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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.

The purpose of this paper is to pay more attention to four different research questions at least. One is that this study intends to explore the changes of the risk-return relationship over time, because the institutions and environment have changed a lot and might tend to influence the risk-return regime in the Chinese stock markets. The second question is whether there is any difference for the risk-return relationship between Shanghai and Shenzhen stock markets. The third question is to compare the similarities and dissimilarities of the risk-return tradeoff for different frequency data. The fourth question is to compare the explanation power of different GARCH-M type models which are all widely used in exploring the risk-return tradeoff.

This paper investigates the risk-return tradeoff in the Chinese emerging stock markets with a sample including daily, weekly and monthly market return series. A group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. Additionally, some diagnostic checks proposed by Engle and Ng (1993) are used in this paper, and this will help to assess the robustness of different models.

The empirical results show that the dynamic risk-return relationship is quite different between Shanghai and Shenzhen stock markets. A positive and statistically significant risk-return relationship is found for the daily returns in Shenzhen Stock Exchange, while the conditional mean of the stock returns is negatively related to the conditional variance in Shanghai Stock Exchange. The risk-return relationship usually becomes much weaker for the lower frequency returns in both markets. A further study with the sub-samples finds a positive and significant risk-return trade-off for both markets in the second stage after July 1, 1999.

This paper extends the existing related researches about the Chinese stock markets in several ways. First, this study uses a longer sample to investigate the relationship between stock returns and volatility. Second, this study estimates the returns and volatility relationship with different frequency sample data together. Third, a group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. In particular, the author employs the Component GARCH-M model which is relatively new in this line of research. Fourth, this study investigates if there is any structural break affecting the risk-return relationship in the Chinese stock markets over time.

The current paper aims to fill a gap in the literature by analyzing the nature of volatility on the Karachi Stock Exchange (KSE) 100 index of the KSE, and develop an understanding as to which model is most suitable for measuring volatility among those used. The study contributes significantly to the literature as, compared with the limited previous studies of Pakistan undertaken in the past, it covers three types of data (i.e. daily, weekly and monthly) for the whole period from the introduction of the KSE 100 index on November 2, 1991 to December 31, 2013. In addition, to analyze the impact of global financial crises upon volatility, the data have been divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods.

This study has used an advanced set of volatility models such as autoregressive conditional heteroskedasticity [ARCH (1)], generalized autoregressive conditional heteroskedasticity [GARCH (1, 1)], GARCH in mean [GARCH-M (1, 1)], exponential GARCH [E-GARCH (1, 1)], threshold GARCH [T-GARCH (1, 1)], power GARCH [P-GARCH (1, 1)] and also a simple exponentially weighted moving average (EWMA) model.

The results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity and volatility clustering. However, the heteroskedasticity is absent only in the monthly returns making only the EWMA model usable to measure the volatility level in the monthly series. The P-GARCH (1, 1) model proved to be a better model for modeling volatility in the case of daily returns, while the GARCH (1, 1) model proved to be the most appropriate for weekly data based on the Schwarz information criterion (SIC) and log likelihood (LL) functionality. The study shows high persistence of volatility, a mean reverting process and an absence of a risk premium in the KSE market with an insignificant leverage effect only in the case of weekly returns. However, a significant leverage effect is reported regarding the daily series of the KSE 100 index. In addition, to analyze the impact of global financial crises upon volatility, the findings show that the subperiods demonstrated a slightly low volatility and the global economic crisis did not cause a rise in volatility levels.

Previously, the literature about volatility modeling in Pakistan’s markets has been limited to a few models of relatively small sample size. The current thesis has attempted to overcome these limitations and used diverse models for three types of data series (daily, weekly and monthly). In addition, the Pakistani economy has been beset by turmoil throughout its history, experiencing a range of shocks from the mild to the extreme. This paper has measured the impact of those shocks upon the volatility levels of the KSE.

This paper aims to examine the risk premium for investors in a changing information environment in the Taiwan, New York and London real estate markets from March 2006 to November 2014. This study attempts to quantify behavioral expectations regarding (or motivation for) investment in the Taiwanese real estate in a changing information environment.

This paper uses the rolling generalised autoregressive conditionally heteroskedastic in mean (GARCH-M) methodology which fixes the problem of conventional GARCH-M methodology.

Empirical evidence suggests that the time-varying risk premium changed for the Taiwan real estate market with a new information set. The risk premium changed from 1.305 per cent per month to −7.232 per cent per month. The study also found persistent volatility shocks from March 2006 to November 2014. No such evidence was found for the New York and London real estate markets. Overall, this study finds evidence of a time-varying risk premium, partly explainable by governmental policies and partly unexplainable.

The use of the index of Standard and Poor’s Taiwan Real Estate Investment Trusts to study the Taiwan real estate industry may have aggregation effects in result.

The present study will provide guidance to investors as well as policymakers regarding the Taiwan real estate market.

This study uses the rolling GARCH-M model, which is a first for the Taiwan real estate market.

