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We examine the predictability of positive and negative stock market bubbles via an application of the LPPLS Confidence Multi-scale Indicators to the , FTSE and NIKKEI indexes. We find that the LPPLS framework is able to successfully capture, ex-ante, some of the prominent bubbles across different time scales, such as the Black Monday, Dot-com, and Subprime Crisis periods. We then show that measures of short selling activity have robust predictive power over negative bubbles across both short and long time horizons, in line with the previous studies suggesting that short sellers have predictive ability over stock price crash risks. Market liquidity, on the other hand, is found to have robust predictive power over both the negative and positive bubbles, while its predictive power is largely limited to short horizons. Short selling and liquidity are thus identified as two important factors contributing to the LPPLS-based bubble indicators. The evidence overall points to the predictability of stock market bubbles using market-based proxies of trading activity and can be used as a guideline to model and monitor the occurrence of bubble conditions in financial markets. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we study the dynamical behavior of a regime-switching SIRS epidemic model with a ratio-dependent incidence rate. We propose a stochastic reproduction number R 0 S which can be regarded as a threshold to use in identifying the stochastic extinction and persistence: if R 0 S < 1 , the disease dies out exponentially with probability one; while if R 0 S > 1 , there exists a unique ergodic stationary distribution of the positive solutions to the system which implies the stochastic persistence of the infectious disease. Highlights • A regime-switching SIRS epidemic model with a ratio-dependent incidence rate is studied. • A stochastic reproduction number R 0 S is proposed to be regarded as a threshold. • If R 0 S < 1 , the disease dies out exponentially with probability one. • If R 0 S < 1 , there is a unique ergodic stationary distribution of the system. [ABSTRACT FROM AUTHOR]

Abstract This paper examines whether the intertemporal tradeoffs between risk and return explain mean reversion in sovereign CDS spreads. I test how mean reversion prevents the explosiveness of CDS spreads in Europe. The relationship between risk and return has been widely studied in finance. The results show that, during the pre-crisis period, sovereign CDS spread changes were more consistent with the mean reversion hypothesis for most European countries. I also find strong evidence that the intertemporal tradeoffs between volatility and return explain in part the mean reversion in the markets for European CDS. Moreover, I show that during the crisis period, CDS spreads' changes are consistent with the random walk hypothesis and mean aversion is more important for large holding periods. [ABSTRACT FROM AUTHOR]

This study aims to analyze two important features of crude oil price data, namely, persistence and parameter instability. We apply an autoregressive fractionally integrated moving average model in which crude oil price persistence measured through the fractional integration parameter, conditional innovation variance and persistence change through Markov-switching dynamics. Model estimation is conducted using Gibbs sampling combined with data augmentation. Applied to monthly West Texas Intermediate crude oil price data from September 1859 to December 2017, we find evidence of four regimes. The main effect of regime switching is in the conditional variance and persistence of the innovations, but there is the possibility that regime switching also affects the fractional integration parameter. Across regimes crude oil price is very persistent with the order of integration estimated close to or slightly higher than one. Finally, discarding parameter instability leads to overestimation of the degree of persistence in the price of crude oil. It also results in inferior density forecasts. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: We explore the likely effect of Brexit on inward foreign direct investment (FDI) through its possible effect on the benchmark variables that characterize the macroeconomy. For this we propose the use of a Markov regime switching structural vector auto‐regression to distinguish between the volatile and stable states of the economy and account, among other effects, for the contemporaneous effects that the frequency of FDI innately generates. Our findings suggest that, if Brexit triggers a sterling depreciation in the current economic climate, this will fuel a prolonged negative effect on FDI. FDI flows may be positively affected (at most) by a sterling depreciation after Brexit only if this event drives the UK economy to a period of highly volatile growth, inflation, interest and exchange rates: a scenario that is rather unlikely. And, even then, the sterling depreciation benefits would last for only a short period of time. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to constructing investment portfolios based on Environmental, Social, and Governance (ESG) factors. The approach uses machine learning models and a Markov Switching Variance (MSV) model to predict returns and volatility, and a Multi Criteria Decision-Making (MCDM) model, named TODIM, to select superior stocks. An optimization model based on the Mean-Variance model considering ESG is then used to find optimal weights for each asset. The goal is to find more profitable and stable investment opportunities by considering a broader range of factors for investors, and the proposed approach is compared to several benchmarks and outperforms them in terms of profitability and risk metrics. Results in two different markets show that ESG-oriented investment can be profitable in the short term, and the use of features such as regime separation and future-encompassing information in addition to historical data enhances the outcomes of the proposed framework. [Display omitted] • Designing a highly profitable portfolio construction approach based on ESG factors. • Training predictors on the separated regimes through MSV, can positively affect the performance of the model. • Compared to classic portfolio construction models, the proposed model considers more criteria than historical prices, such as predicted return, volatility, correlation, and the relationships among different assets. [ABSTRACT FROM AUTHOR]

