Regime-switching Effects Research Articles

Optimal Asset Allocation of Pension Funds with a Portfolio Constraint Change

We investigated the asset allocation problem of a pension fund in which the constraints on the risky investment proportion change owing to local regulations or the fund’s policy changes. We assumed that the fund manager has a utility function with respect to the funding ratio of the pension fund. A dynamic programming approach was employed and we obtained a closed-form solution to the problem. To numerically measure the effect of such changes on asset allocation, we define the concept of policy change equivalent return (PCER) as the additional expected return for the value function of the case without such changes at the same level as the value function of the case with them. In the numerical analysis, we analyzed both the effect of regime switching on market parameters and policy change on the PCER and showed the effectiveness of introducing policy changes to asset allocation.

The effect of regime-switching transaction costs and cash dividends on liquidity premia

We investigate the effect of the regime-switching transaction costs and dividends on liquidity premium and investor’s optimal strategy. With reasonably calibrated parameters, we show that counter-cyclical transaction costs substantially raise liquidity premium while pro-cyclical dividends amplify this effect. More importantly, we observe that cash dividends can no longer play a role as a liquidity provider if the volatility of pro-cyclical dividends increases. Our model provides a universal framework in the liquidity contexts, so that we can examine the effect of regime switching on liquidity premium for all combinations of regimes in transaction costs and dividends.

Advancing the affordable and clean energy in large energy-consuming economies: The role of green transition, complexity-based, and geostrategy policies

With decreasing costs of the clean technologies, the balanced scales of the Sustainable Development Goal 7 targets, e.g., energy equity (EE), energy security (ES), and environmental sustainability (EVS), are quickly changing. This fundamental balancing process is a key requirement for a net-zero future. Accordingly, this research analyzes the regime-switching effect of Hydrogen economy as the green transition, sharing economy and economic complexity as the complexity-based, and geopolitical risks and energy prices as the geostrategy policies on the Goal 7 targets. To this end, a Markov-switching panel vector autoregressive method with regime-heteroskedasticity is applied to study advancing the Goal 7 in the world's twenty-five large energy consumers during 2004–2020. Concerning the parameters and statistics of the model, the results refer to the existence of two regimes associated with the Goal 7 corners, called “upward and downward” regimes for EE and “slightly upward and sharply upward” regimes for ES and EVS. It is revealed that the vulnerability of EE and ES targets is considerably reduced when the regime switches to the dominant regime that is “downward” and “slightly upward” regimes respectively, and that of the EVS target remains unaffected. Through the impulse-response analysis, the findings denote that the first hypothesis of the efficiency of the Hydrogen economy in promoting the Goal 7 targets is insignificant. However, the significant short-term and dynamic shock effects of the complexity-based and geostrategy policies on the Hydrogen economy are detected, which will be a feasible alternative assessment in advancing the Goal 7. Further, the complexity-based policies support the Goal 7 targets under different regimes, especially in the short- and medium-term. Hence, the second hypothesis regarding the effectiveness of the complexity-based policies in promoting Goal 7 targets is confirmed. The third hypothesis concerning the complexity of the impact of geostrategy policies on the Goal 7 targets is verified. Particularly, the switching process towards the Goal 7 may not necessarily be restricted by the geopolitical risks. Moreover, EE is supported through energy prices in the short-term under both regimes, while they are non-conductive to promote ES and EVS through time. Accordingly, the decision-makers should acknowledge adopting a regime-switching path forward for ensuring the time-varying balanced growth of the Goal 7 targets as the impact of the suggested policy instruments is asymmetric.

