Leverage effect, economic policy uncertainty and realized volatility with regime switching

Duan, Yinying; Chen, Wang; Zeng, Qing; Liu, Zhicao

doi:10.1016/j.physa.2017.10.040

Cited by 33 publications

(10 citation statements)

References 28 publications

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“…The corresponding model with regime switching can be expressed as

r_{t + 1} = β_{0} + β_{italicVar, S_{t}} {Var}_{t} + ω_{t + 1},

where S t is the state for month t ,

β_{d, S_{t}}

is the regression slope of Var at state S t ,

ω_{t} \sim N ((),, 0, σ_{S_{t}}^{2})

, and

σ_{S_{t}}^{2}

is the variance of the innovation at state S t . Following Raggi and Bordignon (2012), Shi and Ho (2015), Ma, Wahab, Huang, and Xu (2017), and Duan, Chen, Zeng, and Liu (2018), among others, we consider two regimes 7 . More specifically, the state (regime) of S t = 0 indicates the low‐return regime, whereas the state (regime) of S t = 1 indicates the high‐return regime.…”

Section: Resultsmentioning

confidence: 99%

Good variance, bad variance, and stock return predictability

Zhang

Liang

et al. 2020

Int. J Fin Econ

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In the stock market, past winners and losers usually have different attitudes to variance risk. In light of this, we decompose stock variance into good and bad variances, and further construct a composite signed variance. The recursively estimated coefficient of standard variance is volatile over time, but the ones of good, bad, and signed variances are fairly stable. This suggests the high efficiency of our decomposition approach. We provide convincing evidence that bad variance and composite signed variance can significantly predict future stock returns both in‐ and out‐of‐sample. Furthermore, a mean–variance investor can realize substantial economic gains by using our bad and signed variances relative to the traditional variance predictor and simple mean benchmark. Our decomposition approach can outperform other similar but complex techniques, including threshold regression and Markov regime switching. The results are consistent across multiple robustness tests.

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“…The corresponding model with regime switching can be expressed as

r_{t + 1} = β_{0} + β_{italicVar, S_{t}} {Var}_{t} + ω_{t + 1},

where S t is the state for month t ,

β_{d, S_{t}}

is the regression slope of Var at state S t ,

ω_{t} \sim N ((),, 0, σ_{S_{t}}^{2})

, and

σ_{S_{t}}^{2}

Section: Resultsmentioning

confidence: 99%

Good variance, bad variance, and stock return predictability

Zhang

Liang

et al. 2020

Int. J Fin Econ

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show abstract

“…Moreover, Veronesi (2012, 2013) explain how uncertainty commands equity risk-premium by developing a general-equilibrium model. Unlike the previous studies (e.g., Baker et al, 2016;Demir et al, 2018;Duan et al, 2018;Hu et al, 2018;Raza et al, 2018), the present work attempts to explain changes in the volatility of the volatility indexes specific to "policy uncertainty". FOMC meetings, GDP report, and other macroeconomic indicators have been considered to show the effects of policy uncertainty on the various asset class.…”

Section: Empirical Model Buildingmentioning

confidence: 98%

Does Policy Uncertainty Affect Equity, Commodity, Interest Rates, and Currency Markets? Evidence From Cboe’s Volatility Index

Shaikh

2020

Journal of Business Economics and Management

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Economic policy drives investment, production, employment, and other macroeconomic indicators of the economy. The study examines the equity, commodity, interest rates, and currency markets, taking into consideration the US economic policy uncertainty (EPU) index. The present work determines the association among policy uncertainty and volatility index, expressed in terms of generalized autoregressive conditional heteroscedasticity and period of empirical work spanning from 2000 to 2018. The results suggest that equity markets’ volatility tends to be very high based on a high degree of policy uncertainty. The findings on the commodity market indicate that crude oil and gold prices remain more volatile during the presidential election and financial crisis. One of the essential results shows that the 2000s boom, early credit crunch, Lehman’s collapse and recession, and fiscal policy battles have significantly affected the equity, currency, and commodity markets. The interest rates and currency markets have responded considerably to Feds’ and EPU index. The empirical outcome provides evidence that implied volatility index is a forward looking expectation of future stock market volatility, and it uncovers that policy uncertainty affects investor sentiment. The present work holds some practical implications for the government to formulate policies to regulate the US market.

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“…Indeed, such involvement is independent of the level of VIX and household expectations. Duan et al [9] investigated Does policy uncertainty affect investors' sentiment, measured by VIX? To answer this question, this study explores different VIX allied volatility measures in relation to EPU of the U.S. economy.…”

Section: Introductionmentioning

confidence: 99%

On the Relationship between Economic Policy Uncertainty and the Implied Volatility Index

Shaikh

2019

Sustainability

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This article examines the effects of economic policy uncertainty (EPU) on the implied volatility index. The implied volatility index of various markets has been analyzed in relation to scheduled macroeconomic announcements, such as EPU and equity market policy uncertainty (EMPU) indices. The study highlights that EPU contains important information to explain the diverse market effects of the U.S., which is gauged into the volatility index. Estimates obtained in an autoregressive conditional heteroscedasticity framework indicate the persistence of volatility during spikes in the EPU. More importantly, the lagged values of the policy uncertainty index also contains market-related information to explain the markets’ future volatility. Major political and economic events have also contributed positively in that a presidential election contains information to explain various asset classes. Commodities, such as crude oil, gold, corn, and soybean, have been impacted significantly followed by EPU. Moreover, interest rate market volatility has also been moved adversely due to tight monetary policy. The Markov regime switching regression manifests that the implied volatility index (VIX) behaves abruptly in two different regimes followed by EPU.

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Leverage effect, economic policy uncertainty and realized volatility with regime switching

Cited by 33 publications

References 28 publications

Good variance, bad variance, and stock return predictability

Good variance, bad variance, and stock return predictability

Does Policy Uncertainty Affect Equity, Commodity, Interest Rates, and Currency Markets? Evidence From Cboe’s Volatility Index

On the Relationship between Economic Policy Uncertainty and the Implied Volatility Index

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