Regime-switching Model Research Articles

Integrating Complex Derivative Models in Understanding Dollarization’s Impact on Sustainable Development Goals and Environmental Economics: A Neo-Classical Finance Perspective

Dollarization has major implications for sustainable development and environmental economics in developing countries. This paper integrates complex derivative models from neoclassical finance theory, including stochastic volatility, copula, and regime-switching models, to quantitatively analyze the impacts of dollarization. The models examine effects on poverty, inequality, economic growth, natural capital investment, and green bond financing access related to sustainable development goals and environmental economics. Despite limitations in assumptions, the models provide useful risk quantification. Results suggest dollarization exacerbates volatility and negative externalities, hindering sustainability objectives. The integration of derivative modeling and development economics provides an analytical framework for examining dollarization, indicating potential gains from gradual de-dollarization policies. Further empirical research is warranted to validate the theoretical insights.

Fiscal and Monetary Policies Interaction in Crisis: An Insight Into Japan

In this study, we explore the interaction between fiscal and monetary policies in Japan over a period including the Global Financial Crisis (GFC) and a phase of the Covid-19 pandemic under the presupposition that policy regimes are fixed and there are random switches between the two regimes. Using the Regime switching model, the results show that fiscal policy is more active in Japan than monetary policy, especially during the GFC and the Covid-19 pandemic. By using the Bayesian VAR model, we continue to further analyze the response of macroeconomic factors to an expansionary fiscal policy to verify the above findings once again. The results indicate that government expenditure helps boost the economy in the short-run. The positive impact of a government expenditure shock on GDP, PPI, and Investment lasts about three to four quarters, while private consumption only increases in one next quarter. These positive responses can increase the inflation rate and make the local currency overvalued, reflected in the increase in the effective exchange rate. A fiscal policy shock does not have much impact on the monetary policy, specifically, the money supply and interest rates of long-term government bonds still tended to decrease as expected by Japan. This is the basis for studying policy implications for some countries pursuing similar policies to Japan. JEL Classification: C23, C54, E42, E62, E63

Business decision-making of power generators in competitive electricity market

This paper presents a theoretical framework for the business decision-making process of the power generators as price takers when considering the participation of energy storage. The framework assesses rational valuation, optimal sales strategies, and hedging options for power plants with and without a gross sales constraint. The valuation and optimal sales strategy problems are analyzed using a risk-neutral pricing approach, dynamic programming principles, and the trinomial tree model suitable for the regime switching model. The formulation of a price risk hedging scheme flexible and widely used over-the-counter electricity derivative, the electricity contract for difference, as a tool for hedging electricity spot price risk. The minimum variance hedge ratio and its corresponding hedging efficiency formula are derived. In the section of numerical simulations, we first use the EM algorithm to calibrate the electricity spot model based on electricity spot price data of Nord Pool. Numerical simulations are then conducted on the operational decision-making of power generators under three different forms of energy storage. The results of the simulations provide a basis for power generators to evaluate the real-time value of power plants, to select optimal real-time power sales, and to determine the optimal timing of power plant transfer and storage methods.

A Dynamic Regime-Switching Model Using Gated Recurrent Straight-Through Units

A Dynamic Regime-Switching Model Using Gated Recurrent Straight-Through Units

Impact of Socioeconomic Globalization and Institutional Quality on Human Capital Development in Nepal: A Markov Regime Switching Analysis

Human capital development is a catalyst for the economic growth of a nation. This study employed the Markov regime-switching model to examine the impact of socioeconomic globalization and institutional quality on human capital development in Nepal, using data from 1980 to 2018. The findings revealed that social globalization promotes human capital development in Nepal, whereas institutional quality impedes it. Additionally, economic globalization either negatively impacted or did not significantly contribute to human capital development in both regimes. Financial development was found to severely hamper human capital development, while inflation had a positive effect in both regimes. The results offer insights that the quality of institutions within the globalized context of Nepal is crucial for enhancing human capital development. This improvement could stimulate private-sector financing and alleviate the crowding-out effects of government spending and policy shifts. Consequently, the policy implication of this research is that Nepal should prioritize enhancing institutional quality and fostering social globalization while addressing the adverse effects of economic globalization and financial development to bolster human capital development.