Interpretable Transformer Model for Capturing Regime Switching Effects of Real-Time Electricity Prices

Real-time electricity prices are economic signals incentivizing market players to support real-time system balancing. These price signals typically switch between low- and high-price regimes depending on whether the power system is in surplus or shortage of generation, which is hard to capture. In this context, we propose a new Transformer-based model to assist the short-term trading strategies of market players. The proposed model offers high-performance probabilistic forecasts of real-time prices while providing insights into its inner decision-making process. Transformers rely on attention mechanisms solely computed via feed-forward networks to explicitly learn temporal patterns, which allows them to capture complex dependencies such as regime switching. Here, we augment Transformers with subnetworks dedicated to endogenously quantify the relative importance of each input feature. Hence, the resulting forecaster intrinsically provides the temporal attribution of each input feature, which foster trust and transparency for subsequent decision makers. Our case study on real-world market data of the Belgian power system demonstrates the ability of the proposed model to outperform state-of-the-art forecasting techniques, while shedding light on its most important drivers.

Sensitivity analysis with respect to a stochastic stock price model with rough volatility via a Bismut–Elworthy–Li formula for singular SDEs

In this paper, we show the existence of unique Malliavin differentiable solutions to SDE‘s driven by a fractional Brownian motion with Hurst parameter H<12 and singular, unbounded drift vector fields, for which we also prove a stability result. Further, using the latter results, we propose a stock price model with rough and correlated volatility, which also allows for capturing regime switching effects. Finally, we derive a Bismut–Elworthy–Li formula with respect to our stock price model for certain classes of vector fields.

Regime-switching effect of COVID-19 pandemic on stock market index: evidence from Turkey as an emerging market example

ABSTRACT This study investigates the regime-switching effect of the pandemic on the stock market index in Turkey. Daily data from 3 March 2020 to 31 August 2020 is used, four explanatory variables are included and Markov switching regression is applied. The empirical findings indicate that (i) the index has a long-term cointegration with the explanatory variables included; (ii) the new COVID-19 cases, credit default swap (CDS) spreads and foreign exchange (FX) rates are influential in the high-volatility regime, whereas FX rates are not influential in the low-volatility regime and (iii) net buying amounts of foreign investors are not effective in both regimes.

Threshold effect for the life insurance industry: evidence from OECD countries.

We investigate the impact of new financial and economic determinants on life insurance demand for 29 OECD countries for the period 2005–2017 while controlling for a set of widely used socio-demographic and economic characteristics. Based on a panel smooth transition regression model, we find a regime-switching effect characterising the impact of bank concentration and interest rate on the size of the life insurance market, in light of the old-age dependency ratio as the threshold variable. We also show that life insurance development is boosted in countries with high scores for investment freedom and with high levels of foreign direct investment rates, regardless of the level of the old-age dependency ratio. The impact of GDP per capita on the demand for life insurance products is positive and statistically significant, regardless of the level of the threshold variable.

Hybrid optimal impulse control

This paper is concerned with an optimal impulse control problem under a hybrid diffusion (or, regime switching) model, where the state of the system consists of a number of diffusions coupled by a continuous-time finite-state Markov chain. The objective is to minimize the expected discounted cost from exerting the impulse control in the infinite horizon. Based on the dynamic programming principle (DPP), the value function of the hybrid optimal impulse control problem is shown to be the unique viscosity solution to the associated Hamilton–Jacobi–Bellman (HJB) equation, which is in the form of a coupled system of variational inequalities. Moreover, a verification theorem as the sufficient condition for optimality of a solution is also established. The optimal impulse control, indicates when and how it is optimal to intervene, is described by the obstacle part of the HJB equation. Finally, the general theoretical results are applied to an optimal cash management problem. The value function in closed-form and an explicit optimal policy are obtained, which exhibit clearly the effect of regime switching on the agent’s decision.

Dynamics and optimal harvesting of a stochastic predator-prey system with regime switching, S-type distributed time delays and Lévy jumps

&lt;abstract&gt;&lt;p&gt;This work is concerned with a stochastic predator-prey system with S-type distributed time delays, regime switching and Lévy jumps. By use of the stochastic differential comparison theory and some inequality techniques, we study the extinction and persistence in the mean for each species, asymptotic stability in distribution and the optimal harvesting effort of the model. Then we present some simulation examples to illustrate the theoretical results and explore the effects of regime switching, distributed time delays and Lévy jumps on the dynamical behaviors, respectively.&lt;/p&gt;&lt;/abstract&gt;