• Analyses linkages between green, conventional bond markets and geopolitical risk. • Contagion is tested for using a Markov-switching VAR (MS-VAR) framework. • Green bonds are more susceptible to geopolitical risk in times of high volatility. • Corporate and sovereign bonds less vulnerable to geopolitical risk than green bonds. This paper analyses linkages between green, conventional (corporate and sovereign) bond markets and geopolitical risk in high and low volatility periods between 2014 and 2022 using a Markov-switching VAR (MS-VAR) framework. The results indicate that geopolitical risk significantly affects green bonds in periods of high volatility, but does not do so to conventional bond markets. Green bond markets are significantly affected by sovereign and corporate bonds in both regimes, with stronger effects from corporate bonds evident in high volatility periods. This suggests that green bonds behave differently to conventional bonds and may be more susceptible to geopolitical risk and contagion. [ABSTRACT FROM AUTHOR]

In this paper, we focus on extinction and ergodicity for the n -dimensional population model driven by α -stable processes with Markov switching. Some sufficient conditions for extinction or ergodicity are given. Our conditions disclose the parameter α and stationary distribution of Markov chain to the impact on the extinction or ergodicity. [ABSTRACT FROM AUTHOR]

In this paper, we use Markov switching autoregressive model and bivariate VAR model to analyze the empirical relationship between stock return and trading volume based on stock market cycles. Using daily data for the IHSG closing price and trading volume from 2010 to 2014, we identify the bull and bear phases in Indonesia stock market, then we analyze the return–volume relationship in both contemporaneous and dynamic context. We find that (1) there is a positive contemporaneous return–volume relationship in both bull and bear markets, which is only significant in bull markets; (2) no evidence of asymmetry in contemporaneous relationship is found; and (3) there exists a positive unidirectional causality from stock return to trading volume. In addition, our findings are robust for different sample period and data frequency.

The goal of this paper is to evaluate the behavior of the main parameters of the Brazilian economy through the estimation of an open-economy dynamic stochastic general equilibrium (DSGE) model using Bayesian methods and allowing for Markov switching of certain parameters. Using the DSGE model developed by Justiniano and Preston (2010) and the solution method of the Markov switching DSGE (MS-DSGE) model proposed by Farmer et al. (2008), this paper found a superior fit in the data of Markov switching models, rejecting the hypothesis of constant parameters in DSGE models for the Brazilian economy.

The paper will use a MSGARCH model to analyze how are transmitted the sudden changes in volatility transmission from the energy market across several energy indices including Romania. In addition to the GARCH models, the class of Markov-switching GARCH (MSGARCH) may provide an early warning indication of changes in the conditional volatility. We use daily closing data spanning a ten year period in order to capture the dependencies and sensitivities of energy related equity sector.

We investigate the stochastic HTLV‐I infection model with CTL immune response, which is disturbed by both white and telegraph noises. First, we obtain that the model solution is positive and global. Furthermore, we obtain the existence of ergodic stationary distribution of the solution by stochastic Lyapunov functions with regime switching, and we also establish sufficient conditions for the extinction of system with regime switching. Meanwhile, the threshold R0∗ between ergodic property and extinction of the stochastic system is acquired. Finally, our theoretical results are verified by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper proposes two stochastic SIQS epidemic models with periodic parameters and Markov switching. We first prove that the stochastic non-autonomous periodic system has a nontrivial positive periodic solution by using the Khasminskii’s theory. Then the sufficient conditions for extinction of the disease are obtained. Furthermore, we construct suitable stochastic Lyapunov functions with regime switching to prove the existence of ergodic stationary distribution of the stochastic SIQS epidemic model. At last, some rigorous numerical simulations are presented to illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we consider long time behavior of the Cox-Ingersoll-Ross (CIR) interest rate model driven by stable processes with Markov switching. Under some assumptions, we prove an ergodicity-transience dichotomy, namely, the interest rate process is either ergodic or transient. The suficient and necessary conditions for ergodicity and transience of such interest model are given under some assumptions. Finally, an application to interval estimation of the interest rate processes is presented to illustrate our results. [ABSTRACT FROM AUTHOR]

