Corridor Implied Volatility and the Variance Risk Premium in the Italian Market

Abstract We incorporate a latent stochastic volatility factor and macroeconomic expectations in an affine model for the term structure of nominal and real rates. We estimate the model over 1999–2016 on U.S. data for nominal and TIPS yields, the realized and implied volatility of T-bonds, and survey forecasts of GDP growth and inflation. We find relatively stable inflation risk premia averaging at 40 basis points at the long-end, and which are strongly related to the volatility factor and conditional mean of output growth. We also document real risk premia that turn negative in the post-crisis period, and a non-negligible variance risk premium.

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How informative are variance risk premium and implied volatility for Value-at-Risk prediction? International evidence

The Quarterly Review of Economics and Finance ◽

10.1016/j.qref.2019.08.006 ◽

2020 ◽

Vol 76 ◽

pp. 22-37

Author(s):

Skander Slim ◽

Meriam Dahmene ◽

Adel Boughrara

Keyword(s):

At Risk ◽

Risk Prediction ◽

Risk Premium ◽

Value At Risk ◽

Implied Volatility ◽

Variance Risk Premium ◽

International Evidence ◽

Variance Risk

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Can the Implied Information of Options Predict the Liquidity of Stock Market? A Data-Driven Research Based on SSE 50ETF Options

Journal of Mathematics ◽

10.1155/2021/9059213 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Hairong Cui ◽

Jinfeng Fei ◽

Xunfa Lu

Keyword(s):

Stock Market ◽

Risk Premium ◽

Implied Volatility ◽

Option Price ◽

Price Volatility ◽

Market Liquidity ◽

Data Driven ◽

Management Tool ◽

Variance Risk Premium ◽

Variance Risk

Liquidity reflects the quality of the market. When the market is short of liquidity, it often causes investors’ trading difficulties and stock price volatility, expanding the investment risk. As a risk management tool, options attract more informed investors to trade because of their flexible design. To explore whether the implied information based on the formation of option price can predict the liquidity of stock market, we take SSE 50ETF options from February 9, 2015, to December 31, 2020, as the research sample. Based on the idea of data-driven approach, we extract the implied information contained in option price, including implied volatility, implied volatility spread, and variance risk premium. Through the regression analysis method, we examine the ability to predict the liquidity of the stock market. The results show that implied volatility spread has the strongest ability to predict the liquidity of the stock market, and it is more significant within 270 days. Implied volatility contains the information about the short-term (120 days) liquidity of the stock market in the future. It shows that implied volatility and implied volatility spread are good indicators to predict stock market liquidity. In contrast, variance risk premium cannot predict the liquidity of stock market. The research conclusion verifies the role of option-implied information in predicting the stock market’s liquidity. By extracting the information of options price, investors and financial regulators can scientifically participate in the financial market under data guidance.

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