Regime-switching Model Research Articles

Geopolitical threats, equity returns, and optimal hedging

In this paper, we demonstrate that the U.S. equity market and a few specific sectors produce significantly positive returns during high geopolitical threats, even with the presence of standard controls, whereas other major markets around the world fail to exhibit such results. We use the geopolitical threats (GPT) index of Caldara and Iacoviello (2022). We extend our study by examining the equity returns during extremely high geopolitical threats and find the results significantly positive for the U.S. equity market and two specific sectors- information technology and financials. The results of our investigation are likewise supported by the lead-lag regression and the Markov regime-switching model. Our results are robust in the presence of various alternative measures of market uncertainty indices, for instance, economic policy uncertainty, economic uncertainty, macroeconomic uncertainty etc., on a daily basis. However, the return on equity was not robust when conditional volatility and monthly frequency were considered. We also investigate and find the optimal hedging implications for investors during the presence of geopolitical threats. We find a considerable hedge alternative between the US market and gold and further explore how Geopolitical threats affect Gold and different US sectoral Exchange-traded funds (ETFs).

Efficient adaptive strategies with fourth-order compact scheme for a fixed-free boundary regime-switching model

Efficient adaptive strategies with fourth-order compact scheme for a fixed-free boundary regime-switching model

Regime-based debt sustainability analysis: Evidence from euro area economies

This paper empirically studies non-linearities in debt sustainability analysis by resorting to the modern estimation technique of panel smooth transition regression (PSTR). We assess euro area debt sustainability by analysing the reaction of the primary balance to changes in public debt, relative to GDP respectively, in annual frequency from 2000–2019 in a panel framework. The PSTR allows to estimate the existence of a threshold in the behaviour of the reaction function, refrains from the country-wise perspective and applies a regime-switching model to detect non-linearities. Data is segregated into different regimes endogenously via a logistic regression. Our results show that there are two different regimes in the euro area: a high and a low debt regime. The estimated reaction coefficient for the low debt regime is statistically insignificant, whereas it is positive and statistically significant for the high debt regime. Further, for a sub-sample of highly indebted economies we find a statistically significant negative (positive) reaction coefficient for the low (high) debt regime.

British Put Option On Stocks Under Regime-Switching Model

In a plain vanilla option, its holder is given the right, but not the obligation, to buy or sell the underlying stock at a specified price (strike price) at a predetermined date. If the exercise date is at maturity, the option is called a European; if the option is exercised anytime prior to maturity, it is called an American. In a British option, the holder can enjoy the early exercise feature of American option whereupon his payoff is the ‘best prediction’ of the European payoff given all the information up to exercise date under the hypothesis that the true drift of the stock equals a specified contract drift. In this paper, in contrast to the constant interest rate and constant volatility assumptions, we consider the British option by assuming that the economic state of the world is described by a finite state continuous-time Markov chain. Also, we provide a solution to a free boundary problem by using PDE arguments. However, closed form expression for the arbitrage-free price are not available in our setting.

A general approach for lookback option pricing under Markov models

We propose a computationally efficient method for pricing various types of lookback options under Markov models. We utilize the model-free representations of lookback option prices as integrals of first passage probabilities. We combine efficient numerical quadrature with continuous-time Markov chain approximation for the first passage problem to price lookbacks. Our method is applicable to a variety of models, including one-dimensional time-homogeneous and time-inhomogeneous Markov processes, regime-switching models and stochastic local volatility models. We demonstrate the efficiency of our method through various numerical examples.