Advances in pain measurement using ecological momentary assessments offer novel opportunities for understanding the temporal dynamics of pain. This study examined whether regime-switching models, which capture processes characterized by recurrent shifts between different states, provide clinically relevant information for characterizing individuals based on their temporal pain patterns. Patients with rheumatic diseases (N = 116) provided 7 to 8 momentary pain ratings per day for 2 weekly periods, separated by 3 months. Regime-switching models extracted measures of Average pain (mean level over time), Amplitude (magnitude of shifts in pain levels), Persistence (average duration of pain states), and Dominance (relative duration of higher vs lower pain states) for each patient and assessment period. After controlling for Average pain, the Persistence of pain states uniquely predicted emotional functioning measures, whereas the Dominance of higher pain uniquely predicted physical functioning and pain interference. Longitudinal analyses of changes over the 3 months largely replicated cross-sectional results. Furthermore, patients' retrospective judgments of their pain were uniquely predicted by Amplitude and Dominance of higher pain states, and global impressions of change over the 3 months were predicted by changes on Dominance, controlling for Average pain levels. The results suggest that regime-switching models can usefully capture temporal dynamics of pain and can contribute to an improved measurement of patients' pain intensity. [ABSTRACT FROM AUTHOR]

This article considers the valuation of digital, barrier, and lookback options in a Markovian, regime-switching, Black-Scholes model. In Fourier space, integral representations for the option prices are derived via the theory on matrix Wiener-Hopf factorizations. Our main focus is on the 2-state case where the matrix Wiener-Hopf factorization is available analytically. A comparison to several numerical alternatives (analytical approximations, the Brownian bridge algorithm and a finite element scheme) demonstrates that the pricing formulas are easy to implement and lead to accurate price estimates. [ABSTRACT FROM AUTHOR]

Stock market volatility forecasting is an important and interesting topic of research due to its impact on trading decisions. This behavior is particularly important in emerging economies in Latin America, and moreover, in the larger stock markets of this region (Brazil, Mexico, and Chile). The Latin American region is highly influenced by macroeconomic factors; therefore, it is relevant to discover ways in which the market index forecast accuracy can be improved. Thus, in this study, we present a novel methodology: first, we forecast the volatility of each market using different GARCH models. Then, we use Markov Switching to determine the states of external factors. Subsequently, these states are combined in a ANFIS model to determine individual impact on each index, and finally, we use an ANN algorithm to improve the forecast accuracy of the best GARCH model forecast with the combined effects of all the external factors. The results indicate that this methodology manages to improve prediction in terms of MAPE and RMSE, thus providing a more accurate volatility estimation. [ABSTRACT FROM AUTHOR]

In this paper, we examine empirically GDP per capita convergence using an approach that explicitly allows for regime switching in the long memory parameter d within the context of a Markov Switching (MS)-ARFIMA framework. As existing methods used in the estimation of standard MS models, such as the EM algorithm are no longer appropriate, we will make use of the Viterbi algorithm to estimate the long memory MS model used by Tsay and Härdle (Tsay, W.-J., and W. K. Härdle. 2009. "A Generalized Arfima Process with Markov- Switching Fractional Differencing Parameter." Journal of Statistical Computation and Simulation 79: 731-745.). We will classify the output gap series into two regimes, a high d and a low d regime, where a high d close to unity would imply persistence and lack of convergence. By examining the path of d parameter over time which enables us to observe non-convergent behavior in more detail, we find that converging behavior is diminishing over time and divergence is the dominant force. [ABSTRACT FROM AUTHOR]

The task of cleaning reflectors is a widely adopted strategy to handle the soiling in concentrated solar power (CSP) plants. The dynamics of the soiling of frequently cleaned mirrors is dependent on many factors such as the cleaning method, its frequency and the seasonal soiling rate. This study proposes a new approach to model the soiling of regularly cleaned reflectors instead of the inaccurate methods undermining the CSP yield by using fixed reflectance assumptions. Markov switching (MS) regime, which is a non linear time series approach where parameters are allowed to switch between the regimes, are applied on the reflectance data of second surface silvered-glass mirrors, exposed at the Plataforma Solar de Almería (PSA) in Spain during two years and cleaned biweekly using high pressure demineralized water. The nonlinearities typically exhibited by such reflectance time series are successfully modeled by accounting for two regimes, a soiled and a clean regime, and incorporating various parameters (rain, washing cycle, and lagged autoregressive terms) in the suggested MS models. Based on the information criteria and the diagnostic of the models residuals, the best model has a switching normalized reflectance mean of 0.944 during the clean regime versus 0.688 during the soiled regime, in addition to one fixed lag autoregressive term. In order to evaluate the adequacy of the proposed model versus the traditional approach, which uses fixed reflectance as input for estimating CSP plants yield, four reflectance scenarios were studied by simulating the output of a 30 MWe plant using TRNSYS© (TRaNsientSYstem Simulation) program. The first and the second scenarios used the time series of the measured and fitted model reflectance data, while the third and the forth scenarios used fixed inputs (a maximum and a yearly average reflectance). The comparison of the simulation results showed that adopting the innovative proposed concept of switching regimes results in very good performance, especially in the soiled regime during which the simplistic reflectance considerations, which ignore the soiling dynamics and the applied washing cycle, undermine the generated power. [ABSTRACT FROM AUTHOR]

