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We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

Exchange rate regime decisively impacts key policy objectives such as financial stability, inflation control, etc. The purpose of this paper is to overview the evolution of exchange rate regimes spanning 12 nations in the Latin American region over the last two decades and estimate the degrees of influence of other major currencies on each nation.

Using the methodology developed by Frankel and Wei, the de facto extent of exchange rate flexibility is discerned for these nations and put into perspective with that of the IMF exchange rate regime classifications.

An increase in flexibility is found from the 1990s to the 2000s, especially for inflation targeting nations. However, the results reveal these nations adopt a policy of “guarded caution” and follow more of a de facto managed floating regime that is far from pure floats. The smaller economies of the region still pursue more fixed regimes. While the results correlate, to an extent, with the IMF's classifications, several areas of discrepancy are noted. The findings are robust to several sensitivity analyses.

A discrepancy between the IMF regime categorization and the true regime a country actually follows may cause IMF financial assistance programs to be less effective. Do countries follow regimes they are classified into? The present study gleans deeper into the issue and discerns this. The comparative analysis includes the relatively larger economies of the region as well as the seldom researched smaller ones.

This paper aims to propose a novel model to forecast regime switches in a time series to assist decision making.

The authors apply the clustering technique to group the data into five states. Then, a model is proposed to formulate the relationships from in‐sample observations, including regime switch relationships. Afterwards, the model uses the relationships to forecast the regime switches in out‐sample observations.

The study uses daily Taiwan Stock Exchange Capitalization Weighted Stock Index as the forecasting target. Regime switches in in‐sample observations are identified. And a regime switch is successfully forecasted by the proposed model.

The proposed model identifies a regime switch which matches the real event. It implies that the proposed model can be applied to other time series, such as Dow Jones or NASDAQ.

Previous studies contribute to the forecasting of regime switches. The forecasting results are validated with the real event. One of the forecasted regime switches matches the event of Lehman Brothers' declaring of bankruptcy.

We examine the rational utility and social–psychological approaches to develop fresh insights into nonviolent civil resistance. Rational utility models provide a useful, even essential, starting point for understanding what movement organizers must do if they are to overcome their movements’ collective action problems. However, the model's spare definition of agency excludes an investigation of regime legitimacy, how it is constructed and the role it plays in regime continuity. Employing a social psychological approach, we introduce the concept of “ideational assault” in which movement organizers challenge the ideas that justify voluntary civic cooperation with the ruling order. Ideational assault seeks “rhetorical coercion” in which the regime is stripped of credible arguments in its own defense and must increasingly rule by sanctions alone. Ideational assaults employ frames that delegitimize the prevailing order and mobilize people to act against it. By examining several frame forms, including, calls to action, symbolic jiu-jitsu, humor, and moral appeal, we cast new light on the ideational battle that rages alongside the fight for control of the streets. We conclude by arguing that students of nonviolent civil resistance should consult both the rational and social–psychological approaches in their analysis.

This chapter claims technology to be a principal mode of regulation in formal organizations alongside social structure and culture. Such a claim breaks with the conventional neo-institutional outlook that considers technology outside the object of institutional analysis of organizations. The distinctive regulative logic of computational technology is manifested in the increasing entanglement of domain-specific practices and their underlying cognitive and normative order with the decontextualized principles and methods that have traditionally been deployed in the management and control of work operations. Such entanglement and the effects it generates reflect the reshuffling of the regulative reach of technology, social structure and culture under the pressures exercised by the dynamics of current technological change and the impressive involvement of computational systems and artefacts in human affairs.

The Rajapaksa regime over the 2005–2022 period promoted a national-popular project based on a militarised Sinhala-Buddhist nationalism promoting a market-driven rentier economy. It illustrated a form of patrimonial capitalism undermining public accountability and the efficacy of the state bureaucracy. This popular-national project was dependent on strengthening ties with China while distancing relations with India and the Global North (USA and the EU). The ways in which the external relations were coordinated reinforced discrimination against Tamil and Muslim communities, while disregarding their demands for justice and reparations. The increasing integration of the economy with financial markets, driven by the Central Bank, amplified the commercialisation of the state, restraining public revenues and state oversight. Meanwhile, the militarisation of the state involved the commercialisation of the military, opaque military budgets and violent repression of protests. The Rajapaksa regime, which enabled a minority-privileged (leisure) class to culturally flourish in regulated safe spaces, also instigated multiple protests from below demanding democracy as well as justice.

The Epidemiologic Transition can help us understand a fundamental puzzle about aging. The puzzle stems from two seemingly contradictory facts. The first fact is that death rates from noninfectious degenerative maladies – the so-called diseases of aging – increase as people age. It seems to be at odds with the historical fact that for nearly a century in which people were aging more than ever before, the aggregate rates of such diseases have been decreasing. In what sense can both be true? Crucial to resolving the puzzle are the age-profiles of such diseases in cohorts that grew up in the different regimes of the Transition. For each cohort, noninfectious diseases had increased with age, resulting in an upward-sloping age profile, which affirms the first fact. As the regimes were transitioning from the Malthusian to the modern one, however, the profiles of successive cohorts had been shifting downward: death rates from noninfectious diseases were shrinking at each age, signifying the newer cohorts’ greater aging potentials. The shifting profiles had been renewing the cohort mix of the population, shaping the century-long descent of such diseases in aggregate, giving rise to the historical fact. The profiles had shifted early in the cohorts’ adult years, associating closely with the newer epidemiologic conditions in childhood. Those conditions appear to be a circumstance under which aging potentials of cohorts could be misgauged, including in one troubling episode in the first half of the nineteenth century when the potentials had reversed.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

We introduce a multi-level model of the dependence of interfirm network topologies on the distribution and commonality of information in a network and the information strategies pursued by its member firms. Network topology, information properties of the network, and firm-level action within the network form dynamic, recursive, cross-level relationships – information properties in the network determine firm-level action, which in turn impacts the network topology and information properties. We derive predictions about the kinds of information strategies that firms are likely to adopt and succeed with in different information regimes, and about the kinds and short- and long-run dynamics of network topologies expected under different information regimes. Our model sheds new light on network topologies as a dependent variable that can be explained by network-level information regimes and firm-level information strategies.

In this chapter, we outline the statistical consequences of neglecting structural breaks and regime switches in autoregressive and GARCH models and propose two strategies to approach the problem. The first strategy is to identify regimes of constant unconditional volatility using a change point detector and estimate a separate GARCH model on the resulting segments. The second approach is to use a multiple-regime GARCH model, such as the Flexible Coefficient GARCH (FCGARCH) specification, where the regime-switches are governed by an observable variable. We apply both alternatives to an array of financial time series and compare their forecast performance.