Investment decisions determinants in the GCC cryptocurrency market: a behavioural finance perspective

PurposeThis study aims to investigate the impact of financial and behavioural factors on investment decisions in the cryptocurrency market within the Gulf Cooperation Council (GCC).Design/methodology/approachThe study uses the cross-sectional absolute deviation methodology developed by Chang et al. (2000) to determine the existence of herding behaviour during extreme conditions in the cryptocurrency market of four GCC countries: Bahrain, Saudi Arabia, Kuwait and UAE. In addition, a questionnaire survey was distributed to 322 investors from the GCC cryptocurrency markets to gather data on their investment decisions.FindingsThe study finds that the herding theory, prospect theory and heuristics theory account for 16.5% of the variance in investors' choices in the GCC cryptocurrency market. The regression analysis results show no multicollinearity problems, and a high F-statistic indicates the general model's acceptability in the results.Practical implicationsThe study's findings suggest that behavioural and financial factors play a significant role in investors' choices in the GCC cryptocurrency market. The study's results can be used by investors to better understand the impact of these factors on their investment decisions and to develop more effective investment strategies. In addition, the study's findings can be used by policymakers to develop regulations that consider the impact of behavioural and financial factors on the GCC cryptocurrency market.Originality/valueThis study adds to the body of literature in two different ways. Initially, motivated by earlier research examining the impact of behaviour finance factors on investment decisions, the authors look at how the behaviour finance factors affect investment decisions of the GCC cryptocurrency market. To extend most of these studies, this study uses a regime-switching model that accounts for two different market states. Second, by considering the recent crisis and more recent periods involving more cryptocurrencies, the authors have contributed to several studies examining the impact of behavioural financial factors on investment decisions in cryptocurrency markets. In fact, very few studies have examined the impact of behavioural finance on cryptocurrency markets. Therefore, to the best of the authors’ knowledge, this study is the first of its kind to investigate how behavioural finance factors influence investment decisions in the GCC cryptocurrency market. This allows to better illuminate the factors driving herd behaviour in the GCC cryptocurrency market.

Determinants of the European Sovereign Debt Crisis: Application of Logit, Panel Markov Regime Switching Model and Self Organizing Maps.

The study aims to empirically identify the determinants of the debt crisis that occurred within the framework of 15 core EU member countries (EU-15). Contrary to previous empirical studies that tend to use event-based crisis indicators, our study develops a continuous fiscal stress index to identify the debt crises in the EU-15 and employs three different estimation techniques, namely self-organizing map, multivariate logit and panel Markov regime switching models. Our estimation results show first that the study correctly identifies the time and the length of the debt crisis in each EU-15-member country. Empirical results then indicate, via three different models, that the debt crisis in the EU-15 is the consequence of deterioration of both financial and macroeconomic variables such as nonperforming loans over total loans, GDP growth, unemployment rates, primary balance over GDP, and cyclically adjusted balance over GDP. Furthermore, variables measuring governance quality, such as voice and accountability, regulatory quality, and government effectiveness, also play a significant role in the emergence and the duration of the debt crisis in the EU-15.

Conditional mean reversion of financial ratios and the predictability of returns

While traditional predictive regressions for stock returns using financial ratios are empirically proven to be valuable at long-term horizons, evidence of predictability at few-month horizons is still weak. In this paper, based on the empirical regularity of a typical dynamic of stock returns following the occurrence of a mean reversion in the US Shiller CAPE ratio when the latter is high, we propose a new predictive regression model that exploits this stylized fact. In-sample regressions approximating the occurrence of mean reversion by the smoothed probability from a regime-switching model show superior predictive powers of the new specification at few-month horizons. These results also hold out-of-sample, exploiting the link between the term spread and the credit spread as business cycle variables and the occurrence of mean reversion in the US Shiller CAPE ratio. For instance, the out-of-sample R-squared of the new predictive regression model is ten (four) times bigger than that of the traditional predictive model at a 6 (12) month horizon. Our results are robust with respect to the choice of the valuation ratio (CAPE, excess CAPE or dividend yield), and countries (Canada, Germany and the UK). We also conduct a mean–variance asset allocation exercise which confirms the superiority of the new predictive regression in terms of utility gain.

Political stability and credibility of currency board

Do currency boards offer protection against self-fulling speculative attacks? This paper examines the credibility of currency boards of Argentina, Bulgaria, Estonia, Hong Kong, Latvia, and Lithuania. We employ a Bayesian Markov regime-switching model to analyze the role of economic fundamentals and self-fulfilling expectations in accounting for the credibility of the currency board. We find that the credibility of our sample currency boards is all subject to self-fulfilling runs. We also find that the political stability of adopting economies relates to the credibility of their currency boards and explains a few self-fulfilling runs of their currency boards.

