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We develop a new model of cycles and crises in emerging markets, featuring an occasionally binding borrowing constraint and stochastic volatility, and estimate it with quarterly data for Mexico since 1981. We propose an endogenous regime-switching formulation of the occasionally binding borrowing constraint, develop a general perturbation method to solve the model, and estimate it using Bayesian methods. We find that the model fits the Mexican data well without systematically relying on large shocks, matching the typical stylized facts of emerging market business cycles and Mexico's history of sudden stops in capital flows. We also find that interest rate shocks play a smaller role in driving both cycles and crises than previously found in the literature.

We consider several time series, and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modelled by a regime-switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness-of-fit procedure based on Cramér–von Mises statistics is proposed to select the appropriate number of regimes. Numerical experiments are performed to assess the validity of the proposed methodology. As an example of application, we evaluate a European put-on-max option on the returns of two assets. To facilitate the use of our methodology, we have built a R package HMMcopula available on CRAN. Les auteurs considèrent plusieurs séries temporelles univariées et trouvent pour chacune un modèle dynamique paramétrique approprié. Ils obtiennent alors des termes d’erreur indépendants pour chaque série et modélisent la dépendance entre ces termes d’erreur par une copule avec changement de régime. Ils utilisent l’algorithme EM pour estimer les paramètres et proposent une procédure séquentielle de tests d’adéquation basés sur la statistique de Cramér-von Mises pour sélectionner le nombre approprié de régimes. Les auteurs réalisent une série d’expériences numériques afin d’évaluer la validité et la performance de la méthodologie proposée. À titre d’exemple d’application, ils évaluent le prix d’une option de vente européenne sur le rendement maximal de deux titres en utilisant un modèle de copule à changement de régime. Finalement, afin de faciliter l’utilisation future de la méthodologie proposée, ils construisent une librairie de fonctions basée sur le progiciel R qui s’intitule HMMcopula et qui est disponible gratuitement sur le CRAN.

In this work, we present a rigorous development of a model for the Price–Volume relationship of transactions introduced in 2009. For this development, we rely on the precise formulation of diffusion auto-induced regime-switching models presented in our previous work of 2020. The auto-induced regime-switching models referred to may be based on a finite set of stochastic differential equations (SDE)—all defined on the same bounded time interval—and a sequence of interlacing stopping times defined by the hitting threshold times of the trajectories of the solutions of the SDE. The coupling between price and volume—which we take as a proxy of liquidity—is assumed to be the following: the regime switching in the price variable occurs at the stopping times for which there is a change of region—in the product state space of price and liquidity—for the liquidity variable (and vice versa). The regimes may be defined parametrically—that is, the SDE coefficients keep the same functional form but with varying parameters—or the functional form of the SDE coefficients may change with each regime. By using the same noise source for both the price and the liquidity regime-switching models—volume (liquidity), which, in general, is not a tradable asset—we ensure that despite incorporating information on liquidity, the price part of the coupled model can be assumed to be arbitrage free and complete, allowing the pricing and hedging of derivatives in a simple way.

This paper introduces a new empirical framework to identify the regimes of jump‐type tail risk over multiple trading horizons. Our approach combines the hidden Markov regime‐switching model with realized jumps, which allows us to examine the tail risk exposure of investors at different investment scales. Applying our method to data on bonds, stocks, and currencies, we find evidence that market risk linked to jumps exhibits time‐varying regime shifts and horizon‐dependence. We show that high‐frequency trading does not contribute to market instability, as measured by jump risk. The European bond market appears to be more vulnerable to downside (left‐tail) jump risk, relative to the U.S. Treasury bond market. Market jump fear embedded in the VIX exhibits vertical clustering where both risk regimes at low frequencies remain unchanged at higher frequencies. These results suggest that the premium for jump risk not only depends on stress periods, but also on the frequency at which investors trade financial assets.

This research focuses on the prediction of the probability of (re)entering high financial stress (via a large set of cyclical risk accumulation indicators). The focus is placed on a specific single-country analysis to obtain answers to questions about which indicators are best in explaining the future probability of (re)entering a high-stress regime. This allows the policymaker to get a better focus on the best-performing variables. It is challenging to monitor a whole set of indicators of cyclical risk build-up; the results could bring into focus a smaller group of the essential variables. The contribution of this paper is in finding a set of indicators that help in forecasting financial stress, in terms of switching from one regime to another. The regime-switching models 'results indicate that some credit specifications, house price dynamics, and debt burden could be best monitored for the case of Croatian data.

