Threshold arch models and asymmetries in volatility

Rabemananjara, R.; Zakoïan, Jean‐Michel

doi:10.1002/jae.3950080104

Cited by 373 publications

(142 citation statements)

References 29 publications

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“…Having outlined how this study will examine the relationship between financial distress and stock premiums, we now employ a Threshold ARCH (TARCH) model (Rabemananjara and Zakoian, 1993) to examine the relationship between liquidity crises and stock premiums. This method is employed because good news and bad news may have different impacts on volatility (Bollerslev, Chou and Kroner, 1992;Glosten, Jaganathan and Runkle, 1993;Black, 2002), therefore this model enables us to allow for and measure these asymmetric shocks to volatility.…”

Section: Liquidity Crises and Stock Premiumsmentioning

confidence: 99%

Do financial distress and liquidity crises affect value and size premiums?

et al. 2016

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This study investigates the impact of liquidity crises on the relationship between stock (value and size) premiums and default risk in the US market. It first examines whether financial distress can explain value and size premiums, and then, subsequently, aims to determine whether liquidity crises increase the risk of value and size premium investment strategies. The study employs a time-varying approach and a sample of US stock returns for the period between January 1982 and March 2011, a period which includes the current liquidity crisis, so as to examine the relationship between default risk, liquidity crises and value and size premiums. The findings indicate that the default premium has explanatory power for value and size and premiums, which affect firms with different characteristics. We also find that liquidity crises may actually increase the risks related to size and value premium strategies.

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Section: Liquidity Crises and Stock Premiumsmentioning

confidence: 99%

Do financial distress and liquidity crises affect value and size premiums?

et al. 2016

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“…Rabemananjara and Zakoian (1993) applied this model in a generalized form to the French stock market. Recently, this model was generalized by El Babsiri and Zakoian (1996) by specifying " t = t + + t + t ; ; t , w h e r e t + and t ; are TGARCH processes.…”

Section: A Succinct Review Of Exible Arch Modelsmentioning

confidence: 99%

“…The TGARCH model captures this e ect by h a ving 1 > 2 . We are aware of the fact that other parametric models may a s w ell describe this feature, but the TGARCH model has proven to be a su ciently exible and tractable model for stock returns (see, e.g., Rabemananjara and Zakoian, 1993), whereas the EGARCH model, as noted above, su ers from several theoretical and practical drawbacks.…”

Section: Figures 1 Andmentioning

confidence: 99%

Discrete time option pricing with flexible volatility estimation

Härdle

Hafner

2000

Finance and Stochastics

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By extending the GARCH option pricing model of Duan (1995) to more exible volatility estimation it is shown that the prices of out-of-the-money options strongly depend on volatility features such as asymmetry. Results are provided for the properties of the stationary pricing distribution in the case of a threshold GARCH model. For a stock index series with a pronounced leverage e ect, simulated threshold GARCH option prices are substantially closer to observed market prices than the Black/Scholes and simulated GARCH prices.

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“…Their approach is econometrically more challenging than the static MES due to the fact that it accounts for time varying volatility and correlation as well as nonlinear tail dependence in the banks' and sector's returns. We employ the TARCH (see Rabemananjara and Zakoïan, 1993) and Dynamic Conditional Correlation (DCC) (see Engle, 2002) specifications for computing daily MES estimates for all trading days within one year. The daily MES estimates are then averaged for each bank-year to yield our first dependent variable.…”

Section: Systemic Risk Measuresmentioning

confidence: 99%

Why Do U.S. Banks Contribute More to Global Systemic Risk?

Bostandzic,

Weiss

2013

SSRN Journal

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We show that U.S. banks are more exposed and contribute more to systemic risk in the global financial system than European banks. We find that banks become systemically relevant if they rely too strongly on non-interest income, less traditional lending and if the quality of their loan portfolio decreases. More stringent capital regulations and more independent supervisory agencies improve financial stability. As we match European and U.S. banks based on firm size and valuation, the differences we find in the banks' systemic relevance cannot be explained by the too-big-to-fail or charter value hypotheses.Keywords: Financial crises, systemic risk, bank regulation, non-interest income, capital regulation. Why do U.S. banks contribute more to global systemic risk? AbstractWe show that U.S. banks are more exposed and contribute more to systemic risk in the global financial system than European banks. We find that banks become systemically relevant if they rely too strongly on non-interest income, less traditional lending and if the quality of their loan portfolio decreases. More stringent capital regulations and more independent supervisory agencies improve financial stability. As we match European and U.S. banks based on firm size and valuation, the differences we find in the banks' systemic relevance cannot be explained by the too-big-to-fail or charter value hypotheses.

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Threshold arch models and asymmetries in volatility

Cited by 373 publications

References 29 publications

Do financial distress and liquidity crises affect value and size premiums?

Do financial distress and liquidity crises affect value and size premiums?

Discrete time option pricing with flexible volatility estimation

Why Do U.S. Banks Contribute More to Global Systemic Risk?

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