Renewable energy deployment and geopolitical conflicts

This paper investigates whether geopolitical conflicts play a critical role in stimulating countries to shift toward clean energy solutions. We use the panel regime-switching models, which allow us to capture the nonlinear dynamics of the energy transition. Our results for a panel of developed and emerging countries reveal that the geopolitical context does not impact the renewable-income nexus; however, we find that adverse geopolitical events would impact the diffusion of alternative energy sources depending on the level of economic development. Rising geopolitical conflicts would encourage high-income nations to switch toward low-carbon energy sources. Considering the increasing number of regional conflicts, less developed countries must urgently develop their economies away from traditional energy sources and enhance the contribution of the renewable sector.

Automation of the Individualized Investing Strategy for an Investment Advisor Established by a Semi-Markov Regime-Switching Model

Automation of the Individualized Investing Strategy for an Investment Advisor Established by a Semi-Markov Regime-Switching Model

Analyzing the Impact of Foreign Capital Inflows and Political Economy on Economic Growth: An Application of Regime Switching Model

The goal of this study is to use the Markov Regime Model (MSM) to evaluate the impact of three important FCIs and the political economy on Pakistan’s economic growth. The current research used quarterly data from 2000 to 2020. The empirical results demonstrate that dictatorship contributes favorably to the high and low growth regime of the economy, while election time contributes positively to the expansion period and adversely in recession times of the economy. Results confirmed that external debt has a strong negative influence on economic growth in both the high and low-growth regimes. Remittances, FDI, and growth, on the other hand, have a positive and substantial link with economic growth. This research suggested that effective fiscal and monetary policies, as well as a suitable political environment, should be provided so that foreign investors would be enticed to invest in Pakistan.

Intelligent cryptocurrency trading system using integrated AdaBoost-LSTM with market turbulence knowledge

The Bitcoin market is firmly positioned as a global asset market. However, due to its extremely high volatility and the lack of a custom Bitcoin trading system, investors find it difficult to establish an effective investment strategy in this market. In this study, we build an intelligent Bitcoin trading system to maximize profitability by predicting the market. We propose a fusion approach combining technology with economic knowledge to achieve accurate predictions. Firstly, we provide an ensemble prediction framework that integrates the AdaBoost algorithm with the LSTM deep learning model as a form of technological convergence. This dramatically improves the predictive performance due to the high representational capacity of LSTM and the overfitting minimization capability of AdaBoost. Secondly, to account for the repeated structural volatility in the market, we combine the econometrics Markov regime-switching model with the AdaBoost-LSTM model to predict the period of market turbulence caused by structural breaks in the regime. Our prediction model suggests that market turbulence lasting longer than 21 days could be a potentially high-risk investment in the future. Finally, we have developed a Bitcoin-customized trading algorithm that maximizes rewards by predicting upward movements in prices and warning of high-risk investments during a long-lasting turbulent regime. In trading tests, our system yielded a cumulative return of 2.4 times and 4.0 times on 1-day and 5-day forecasts, respectively, compared to other representative trading strategies such as the stochastic oscillator.

A lognormal/normal regime-switching commodity pricing model

Inspired by the negative price of WTI crude oil observed during the COVID-19 pandemic, we develop a new model for commodity pricing which allows structural change between price normality and lognormality under a Markov regime-switching (RS) framework. We augment the Extended Kalman Filter to calibrate the structural changing model. The model performance in calibration is compared to that of the common RS model with historical WTI spots, various futures and hypothetical scenarios. We conclude that our model is superior in capturing price dynamics especially in the oil market downturns. Encouragingly, the regime probabilities estimated with the new model indicate that during severe events including the 2008–2010 financial crisis, 2014–2016 oil crash and the outbreak of COVID-19 in 2020, WTI spot itself follows normal rather than the widely assumed lognormal process. This finding is consistent with our empirical studies. In addition, we assess the probability density of spot prices with the new model. Finally, we present the PDE finite difference and Monte Carlo approaches to price commodity options under the new model.