Determinants of Monetary Policy: An Application of Regime Switching Model

The monetary policy of India has undergone substantial changes since 2013, particularly after taper tantrum episodes. The RBI moved from WPI to CPI for measuring inflation. Monetary policy anchoring shifted from a multiple-indicator approach (MIA) to flexible inflation targeting (FIT) in 2016 to deal with the aftermath of the global financial crisis. In this context, the study examined whether there was a significant shift in the monetary policy regime using a non-linear model (Markov Switching-Vector Autoregression Model) covering monthly data from 2013 to 2020. The study also investigated the role of the inflation rate, oil price, growth rate, and exchange rate in influencing monetary policy in India. The study found no change in the RBI monetary policy regime, and monetary policy remained biased towards controlling the inflation rate.

Do human capital and institutional quality contribute to Brazil's long term real convergence/divergence process? A Markov regime-switching autoregressive approach

Abstract This paper assesses Brazil's real convergence (1822–2019) through unit root tests and Markov Regime-Switching (MS) models in three different scenarios: towards (i) other six Latin American countries (LA6); (ii) Portugal; and (iii) the technological frontier country, the US. The extended unit root test results favour Brazil's very long-run real convergence towards LA6 and Portugal, but not the US. The estimated MS models, involving two different regimes, real convergence and real non-convergence/divergence, capture institutional quality's positive effect in promoting Brazil's real convergence.

Should You Use GARCH Models for Forecasting Volatility? A Comparison to GRU Neural Networks

Abstract The GARCH model is the most used technique for forecasting conditional volatility. However, the nearly integrated behaviour of the conditional variance originates from structural changes which are not accounted for by standard GARCH models. We compare the forecasting performance of the GARCH model to three regime switching models: namely, the Markov Switching GARCH, the Hidden Markov Model, and the Gated Recurrent Unit neural network. We define the number of optimal states by means of three methods: piecewise linear regression, Baum–Welch algorithm and Markov Chain Monte Carlo. Since forecasting volatility models face the bias-variance trade-off, we compare their out-of-sample forecasting performance via a walk-forward methodology. Moreover, we provide a robustness check for the results by applying k-fold cross-validation to the original time series. The Gated Recurrent Unit network is the best suited for volatility forecasting, while the Hidden Markov Model is the best at discerning the market regimes.

Coupled Price–Volume Equity Models with Auto-Induced Regime Switching

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.

Interest rate risk of Chinese commercial banks based on the GARCH-EVT model

Interest rate market risk faced by China’s commercial banks is increasing after the announcement that the interest rate marketisation is completed. This paper examines the Value-at-Risk, and statistical properties in the daily price return of Shanghai banks’ overnight offered rate. The study applies two-stage approaches, combining GARCH-type models with extreme value theory. Firstly, the Markov regime switching model is used to test the regime states of the series. Secondly, the performance of different VaR models are examined. Results show that the extreme value approach estimates better at the 99% confidence level. The EGARCH-GED model is the most suitable of the employed GARCH-type models. The back-testing results support the idea that the approach used in this study is appropriate for improving commercial banks’ daily risk management. This paper applies the GARCH-EVT method for interest rate measurement after China’s interest rate marketisation and added regime analysis of interest rate. Suggested policy implications will help formulate policies that guide the activities of commercial banks in China.

