's fashion market is ‘chaotic’.

With correspondence to the recent ever changing market conditions, it is of growing interest to investigate the ways that sellers should keep up with the market fluctuations. By formulating the market fluctuation as a stochastic process and making use of the optimal stopping theory, the present study examines the ways that a fashion retailer should sustain its brand value in a fluctuating market.

The analysis reveals that a fashion retailer with brand value should introduce new items frequently if (1) the items of the fashion retailer are robust to his/her new items or vulnerable to imitations by other fashion retailers or (2) the market as a whole is less uncertain.

This paper aims to examine the performance of Islamic and conventional stocks listed at the Pakistan Stock Exchange by using both parametric and non-parametric approaches. The motivation is to do risk-return analysis of Islamic stock prices and conventional stock prices.

It uses various measures of performance, e.g. Sharpe ratio, Treynor ratio, Jensen's alpha, beta, generalized auto-regressive conditional heteroskedasticity and stochastic dominance. Using the Karachi Meezan Index-30 (KMI-30) and the Karachi Stock Exchange Index-30 (KSE-30) as proxies for Islamic and conventional stock prices, respectively, it examines the performance of Islamic and conventional stocks. The daily data of KMI-30 and KSE-30, covering period from June 9, 2009 to June 20, 2020 are used.

The results show that the overall KMI-30 outperforms the KSE-30. The returns of the KMI-30 are greater than the KSE-30. However, the risk and volatility of the KMI-30 and KSE-30 are similar. Further, the KMI-30 has higher excess returns per unit of total risk than the KSE-30. But both indexes have similar excess returns per unit of systematic risk. Moreover, the KMI-30 returns have stochastically dominance over the KSE-30 returns. These results reveal that the Islamic index performs better than the conventional index.

The findings provide several practical implications in financial and investment decisions making by investors, managers and policymakers such as strategies for asset allocation and investment. Further, in risk management, it provides guidance for allocating portfolios and managing risk. The investment in Islamic stocks may mitigate potential risk within asset portfolios.

This research is unique in its approach to the analysis of the performance comparison of conventional and Islamic stock by using comprehensive parametric and non-parametric estimation techniques. Such research has not been undertaken in the Pakistan's equity market since.

The garbage can model showed that what appears to be irrational and unpredictable choices can be explained by processes that regulate attention allocation and the availability of choice alternatives. Because attention to alternatives fluctuates, the model generates context-dependent choices: evaluations of alternatives depend on the mix of other alternatives considered. I re-examine the mechanisms by which fluctuating attention can cause context-dependent choices. Using insights from behavioral decision theory I demonstrate how adding fluctuating attention to a well-known model of organizational decision making generates context-dependent choices of a kind that could not be explained by a maximizing process.

The past century and a quarter can be divided into three successive eras for homeownership policy characterization. For the first four decades, the federal government pursued a laissez-faire policy that left housing issues to the individual states and private markets. For the next six decades, the federal government implemented a policy created as part of the Roosevelt New Deal program. Finally, the Clinton administration discarded the New Deal policy in favor of a more aggressive policy that has continued to the present day. The purpose of this study is to compare the performance of the respective policies.

The study introduces two metrics. The first metric, based on government homeownership rate data, enables comparison of the laissez-faire and New Deal policies. The second metric, based on financial frictions in the mortgage market, enables comparison of the New Deal and Clinton policies.

Analysis based on the first metric suggests the New Deal policy was successful in meeting its macroeconomic objectives and was more effective overall than the laissez-faire policy. Analysis based on the second metric suggests the New Deal policy was also more successful in both respects than the Clinton policy.

The findings suggest that the Clinton homeownership policy was the primary driver behind the recent US housing crisis and that vulnerability in the secondary mortgage market created by the Clinton policy represents systemic housing market risk.

The study introduces simple analytical tools to address problems related to systemic risk in the US housing and housing finance markets due to homeownership policy.

This study aims to examine human-made oil–gas disasters to illustrate how a prescriptive model could be developed. Resilience to human-made disasters, such as oil or gas spills, can be improved by using prescriptive models developed by analyzing past behavior. This type of study is useful for urban planning and monitoring, as there is a higher probability of human triggered disasters in densely populated areas.

This study examined 10 years of more than 1,000 oil–gas disasters that were caused by humans in the upstate New York area to illustrate how a prescriptive model could be developed.

A statistically significant predictive model was developed that indicated humans in certain industry categories were approximately six times more likely to have an oil–gas accident resulting in environmental pollution.

A prescriptive environmental protection model based on human accident behavior would generalize to all levels of government for policy planning, and it would be relevant to environmental protection groups in any region with a large population of humans using oil and gas (that covers most countries on earth).

The empirical risk management literature was reviewed to identify factors related to environmental accident prediction with the goal of developing an explanatory model that would fit the oil–gas human accident data.