A Study of Trade Strategies Based on the Markov Regime Switching Model

Market traders often buy and sell stocks to maximize their total return. For each purchase and sale, there is often a return commission. Having trading technology plays an important role in quantitative trading. In this paper, we first use the XGBoost model to learn the historical price fluctuation data of gold and bitcoin, and the prediction accuracy R2 is between 0.998 and 0.999, which is a good fit. Then LT algorithm and PS algorithm are used to identify the rules, the Markov regime switching model is used to determine the bear and bull market, and finally, the DQN model is used to plan the trading strategy and add a machine learning algorithm to it to optimize the strategy to get the initial $1000 in the future investment strategy.

The role of macroeconomic uncertainty in the determination of the natural rate of interest

We incorporate macroeconomic uncertainty into a Holston–Laubach–Williams (HLW)-type model of the natural real interest rate (NRI) for the US., where we allow for nonlinearities by employing a regime-switching model. We find macroeconomic uncertainty to strongly depress the NRI in periods mostly but not exclusively associated with US. recessions. These periods are longer lasting in the 1970s and 1980s and rather short-lived after 1990. Outside these periods, and particularly since the 1990s, we find the level of the NRI to be generally higher in comparison to the estimates by HLW.

How competitive is SRI in developing financial markets: The case of Central and Eastern Europe

This study investigates the competitiveness of sustainable and responsible investment (SRI) in Central and Eastern European (CEE) financial markets. Specifically, we examined whether a statistically significant measurable difference in the return and volatility between an SRI index and two conventional benchmark indices in the CEE region exists. To test whether the market volatility may affect the results, we applied a Markov regime-switching model to examine the performance in high and low volatility environments. We also used the Fama-French three-factor model to analyse the potential sources of outperformance and verify the initial analysis results. The analysis covered an eleven-year period (January 2011–December 2021) and was based on monthly returns of indices available on the Vienna Stock Exchange: CECE SRI, CECE Composite and CECE MID. Our findings showed that the SRI phenomenon in developing financial markets of the CEE countries followed performance patterns similar to ones in developed financial markets. Sustainable and responsible investment is competitive with conventional investment in the CEE region. However, the differences in returns between the SRI index and conventional benchmarks were statistically insignificant. Although a statistically significant difference in volatility between the SRI index and the large-cap CECE Composite index was reported, we did not find any evidence of exposure to the SRI factor regarding the analysed returns of the CECE SRI index. Our analysis of SRI returns pointed to the statistical significance of the common risk factors, such as the market and the size, which is similar to the analysed conventional benchmarks, with alpha not being statistically significant.

Occam’s razor, machine learning and stochastic modeling of complex systems: the case of the Italian energy market

In the spirit of Occam’s razor, we propose a parsimoniuos regime-switching model for describing the complex dynamics of electricity and natural gas prices observed in real markets. The model was built using a machine learning-based methodology, namely a cluster analysis to investigate the properties of the stable dynamics and a deep neural network appropriately trained on market data to drive transitions between different regimes. The main purposes of this study are twofold: (1) to build the simplest model capable of incorporating the main stylized facts of electricity and natural gas prices, including dynamic correlation; (2) to define an appropriate calibration procedure on market data. We applied this methodology to the Italian energy market. The results obtained show remarkable agreement with the empirical data, satisfactorily reproducing the first four moments of the empirical distributions of log-returns.

What drives cryptocurrency returns? A sparse statistical jump model approach

We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.

The behavior of the real effective exchange rate of Ethiopia: does misalignment exist?

ABSTRACT This study examined the main drivers of real effective exchange rate (REER) misalignment for Ethiopia over the period 1970–2020. Based on the behavioural equilibrium exchange rate concept, the study used the dynamic OLS and Markov regime switching model (MSM). The cointegration results revealed that the REER appreciates for an increase in relative productivity, government spending and net remittances, but it depreciates for more trade openness and real investment in the long-run. The MSM results also suggested a distinct misalignment episodes of under- and overvaluation with significant variability for the Ethiopia’s Birr over the study period.