Is gold a hedge or a safe haven against stock markets? Evidence from conditional comoments

In this study, we estimate the conditional correlation and coskewness between gold and stock returns using a bivariate regime-switching model. Motivated by Yang, Zhou, and Wang (2010), we define gold as a strong hedge if the average correlation is negative and the coskewness is positive in the sample. Gold is a strong safe haven if these hold under market turmoil. We empirically examine the property of gold in 24 countries for a sample period spanning over 40 years and find that gold acts as a strong hedge and safe haven in Brazil, India, Indonesia, Italy, Mexico, Russia, South Korea, and Turkey. The interplay between cultural characteristics and the state of financial markets collectively defines gold's role in various countries. We construct a conditional comoment-based dynamic trading strategy and add gold to the stock portfolio when it can serve as a hedge or safe haven. Its out-of-sample performance dominates the buy-and-hold and correlation-based strategies, especially when we consider the safe haven property of gold.

부산 경남지역의 지역내총생산 동조성에 관한 연구

This study examined the synchronicity of economic activities between Busan and Gyeongnam (including Ulsan) using per capita regional gross domestic product data from Busan and Gyeongnam from 1985 to 2020. Synchronization of economic activities is a phenomenon in which major macroeconomic variables move similarly at the same time or over time, and can be analyzed through cointegration analysis and Granger causality analysis. First, we examined whether a long-term equilibrium relationship exists between the regional gross domestic product of Busan and Gyeongnam through a cointegration test considering structural changes. In addition, we examined the mutual predictive power between regional GDP growth rates through the Markov regime-switching model. As a result, a structural change occurred in the long-run stable relationship was found. In the case of the cointegration test method reflecting the structural change, it was found that there is a long-run stable relationship between GRDP of Busan and Gyeongnam. In the Markov phase-switching model, GRDP of the two regions had synchrony in the long run regardless of the economic phase, and there was synchronicity with high mutual predictive power between the rates of change in the GRDP of Busan and Gyeongnam. The synchronicity of economic activities in Busan and Gyeongnam is adequately explained by the method considering the structural change and the nonlinear model.

Inference and forecasting phase shift regime of COVID-19 sub-lineages with a Markov-switching model.

The occurrences and domination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are still crucial factors for determining the coronavirus disease 19 (COVID-19) policies. We collected weekly Phylogenetic Assignment of Named Global Outbreak sub-lineages, naming genetically distinct lineages of SARS-CoV-2, including variants of concern, in the United Kingdom, South Africa, South Korea, Denmark, Germany, the United States, and worldwide. This study included 12,296,756 samples of the max share of the sub-lineages from the 33rd week of 2020 to the 40th week of 2022. This study conducted a two-state Markov-switching model to estimate the probability of the phase shift state and predicted the probability of each regime with the Hamilton filter and Kim's smoothing algorithm. We discovered different weekly patterns based on dominant SARS-CoV-2 variants in target area. Due to differences in containment policies and outbreak waves, we observed a time lag in dominant variants in these countries. Using the inferred probability of the phase shift regime for forecasting, it showed significant probabilities that the stable phase will still be stable in the next week. It also showed significant probabilities that the unstable phase will still be unstable in the next week. Our findings present the probability of observing the phase shift regime each week. Until a new SARS-CoV-2 variant occurs, the regime tended to stay with a low probability of phase shift regime. When a new SARS-CoV-2 variant would occur, the regime would immediately react and change the probability. We propose the Markov-switching model to determine COVID-19 policies and predict SARS-CoV-2 variants. IMPORTANCE Using regime-switching models, we attempted to determine whether there is a link between changes in severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) variants and infection waves, as well as forecasting new SARS-Cov-2 variants. We believe that our study makes a significant contribution to the field because it proposes a new approach for forecasting the ongoing pandemic, and the spread of other infectious diseases, using a statistical model which incorporates unpredictable factors such as human behavior, political factors, and cultural beliefs.

