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I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

This paper aims to examine the contemporary association between accounting information and a number of measures of systematic (beta) risk that incorporate dynamic market features. The goal is to determine the fundamental accounting drivers of beta and to assess whether their explanatory variable power has changed or declined over time.

Beta estimates are calculated using adjustments for thin‐trading, central tendency, leverage, and time variance. Accounting risk variables are derived from theoretical foundations and prior empirical research, and classified as operating, financial or growth.

Results show a strong association between accounting variables (operating and growth) and systematic risk that is consistent over time, but with some industry and size differences and possible country effects. Accounting variables are able to capture dynamic risk shifts and generally are able to outperform naïve M‐GARCH and industry betas in predicting next year's systematic risk.

Internal management and external decision making enable the development of more efficient ex‐post risk measures, isolating actual risk determinants rather than just determining the level of risk, overcoming the problem that conventional ex‐post measures cannot be used for non‐listed entities, initial public offering firms, or those that do not have sufficient trading history, reduces the noise found in traditional risk estimates that rely on historical security returns, and the development of trading and valuation strategies.

This is the first paper that assesses the association between a range of dynamic risk measures and accounting variables and tests whether this long‐run association has changed over time.

The risk management of transitory risk for real assets has gained large interest especially in the past 10 years among researchers as well as market participants. In addition, the recent regulatory tightening in the EU urges financial market participants to disclose sustainability-related financial risk, without providing any methodological guidance. The purpose of the study is the identification and explanation of the methodological limitations in the field of transitory risk modeling and the logic step to advance toward a stochastic approach.

The study reviews the literature on deterministic risk modeling of transitory risk exposure for real estate highlighting the heavy methodological limitations. Based on this, the necessity to model transitory risk stochastically is described. In order to illustrate the stochastic risk modeling of transitory risk, the empirical study uses a Markov Switching Generalized Autoregressive Conditional Heteroskedasticity model to quantify the carbon price risk exposure of real assets.

The authors find academic as well as regulatory urgency to model sustainability risk stochastically from a conceptual point of view. The own empirical results show the superior goodness of fit of the multiregime Markov Switching Generalized Autoregressive Conditional Heteroskedasticity in comparison to their single regime peer. Lastly, carbon price risk simulations show the increasing exposure across time.

The practical implication is the motivation of the stochastic modeling of sustainability-related risk factors for real assets to improve the quality of applied risk management for institutional investment managers.

The present study extends the existing literature on sustainability risk for real estate essentially by connecting the transitory risk management of real estate and stochastic risk modeling.

This paper aims to investigate how oil price uncertainty affects real gross domestic product (GDP) and industrial production in eight Central and Eastern European countries (CEEC).

In the research process, the authors use the Bayesian method of inference for the two applied methodologies – Markov switching generalized autoregressive conditional heteroscedasticity (GARCH) model and quantile regression.

The results clearly indicate that oil price uncertainty has a low effect on output in moderate market conditions in the selected countries. On the other hand, in the phases of contraction and expansion, which are portrayed by the tail quantiles, the authors find negative and positive Bayesian quantile parameters, which are relatively high in magnitude. This implies that in periods of deep economic crises, an increase in the oil price uncertainty reduces output, amplifying in this way recession pressures in the economy. Contrary, when the economy is in expansion, oil price uncertainty has no influence on the output. The probable reason lies in the fact that the negative effect of oil volatility is not strong enough in the expansion phase to overpower all other positive developments which characterize a growing economy. Also, evidence suggests that increased oil uncertainty has a more negative effect on industrial production than on real GDP, whereas industrial share in GDP plays an important role in how strong some CEECs are impacted by oil uncertainty.

This paper is the first one that investigates the spillover effect from oil uncertainty to output in CEEC.

In this work, we estimated the impact that the US VIX, economic policy and epidemic uncertainty indexes had on leisure and recreation stocks. We extended the current literature in two ways: first, we estimated the smoothed probabilities of being in ‘normal’ ( s = 1 ), ‘distress’ ( s = 2 ) and ‘crisis’ ( s = 3 ) episodes in the Refinitiv global leisure and recreation index. Then, we estimated the influence that the VIX and uncertainty indexes had on the generation of distress and crisis episodes in these stocks. By using logit regressions, we found out that only the US Economic policy uncertainty index is a detonator of distress and crisis episodes. We also found that the pandemic (COVID-19) news uncertainty has no significant and direct influence on the smoothed probabilities. Finally, and complementary to the current literature, we found that the volatility spillover effect from the S&P 500 to these stocks generates extreme volatility (crisis) episodes. Our results could be of use for practitioners and scholars and could provide a model to forecast distress and crisis episodes among leisure and recreation stocks. This model could be used for potential portfolio management or economic (tourism) policy purposes.