Regime switching effect of COVID-19 pandemic on renewable electricity generation in Denmark

Denmark has achieved remarkable success in renewable energy generation over the last several decades. However, the country's goals of meeting its 50% energy demand from renewable by 2030 and becoming independent of fossil fuel by 2050 are currently in jeopardy due to the COVID-19 pandemic, which emerged at the end of December 2019 in the Chinese city of Wuhan. This study, therefore, tries to see how COVID-19 affects renewable electricity generation in Denmark using the advanced econometric framework. Several nonlinear estimation techniques such as Fourier ADL cointegration analysis and Markov Switching regression are used to estimate the relationship between the three channels of COVID-19 and renewable electricity generation. The result from the Markov Switching regression reveals that renewable electricity production in Denmark is adversely affected by the enforced lockdown as captured via the stringency index, economic support provided to tackle the pandemic, and daily confirmed deaths of COVID-19. Moreover, the causality test shows that the stringency index and daily confirmed deaths of COVID-19 are important predictors of renewable electricity, but the economic support index has weak causality with renewable electricity. The study finally presents some crucial policy suggestions for Denmark which can help the country achieve its renewable production goals.

Fundamental pricing laws and long memory effects in the day-ahead power market

We apply linear and nonlinear panel models to investigate fundamental pricing laws in the Greek day-ahead electricity market. We link the persistence behaviour and other dynamic properties of electricity price trajectories to an extended array of fundamental variables reflecting market conditions and the price course of energy commodities in global markets. Assuming that the dynamics of the intraday power pricing curve is adequately specified within a panel modelling framework with long memory and common unobserved stochastic effects, we are able to provide consistent estimates of price responsiveness to the main fundamental drivers and thus avoid biases induced by other popular modelling approaches. We also device a panel variant of the standard smooth-transition regression model to investigate regime switching effects in the price responsiveness mechanism. Empirical results suggest that Greek day-ahead electricity market prices respond significantly to hour-specific (load, marginal generation capacity) and global (Brent price, emission allowance rights cost) fundamental variables. In some intraday power delivery zones, these covariates are in a fractional cointegrating relationship with electricity prices being responsible for their drifting behaviour. The strength and type of cointegration changes across intraday trading periods in response to market conditions. The results of the nonlinear model demonstrate the ineffectiveness of linear model designs (either univariate or panel) to accurately estimate the levels of price risk and associate it with changes in the market climate.

Fundamental Pricing Laws and Long Memory Effects in the Day-Ahead Power Market

We apply linear and nonlinear panel models to investigate fundamentals pricing relationships in the Greek day-ahead power market. We link the persistence behaviour and other dynamic properties of power price trajectories to an extended array of fundamental variables reflecting market conditions and the price course of energy commodities in global markets. Assuming that the dynamics of the intraday power pricing curve is adequately specified within a panel modelling framework with long memory and common unobserved stochastic effects, we are able to provide consistent estimates of price responsiveness to the main fundamental drivers and thus avoid biases induced by other popular modelling approaches. We also device a panel variant of the standard smooth-transition regression model to investigate regime switching effects in the price responsiveness mechanism. Empirical results support that Greek day-ahead prices are in a fractional cointegrating relationship with hour-specific covariates (load, marginal generation capacity) as well as price determinants affecting the entire cross-section of hourly contracts (Brent price, emission allowance rights cost). The strength and type of cointegration often changes in a nonlinear way with the hour of power delivery or in response to market conditions. Our study has important implications for price risk management. The results of the nonlinear model demonstrate the ineffectiveness of linear models (either univariate or in a panel context) to accurately estimate the levels of price risk and associate it with changes in market conditions.