The purpose of this paper is to explore the evolvement of investors’ behavior in stock market dynamically on the basis of non-cooperative strategy applied by investors in complex networks.

Using modeling and simulation research method, this study designs and conducts a mathematical modeling and its simulation experiment of financial market behavior according to research’s basic norms of complex system theory and methods. Thus the authors acquire needed and credible experimental data.

The conclusions drawn in this paper are as follows. The dynamical evolution of investors’ trading behavior is not only affected by the stock market network structure, but also by the risk dominance degree of certain behavior. The dynamics equilibrium of trading behavior’s evolvement is directly influenced by the risk dominance degree of certain behavior, connectivity degree and the heterogeneity of the stock market networks.

This paper focuses on the dynamical evolvement of investors’ behavior on the basis of the hypothesis that common investors prefer to mimic their network neighbors’ behavior through different analysis by the strategy of anti-coordination game in complex network. While the investors’ preference and the beliefs among them are not easy to quantify, that is deterministic or stochastic as the environment changes, and is heterogeneous definitely. Thus, these limitations should be broken through in the future research.

This paper aims to address the dynamical evolvement of investors’ behavior in stock market networks on the principle of non-cooperative represented by anti-coordination game in networks for the first time, considering that investors prefer to mimic their network neighbors’ behavior through different analysis by the strategy of differential choosing in every time step. The methodology designed and used in this study is a pioneering and exploratory experiment.

The purpose of this paper is to develop a dynamic, stochastic, mechanistic simulation model of a dairy business to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting framework. A primary objective was to establish a flexible, user‐friendly, farm‐specific, decision‐making tool for dairy producers or their advisers and technology manufacturers.

The basic deterministic model was created in Microsoft Excel (Microsoft, Seattle, Washington). The @Risk add‐in (Palisade Corporation, Ithaca, New York) for Excel was employed to account for the stochastic nature of key variables within a Monte Carlo simulation. Net present value was the primary metric used to assess the economic profitability of investments. The model was composed of a series of modules, which synergistically provide the necessary inputs for profitability analysis. Estimates of biological relationships within the model were obtained from the literature in an attempt to represent an average or typical US dairy. Technology benefits were appraised from the resulting impact on disease incidence, disease impact, and reproductive performance. In this paper, the model structure and methodology were described in detail.

Examples of the utility of examining the influence of stochastic input and output prices on the costs of culling, days open, and disease were examined. Each of these parameters was highly sensitive to stochastic prices and deterministic inputs.

Decision support tools, such as this one, that are designed to investigate dairy business decisions may benefit dairy producers.

The three volumes before us comprise the second title in the “Elgar Reference Collection” of Critical Ideas in Economics, a new series which, we learn from the book cover, aims to provide “an essential reference source for students, researchers and lecturers in economics.” Each volume in the series will bring together a collection of previously-published articles and book-chapters which “focuses on [a] concept widely used in economics,” and will thereby “improve access to important areas of literature which will not be available in the archives of many of the newer libraries.” No one can deny that Professor Walker’s topic is ideally suited to this stated intent; is there a concept more “widely used in economics” than that of equilibrium? A collection of previously-published items cannot, of course, be appraised in terms of the originality of its content. Such a work offers a different sort of contribution. In addition to the publisher’s stated aim of an improved access to those key articles which, either because of their age or the location of their publication, are not widely available, a work such as this can perform a function not unlike that which Weintraub (1991, pp. 129–130) ascribes to the survey article. The act of selection (and, hence, of exclusion) serves to delineate the field for the non-specialist, and the ordering of the items in the collection can reveal instructive lines of intellectual development – a “filiation of scientific ideas” to adopt Schumpeter’s (1954, p. 6) felicitous phrase – that otherwise might be obscured.

The purpose of this paper is twofold. First, the paper examines the risk transmission between crude oil and petroleum product prices of Japan’s oil futures market. Second, it compares the performance of two tests for Granger causality using realized variance (RV) and the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model.

The author measures the daily RV of crude oil, kerosene and gasoline futures listed on the Tokyo Commodity Exchange using high-frequency data, and he examines the Granger causality in variance between these variables using the vector autoregression model. Further, the author estimates the EGARCH model based on daily data and test for Granger causality in variance between commodity futures using Hong’s (2001) approach.

The results of the RV approach reveal that the hypothesis on the existence of a mutual volatility spillover between crude oil and petroleum product markets is accepted. However, the results of the conventional approach indicate that all the hypotheses on Granger causalities in variance are rejected. The methodology based on intraday high-frequency data exhibits higher power than the conventional approach based on daily data.

This is the first paper to investigate Japan’s oil market using RV. The authors conclude that the approach based on RV is universally adoptable when testing for Granger causality in variance.