This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.

This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).

In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).

In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.

It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.

The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.

For the past decades, issues concerning the impact of economic integration on financial integration, especially exchange rate integration, has been criticized among several regions such as ASEAN. This chapter intends to: (i) test for the exchange rate integration among the ASEAN-5, including Indonesia, Philippines, Malaysia, Singapore, and Thailand, using panel data techniques; and (ii) determine the impact of economic integration on the level of exchange rate integration among the ASEAN-5 countries. The purchasing power parity (PPP) is tested using panel unit root tests on monthly data. The results confirm the PPP among the ASEAN-5 countries due to lower transaction costs from ASEAN agreements. The chapter applies Multivariate GARCH (M-GARCH) models using daily data to determine the level of exchange rate integration among the ASEAN-3, including Malaysia, Singapore, and Thailand. The results of panel cointegration tests using quarterly data of economic integration and exchange rate integration confirm the impact of international trade openness on exchange rate integration. With free trade agreements leading to lower trade barriers, lower transaction costs, and low transportation costs, the economic integration among ASEAN countries practically leads to a higher degree of exchange rate integration. The findings imply that trade liberalization has the strongest effect on the real exchange rate. As such, regulators of ASEAN countries should pay more attention to the exchange rate policies of each other because of the interdependence of their exchange rates.

The nexus of commodity prices with inflation is one of the main concerns for a nation's economy like India. The literature does not have enough volatility-based study, especially using the multivariate GRACH family of models to find a link between these two. It is the main reason for the conduct of this study. This paper aims to estimate the volatility effects of commodity prices on inflation.

For ten years (2011–2022), future prices of selected seven agriculture commodities and inflation indices (wholesale price index [WPI] and consumer price index [CPI]) are gathered every month. BEKK GARCH model (BGM) and DCC GARCH model (DGM) are employed to determine the volatility effect of commodity prices (CPs) on inflation.

The authors find that volatility's short-term (shock) impact on agricultural CPs to inflation does not exist. However, the long-term volatility spillover effect (VSE) is significant from commodities to inflation.

The study's findings have a significant implication for the policymakers to take a long-term view on inflation management regarding commodity prices. The findings can facilitate policy on the choice of commodities and the flexibility of their trading on the commodities derivatives market.

The findings of the study are unique. The authors do not observe any study on the volatility effect of agri-commodities (agricultural commodities) prices on inflation in India. This paper applies advanced techniques to provide novel and reliable evidence. Hence, this research is believed to contribute significantly to the knowledge body through its novel evidence and advanced approach.

This paper examines the price discovery process among the Korea stock index markets using the vector error correction model (VECM) and the multivariate generalized auto regressive conditional heteroskedasticity (M-GARCH) model. The minute-by-minute price series of the KOSPI200 index, KOSPI200 futures, and KODEX200 are cointegrated.

The empirical results are summarized as follows: First, VECM estimation results indicate that when the cointegrating relationship is perturbed by the arrival of ntis, the KODEX200(ETF) does not adjusted to restore equilibrium. This is the task of the KOSPI200 futures and spot. These two index securities use the KODEX200 to represent the ntioequilibrium price, with the KOSPI200 futures responding faster than the KOSPI200 spot. When the cointegrating relationship betweeiesOSPI200 spot and futues is perturbed by the arrival of ntis, the KOSPI200 spot does adjusted to restore equilibrium. Next, the results from the multivariate GARCH modes indicate that the volatilities of esOSPI200 spot and futures markets suggest unidirectiona1volatility spillover from KOSPI200 futures to KOSPI200 spot. KODEX200(ETF) volatilities spill over bothesOSPI200 spot and futures markets. and this happen in the reverse direction with a strong effect from the KODEX200 to KOSP200 futures and spot.

The overall findings indicate that the KODEX200(ETF) market dominates KOSPI200 futures and spot in the price discovery process. The regulation of Instutional traders on trading on futures markets explains its superior price discovery function.