Regime-switching volatility of six East Asian emerging markets

Wang, Ping; Theobald, Mike

doi:10.1016/j.ribaf.2007.07.001

Cited by 58 publications

(42 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous research on developed and developing financial markets (see, for example: Hamilton and Susmel, 1994;Gray, 1996;Edwards and Susmel, 2001;Canarella and Pollard, 2007;Wang and Theobald, 2008;Chen, 2009) show that Markov-switching models can produce superior in-sample fit and better forecasts of the conditional variance than many single-state models. In this paper, the empirical results are less clear.…”

Section: Resultsmentioning

confidence: 99%

“…1 Hamilton (1989Hamilton ( , 1990) developed a constant within-regime mean and variance Markov-switching model (hereafter referred to as the simple MS model), which has been subsequently used in one form or another within many empirical studies of financial time series (Bollen et al, 2000;Chen, 2009;Dahlquist and Gray, 2000;Garcia and Perron, 1996;Wang and Theobald, 2008). The most general version of the simple MS model is:…”

Section: Markov-switching Modelsmentioning

confidence: 99%

“…Since Pr *[χ q 2 N M] is strictly greater than Pr[χ q 2 N M], the Davies (1987) upper-bound test need only be conducted if the null is rejected using the quasi-LRT (Wang and Theobald, 2008). Although Davies' (1987) test provides an upper-bound on the actual significance level, and is therefore somewhat conservative (Garcia, 1998), it provides a more formal comparison of one versus two-state and twoversus three-state models.…”

Section: Markov-switching Modelsmentioning

confidence: 99%

“…Regardless of the many likely causes of stock market volatility (Abel, 1988;Adam et al, 2008;Aggarwal et al, 1999;Diebold and Yilmaz, 2007;Olsen, 1998;Schwert, 1989;Shiller, 1987), a reliable statistical model of stock return volatility is important for the pricing of equity derivative securities and effective hedging of stock market risk (Hamilton and Susmel, 1994;Wang and Theobald, 2008). In addition, changes in the co-movement of stock returns across international markets during high-and low-volatility periods have major implications for diversification strategies (Li, 2009;Ramchand and Susmel, 1998).…”

Section: Introductionmentioning

confidence: 99%

“…Markov-switching models often provide a better in-sample fit of the data or more accurate forecasts than the conventional single-state GARCH extensions (Bollen et al, 2000;Cai, 1994;Canarella and Pollard, 2007;Chen, 2009;Edwards and Susmel, 2001;Gray, 1996;Economic Modelling 45 (2015) 50-73 Hamilton and Susmel, 1994;Henry, 2009;Klaassen, 2002;Wang and Theobald, 2008). However, these results are on occasion statistically insignificant or inconclusive (Dueker, 1997), and are less clear when assessed through some purely economic loss functions (Marcucci, 2005).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modelling stock return volatility dynamics in selected African markets

King

Botha

2015

Economic Modelling

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Section: Markov-switching Modelsmentioning

confidence: 99%

Section: Markov-switching Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modelling stock return volatility dynamics in selected African markets

King

Botha

2015

Economic Modelling

View full text Add to dashboard Cite

The impacts of global economic policy uncertainty on stock market returns in regime switching environment: Evidence from sectoral perspectives

Hoque

Zaidi

2018

Int. J Fin Econ

View full text Add to dashboard Cite

This study contributes in building emerging literature by investigating the impacts of global economic policy uncertainty on Malaysian sectoral stock performance. This study models sectoral stock returns as time‐varying transition probability Markovian processes and employs two‐stage Markov‐switching model for findings impacts of global economic policy uncertainty on sectoral stock returns in regime switching environment. The empirical results reveal that linear framework unable to detect the effects global economic policy uncertainty, and the Markov‐switching model exhibits significant effects of global economic policy uncertainty on all sectoral stock returns excluding technology sector in Malaysia stock market. The findings also expose that the effects of global economic policy uncertainty vary across regime states, sectors, and nature of effects, where the negative effects of global economic policy uncertainty dominate over positive effects. The global economic policy uncertainty exhibits greater impacts on stock returns in high‐volatility regime. Thus, the findings confirm the existence of asymmetric, nonlinear, nonmonotonic, and state‐dependent relationship between global economic policy uncertainty and sectoral stock returns in Malaysia. Therefore, the overall empirical findings can be applied in asset pricing and investment decision‐making purposes. The findings also suggest that global economic policy uncertainty can be a systemic risk factor and predictor of stock market returns.

show abstract

Dependency and causal relationship between ‘Bitcoin’ and financial asset classes: A Bayesian network approach

Mroua,

Souissi,

Donia

2023

Int. J Fin Econ

View full text Add to dashboard Cite

This study employs the Bayesian Networks (BN) and the wavelet coherence approaches to invest the relationship between Bitcoin volatility and financial asset classes (MSCI world equity index, S&P Goldman Sachs Commodity Index [GSCI], US index and Investment Grade Corporate Bond Index ETF [PIMCO]) using daily data for the period from August 2011 to October 2021. The results show that the causal relationship between Bitcoin and other financial assets varies depending on the market states. During the low volatility periods, Bitcoin has a stronger impact on the GSCI, while during the stability periods, it has a direct effect on the US index and the MSCI world index. In contrast, during high volatility periods, Bitcoin has a direct impact on both the GSCI and PIMCO indices. The key findings enabled us to provide implications for US investors to promote asset allocation and risk management covering both Bitcoin and traditional financial markets. The results suggest that policymakers should watch Botcoin closely to preserve financial stability.

show abstract

Regime-switching volatility of six East Asian emerging markets

Cited by 58 publications

References 25 publications

Modelling stock return volatility dynamics in selected African markets

Modelling stock return volatility dynamics in selected African markets

The impacts of global economic policy uncertainty on stock market returns in regime switching environment: Evidence from sectoral perspectives

Dependency and causal relationship between ‘Bitcoin’ and financial asset classes: A Bayesian network approach

Contact Info

Product

Resources

About