Regime‐dependent effects of uncertainty shocks: A structural interpretation

Using a Markov‐switching VAR, we show that the effects of uncertainty shocks on output are four times higher in a regime of economic distress than in a tranquil regime. We then provide a structural interpretation of these facts. To do so, we develop a business cycle model in which agents are aware of the possibility of regime changes when forming expectations. The model is estimated using a Bayesian minimum distance estimator that minimizes, over the set of structural parameters, the distance between the regime‐switching VAR‐based impulse response functions and those implied by the model. Our results point to worsening credit‐market conditions that amplify shocks during distress periods. Finally, we show that the expectation effect of regime switching in financial conditions is an important component of the financial accelerator mechanism. If agents are more pessimistic about future financial conditions, then the output effects of shocks are amplified.

A unified option pricing model with Markov regime-switching double stochastic volatility, stochastic interest rate and jumps

We consider a general option pricing framework incorporating the double Heston stochastic volatility, stochastic interest rate, jumps and Markov regime switching. Under the proposed framework, we derive the analytical pricing formulae for European options using Fourier transform technique. Numerical examples illustrate that the option prices and the implied volatility curves under different regimes vary clearly, and the effects of regime-switching and jumps on the option price differ.

The asymmetric spillover effect of the Markov switching mechanism from the futures market to the spot market

This article develops a multichain Markov switching dynamic conditional correlation ARCH model with idiosyncratic jump dynamics to investigate whether the state of the crude oil futures market can asymmetrically affect the state of the crude oil spot market. The asymmetric spillover effects are investigated after controlling the dependence structure on idiosyncratic jumps. The empirical findings show an asymmetric spillover effect from the futures market to the spot market. Moreover, the transition probabilities depend highly on the volatility of the futures market, showing the leading role of the futures market. The jump components play a relatively more important role in explaining the conditional variances than do the ARCH and regime-switching effects. Finally, both the contribution of idiosyncratic jumps on total variance and the correlation coefficient between the futures and spot returns rely on the volatility state of the futures and spot returns. The second and fourth moments of the conditional correlation coefficients will be underestimated when the common jumps and/or independent transition mechanisms are ignored. The findings of this paper have important implications for investors in accurately evaluating riskiness, hedgers in improving hedging performance, as well as market participants and government authorities in understanding the lead-lag relationship between crude oil spot and futures markets.

A regime switching fractional Black–Scholes model and European option pricing

In this paper, we investigate the European option pricing problem under a regime switching FMLS (finite moment log-stable) model. This model is not only able to capture the main characteristics of asset returns, it also incorporates the effect of regime switching being consistent with market observations. However, option prices under this model are governed by a coupled FPDE (fractional partial differential equation) system, and the difficulty in seeking for analytical solution arises from the combination of the coupled system and the spatial-fractional derivative. To deal with this difficulty, we develop a two-step solution procedure; we firstly assume that the future information of the Markov chain is known, and we derive the conditional option price by analytically solving a time dependent FPDE, based on which an exact and explicit pricing formula for the unconditioned price is successfully worked out by using the Fourier cosine series expansion. It is also shown through the numerical experiments that it converges very rapidly and has potential to be applied in practice.

Regime switching effect of financial development on energy intensity: Evidence from Markov-switching vector error correction model

Studies that deal with financial development and energy intensity usually show the single effect of one variable on another or the causality running between them during the whole sample period. But this study is an attempt to explore the regime switching effect of financial development on energy intensity in different periods within the sample. Using yearly data from 1972 to 2017 in case of Bangladesh, a Markov-switching vector error correction model is applied to carry out the study, and the results reveal a two-regime switching nature of the effect of financial development on energy intensity. Financial development plays a stimulating role in regime 1 where it plays an inhibiting role in regime 2 to influence energy intensity. The duration of regime 1 is longer than that of regime 2 specifying that the promoting effect of financial development on energy intensity is more enduring than the inhibiting effect in Bangladesh. The results have important policy implications to formulate energy policy and to maintain a balance between financial development and energy intensity in Bangladesh and other emerging nations.