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.

In any stock market, volatility is a significant factor in strengthening their asset pricing. The upsurge in volatility in the stock market can activate and bring changes in the financial risk. According to financial conventional theory, the stakeholders (investors) are selected to be balanced and variations in pertinent risk are also to be anticipated due to the outcome of the drive-in basic factors in Indian stock markets. The hypothesis shows that there are actions in systematic and unsystematic risks that are determined by volatility. It is allied to sentiment-driven in the trader movement.

The paper used the methodology of generalized autoregressive conditional heteroskedasticity-in mean GARCH-M and exponential GARCH-M (E-GARCH-M) methods on the Indian stock market. The data have been covered from 2000 to 2019.

Finally, the study suggests that due to the unfitness of the capital asset pricing model (CAPM), the selection has enhanced with sentiment is an important risk factor.

The investor sentiment and stock return volatility statement are established by using the investor sentiment amalgamated stock market index built.

The outcome of the study shows that there is an important association between stakeholder (investor) sentiment and stock return, in case of volatility behavioural finance can significantly explain the behaviour of stock returns on the Indian Stock Exchange.

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 study aims to determine the mutual association between the volatility of macroeconomic indicators (MIs) and India’s tourism demand.

Bivariate generalized autoregressive conditional heteroscedasticity (GARCH) models are applied to estimate the volatility spillover effect (VSE) from one market to another. Compared to the other methods, bivariate GARCH has wide acceptance for estimating the VSE. The monthly MIs and tourism demand data (2012–2021) are gathered for empirical analysis.

The evidence of the growth-led tourism (GLT) demand is seen. In the short term, tourism-led growth (TLG) is indicated. However, this TLG does not sustain itself in the long run. There is significant evidence in favour of the VSE from the MIs to the tourism demand ensuring GLT in India.

The main implication of the current study is to ignore the short-term influence of tourism demand on the economy because it does not sustain itself in the long run. However, the long-term influence of macroeconomic indicators on tourism demand should be seen with caution. Hedging, if possible, may be considered to protect the tourism sector’s interests from adverse economic fallouts.

There is a lack of studies on the volatility (especially on the VSE) between MIs and tourism demand. Hence, this study fills the research gap and presents a novel and unique contribution to the extent of the knowledge body on the topic and significantly contributes.

双变量GARCH模型用于估计从一个市场到另一个市场的波动溢出效应(VSE)。与其他方法相比, 双变量GARCH在估计波动溢出效应时得到了广泛的接受。收集2012-2021年的月度管理信息系统和旅游需求数据进行实证分析。

该研究旨在确定宏观经济指标(MIs)的波动与印度旅游需求之间的相互关系。

GLT(增长主导的旅游需求)的证据显而易见。从短期来看, 旅游导向型增长(TLG)可行。然而, 这种旅游导向型增长并不能长期维持下去。有重要的证据支持印度管理信息系统到旅游导向型增长的旅游需求波动溢出效应。

当前研究的主要启示是忽略了旅游需求对经济的短期影响, 因为从长远来看, 它无法自我维持。然而, 宏观经济指标对旅游需求的长期影响应谨慎看待。如有可能, 可考虑对冲, 以保护旅游业的利益不受不利的经济影响。

目前对管理信息需求与旅游需求之间的波动（尤其是波动溢出效应）的研究较少。因此, 本研究填补了这个研究空白, 并对该主题知识体系的内容呈现新颖而独特的促进作用, 有显著的贡献作用。

Los modelos GARCH bivariantes se aplican para estimar el efecto indirecto de la volatilidad (VSE) de un mercado a otro. En comparación con otros métodos, el GARCH bivariante goza de gran aceptación para estimar el VSE. Para el análisis empírico se recopilan los MI mensuales y los datos de demanda turística (2012–2021).

El estudio se centra en medir la relación mutua entre la volatilidad de los indicadores macroeconómicos (MI) y la demanda turística de la India.

Se observan indicios de GLT (demanda turística impulsada por el crecimiento). A corto plazo, se evidencia el TLG (crecimiento impulsado por el turismo). Sin embargo, este TLG no se mantiene a largo plazo. Existen pruebas significativas a favor del VSE de los MI a la demanda turística que garantizan el GLT en India.

La principal implicación del presente estudio es desestimar la influencia a corto plazo de la demanda turística en la economía porque no se sostiene a largo plazo. Sin embargo, la influencia a largo plazo de los indicadores macroeconómicos en la demanda turística debe considerarse con cautela. Por ello, la cobertura de riesgos puede plantearse para proteger los intereses del sector turístico de las repercusiones económicas adversas.

Existe una carencia de estudios sobre la volatilidad (especialmente en el VSE) entre los MI y la demanda turística. En consecuencia, este estudio realiza una aportación investigadora mediante una contribución novedosa y única en la ampliación del conocimiento sobre el tema de análisis.

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 analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research.

The author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding.

This work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily.

The author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals.

To the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.