Pricing European Vulnerable Options with Jumps and Stochastic Default Obstacles Barrier under Regime Switching

In this paper, we propose an enhanced model for pricing vulnerable options. Specifically, our model assumes that parameters such as interest rates, jump intensity, and asset value volatility are governed by an observable continuous-time finite-state Markov chain. We take into account European vulnerable options that are exposed to both default risk and rare shocks from underlying and counterparty assets. We also consider stochastic default barriers driven by a regime-switching model and geometric Brownian motion, thus improving upon the assumption of fixed default barriers. The risky assets follow a related jump-diffusion process, whereas the default barriers are influenced by a geometric Brownian motion correlated with the risky assets. Within the framework of our model, we derive an explicit pricing formula for European vulnerable options. Furthermore, we conduct numerical simulations to examine the effects of default barriers and other related parameters on option prices. Our findings indicate that stochastic default barriers increase credit risk, resulting in a decrease in option prices. By considering the aforementioned factors, our research contributes to a better understanding of pricing vulnerable options in the context of counterparty credit risk in over-the-counter trading.

A Study on Assessing the Forecasting Performance of ARIMA and Regime Switching Model for AUM of Various Types of Mutual Funds in India

A Study on Assessing the Forecasting Performance of ARIMA and Regime Switching Model for AUM of Various Types of Mutual Funds in India

A Study on Assessing the Forecasting Performance of ARIMA and Regime Switching Model for AUM of Various Types of Mutual Funds in India

A Study on Assessing the Forecasting Performance of ARIMA and Regime Switching Model for AUM of Various Types of Mutual Funds in India

The application of blockchain and robo-advisors in wealth management literature review

This paper aims to study the blockchain in the field of financial ecology as the carrier, optimize the consensus mechanism, and use intelligent consulting as an analytical means to provide investors with an objective, low-cost asset allocation portfolio. This article begins with an introduction to the features of blockchain decentralization and tamper-proof execution of algorithms, how proof-of-work works, and how tokens can improve welfare and reduce user base volatility. The paper then introduces how robo-advisors work and how they develop. Finally, this paper reviews existing research models on robo-advisors, from the traditional mean-variance model based on Markowitz to the jump-diffusion, regime-switching model, and the Pi portfolio management model that does not require quantifying risk preference coefficients, which this paper discusses and seeks to explore the advantages and limitations between the different models. Based on the existing research gaps, the directions that digital finance can expand in the future are discussed.

A Study on Assessing the Forecasting Performance of ARIMA and Regime Switching Model for AUM of Various Types of Mutual Funds in India

A Study on Assessing the Forecasting Performance of ARIMA and Regime Switching Model for AUM of Various Types of Mutual Funds in India

International stock market volatility: A data-rich environment based on oil shocks

This paper investigates the predictive ability of oil shocks for international stock market volatility based on a data-rich environment. Our empirical analysis shows that multiple oil shock measures contain valuable information for predicting stock market volatility, in addition to traditional economic variables and uncertainty indices. Moreover, based on the group 7 countries, the least absolute shrinkage and selection operator method and regime-switching model jointly deliver incremental improvement in forecasting accuracy from both statistical and economic perspectives. These results are confirmed by robustness checks under different business cycles and market conditions, including the COVID-19 pandemic.

Valuation of option price in commodity markets described by a Markov-switching model: A case study of WTI crude oil market

This paper suggests a Markov-switching model to evaluate commodity futures and spot dynamics, such that the diffusion coefficients and jump size parameter are associated with a hidden Markov chain. We improve the current models in the literature of the commodity markets by modeling the sudden jumps in the commodity prices through the hidden Markov chain. From the crude oil spot price in West Texas Intermediate, we estimate the parameters of proposed Markov-switching model based on expectation–maximization algorithm. To perform this task, we apply this estimation algorithm to the model discretized by Euler scheme and provide some convergence analysis for this discretization method. There are options, such as European options, which are written on the commodity futures. In this study, we evaluate them under the regime-switching model with various economic states. In the following, we calibrate the option prices resulting from the proposed commodity model to a set of observed European call options written on crude oil futures. For this purpose, we first apply an inverse Fourier transform and obtain a semi-analytical option pricing formula. Then, we use the fast Fourier transform method to compute option prices. Since the investors need to calculate Greeks in order to understand the risk involved in option investments, the Greek formulas of Delta, Rho, Theta, and Gamma are derived.