The paper proposes endogenous information choice as a channel through which uncertainty affects price dynamics. I consider a rational inattention model with volatility uncertainty and endogenous information processing capability. According to the model, firms' learning and optimal attention exhibits inertia and asymmetry in response to volatility changes. Firms choose to process more information when uncertainty rises, especially about aggregate conditions, and their pricing behavior changes accordingly. Using a Markov-switching factor-augmented vector autoregression (MS-FAVAR), the paper also documents a significant positive correlation between volatility and firms' responsiveness to macro-and microlevel shocks, consistent with model predictions.

Dynamic hedging has been adopted by many insurance companies to mitigate the financial risks associated with variable annuity guarantees. To simulate the performance of dynamic hedging for variable annuity products, insurance companies rely on nested stochastic projections, which is highly computationally intensive and often prohibitive for large variable annuity portfolios. Metamodeling techniques have recently been proposed to address the computational issues. However, it is difficult for researchers to obtain real datasets from insurance companies to test metamodeling techniques and publish the results in academic journals. In this paper, we create synthetic datasets that can be used for the purpose of addressing the computational issues associated with the nested stochastic valuation of large variable annuity portfolios. The runtime used to create these synthetic datasets would be about three years if a single CPU were used. These datasets are readily available to researchers and practitioners so that they can focus on testing metamodeling techniques.

We estimate short- and long-run tax elasticities that capture the relationship between changes in national income and tax revenue. We show that the short-run tax elasticity changes according to the business cycle. We estimate a two-state Markov-switching regression on a novel data set of tax policy reforms in 15 European countries from 1980 to 2013, showing that the elasticities during booms and recessions are statistically (and often economically) different. The elasticities of personal income taxes, corporate income taxes, indirect taxes and social contributions tend to be larger during recessions. Estimates of long-run elasticities are in line with existing literature.

This work is devoted to a class of stochastic approximation problems with regime switching modulated by a discrete-time Markov chain. Our motivation stems from using stochastic recursive algorithms for tracking Markovian parameters such as those in spreading code optimization in CDMA (code division multiple access) wireless communication. The algorithm uses constant step size to update the increments of a sequence of occupation measures. It is proved that least squares estimates of the tracking errors can be developed. Assume that the adaptation rate is of the same order of magnitude as that of the time-varying parameter, which is more difficult to deal with than that of slower parameter variations. Due to the time-varying characteristics and Markovian jumps, the usual stochastic approximation (SA) techniques cannot be carried over in the analysis. By a combined use of the SA method and two-time-scale Markov chains, asymptotic properties of the algorithm are obtained, which are distinct from the usual SA results. In this paper, it is shown for the first time that, under simple conditions, a continuous-time interpolation of the iterates converges weakly not to an ODE, as is widely known in the literature, but to a system of ODEs with regime switching, and that a suitably scaled sequence of the tracking errors converges not to a diffusion but to a system of switching diffusion. As an application of these results, the performance of an adaptive discrete stochastic optimization algorithm is analyzed.

In this work, we investigate statistical properties of change point estimators based on moving sum statistics. We extend results for testing in a classical situation with multiple deterministic change points by allowing for random exogenous change points that arise in Hidden Markov or regime switching models among others. To this end, we consider a multiple mean change model with possible time series errors and prove that the number and location of change points are estimated consistently by this procedure. Additionally, we derive rates of convergence for the estimation of the location of the change points and show that these rates are strict by deriving the limit distribution of properly scaled estimators. Because the small sample behavior depends crucially on how the asymptotic (long-run) variance of the error sequence is estimated, we propose to use moving sum type estimators for the (long-run) variance and derive their asymptotic properties. While they do not estimate the variance consistently at every point in time, they can still be used to consistently estimate the number and location of the changes. In fact, this inconsistency can even lead to more precise estimators for the change points. Finally, some simulations illustrate the behavior of the estimators in small samples showing that its performance is very good compared to existing methods.