Regime-Dependent Effects of Uncertainty Shocks: A Structural Interpretation

Using a Markov-switching VAR, we show that the effects of uncertainty shocks on output are four times higher in a regime of economic distress than in a tranquil regime. We then provide a structural interpretation of these facts. To do so, we develop a business cycle model, in which agents are aware of the possibility of regime changes when forming expectations. The model is estimated using a Bayesian minimum distance estimator that minimizes, over the set of structural parameters, the distance between the regime-switching VAR-based impulse response functions and those implied by the model. Our results point to changes in the degree of financial frictions. We discuss the implications of this structural interpretation and show that the expectation effect of regime switching in financial conditions is an important component of the financial accelerator mechanism. If agents hold pessimistic expectations about future financial conditions, then shocks are amplified and transmitted more rapidly to the economy.

Three Essays on Nonlinear Time-series Econometrics

This thesis is submitted in three chapters. Chapter 1 considers a regime-switching Levy framework, where all parameter values depend on the value of a continuous time Markov chain as per Chevallier and Goutte (2017), is employed to study US Corporate Option-Adjusted Spreads (OASs). For modelling purposes we assume a Normal Inverse Gaussian distribution, allowing heavier tails and skewness. After the Expectation - Maximization algorithm is applied to this general class of regime switching models, we compare the obtained results with time series models without jumps, including one with regime switching and one without. We find that a regime-switching Levy model clearly defines two regimes for A-, AA-, and AAA-rated OASs. We find further evidence of regime-switching effects, with data showing relatively pronounced jump intensity around the time of major crisis periods, thereby confirming the presence and importance of volatility regimes. Results indicate that ignoring the complex and dynamic dependence structure in favour of certain model assumptions may lead to a significant underestimation of risk. To the best of our knowledge, this study is the first time that Markov-switching Levy models have been employed to analyze the structure of option-adjusted spreads. Chapter 2 considers a little-used dataset to study the Japan's Real Estate Investment Trusts (REITs) market. We make use of copula theory and test for dependence between equity and REIT returns using several popular copula families. The copula families in our specification were: Gaussian, Student-$t$, Clayton, Frank, and Gumbel. The p-values were computed using parametric bootstrap replications. We motivate the argument presented, and provide the goodness-of-fit test results based on the Rosenblatt transform. We find that the dependence between the innovations of JREIT Composite and JREIT Office returns can be modelled by a Student-$t$ copula with 2.7 degrees of freedom and correlation parameter $\hat{\nu}$ = 0.93. All other models are rejected. To the best of our knowledge, this study represents the first time that copula theory has been applied to study REITs in Japan. Chapter 3 critiques a recent contribution to the financial econometrics literature, namely Chu et al. (2017), which provides the first examination of the time-series price behaviour of the most popular cryptocurrencies. We argue that insufficient attention was paid to correctly diagnosing the distribution of GARCH innovations. When these data issues are controlled for, their results lack robustness and may lead to either underestimation or overestimation of future risks. The main aim of this paper therefore is to provide an improved econometric specification. Particular attention is paid to correctly diagnosing the distribution of GARCH innovations by means of Kolmogorov type non-parametric tests and Khmaladze's martingale transformation. Numerical computation is carried out by implementing a Gauss-Kronrod quadrature. Parameters of GARCH models are estimated using maximum likelihood. For calculating p-values, the parametric bootstrap method is used. Further reference is made to the merits and demerits of statistical techniques presented in the related and recently published literature.

Regime-Switching And Levy Jump Dynamics In Option-Adjusted Spreads

A regime-switching Levy framework, where all parameter values depend on the value of a continuous time Markov chain as per Chevallier and Goutte (2017), is employed to study US Corporate Option-Adjusted Spreads (OASs). For modelling purposes we assume a Normal Inverse Gaussian distribution, allowing heavier tails and skewness. After the Expectation-Maximization algorithm is applied to this general class of regime switching models, we compare the obtained results with time series models without jumps, including one with regime switching and one without. We find that a regime-switching Levy model clearly defines two regimes for A-, AA-, and AAA-rated OASs. We find further evidence of regime-switching effects, with data showing relatively pronounced jump intensity around the time of major crisis periods, thereby confirming the presence and importance of volatility regimes. Results indicate that ignoring the complex and dynamic dependence structure in favour of certain model assumptions may lead to a significant underestimation of risk.