The article investigates the growth in the general profit rate in the US during the 1949-2007 period with a Markov switching model. The evidence is consistent with a long swing with means displaying opposite signs under the two regimes (increasing or declining) and high degree of persistence within each regime. The results for this nonlinear approach reinforce previous empirical evidence that does not provide support for a systematic and declining tendency in profit rate as advanced in the Marxian literature. [ABSTRACT FROM AUTHOR]

Summary: Volatility in financial markets is characterized by alternating persistent turmoil and quiet periods, but also by a slowly varying average level. This slow moving component keeps open the question of whether some of its features are better represented as abrupt or smooth changes between local averages of volatility. We provide a new class of models with a set of parameters subject to abrupt changes in regime (Markov switching) and another set subject to smooth transition changes. These models capture the possibility that regimes may overlap with one another (fuzzy). The empirical application is carried out on the volatility of four US indices. It shows that the flexibility of the new model enables a better overall performance over either Markov switching or smooth transitions and provides a local average volatility measure as a parametric estimation of the low frequency component. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The natural real rate of interest has been on a declining path for the past 30 years. I present a regime-switching analysis of the natural rate, which suggests that the current low levels are likely to persist in the near future. I identify a high global demand for safe assets as the most important factor in keeping the natural rate low. I conclude that the current low levels of the policy rate are generally appropriate, with some upside risk, and that forward guidance should be characterized by a flat policy rate path. [ABSTRACT FROM AUTHOR]

We document the strong evidence of time variation in the volatility of Euro Area business cycles since 1970. Then we provide the quantitative sources of these changes using a medium-scale DSGE model allowing time variation in structural disturbance variances. We show that (1) the size of different types of shock oscillates, in a synchronized manner, between two regimes over time, with the high-volatility regime prevailing predominantly in the 1970s, sporadically in the 1980s and 1990s, and during the Great Recession; (2) their relative importance remains, however, unchanged across regimes, where neutral technology shocks and marginal efficiency of investment shocks are the dominant sources of business cycle fluctuations; and 3) these investment shocks, which affect the transformation of savings into productive capital, can be interpreted as an indicator of credit conditions. [ABSTRACT FROM AUTHOR]

This paper considers two S-DI-A models in random environments. Firstly, using Has’minskii theory of periodic solution, we show that stochastic periodic S-DI-A model has a nontrivial positive periodic solution if. Then, we construct stochastic Lyapunov functions with regime switching to obtain the existence of ergodic stationary distribution of the solution to S-DI-A model perturbed by white and telephone noises. Finally, examples are introduced to illustrate the results developed. [ABSTRACT FROM AUTHOR]

In this paper, we solve a continuous-time portfolio choice problem of an investor under a Markov jump linear system that effectively captures stochasticity in asset returns, price impacts, and market resilience. Specifically, the investor chooses his portfolio to maximize the expected excess returns netting risks and trading costs due to the price impacts in a Markov switching market. We show that the value function and the optimal feedback trading strategy can be expressed in terms of the solution of a coupled matrix Riccati differential system. The presence of the market resilience and the Markov switching results in the coupled Riccati system being non-canonical. We manage to establish a set of transparent sufficient conditions to ensure the well-posedness of the coupled system. Our numerical results indicate that the investor trades more (less) aggressively in a liquidity regime where the temporary price impact is low (high) and permanent price impact is high (low) with low (high) market resilience. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze the impact of trade openness and the factors based on the gravity model on the bilateral trade flows between Thailand and Japan. The factors consist of GDP, distance, trade openness, and exchange rate. Bilateral trade is composed of two flows: Thailand’s export flow to Japan, and Thailand’s import flow from Japan. The specified gravity equations are estimated by Copula-based Markov switching seemingly unrelated regression approach. The best-fitting model is chosen based on the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC). The Normal and Student's t distributions are for Thailand’s export equation and Thailand’s import equation, respectively. The Student's t copula is applied for joint distribution. Analyzing the bilateral trade flow is separated into two situations, namely the high and the low growth markets. Empirical results show that distance provides a positive effect on the export in a high growth regime, but a negative impact on the export in a low growth regime. As for Thailand’s import flow, all variables, but especially trade openness, provide strong evidence supporting significance for both regimes. For the GDPs of both Thailand and Japan, trade openness and the exchange rate increase import flow in a high growth market. Meanwhile, the exchange rate decreases import flow in a low growth market. The Markov Switching Probability Estimation notes that Thailand’s trading with Japan is mostly in the fast-growing market.