Stress-Testing Volatility Risk Premium Harvesting Strategies Based on S&P 500 Index Options

2017 ◽  
Vol 8 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Wei Ge
Keyword(s):  
Risk Premium ◽  
Stress Testing ◽  
Index Options ◽  
Volatility Risk ◽  
Volatility Risk Premium

Determinants of the Index Straddle Returns

2013 ◽  
Vol 21 (4) ◽  
pp. 411-434
Author(s):  
Byung Jin Kang
Keyword(s):  
Risk Premium ◽  
Trading Strategies ◽  
Excess Returns ◽  
Index Options ◽  
Volatility Risk ◽  
Volatility Risk Premium ◽  
Theoretical View

This paper investigates ATM zero-beta straddle (i.e., ZBS) returns, one of the most widely used volatility trading strategies, and then examines the determinants of them. First, from a point of theoretical view, we find that the determinants of the ZBS returns without rebalancing are different from those with rebalancing. This means that most previous studies overlooking the return characteristics by difference of rebalancing frequency could result in misleading implications. Next, from a point of empirical view, we find that the negative excess returns are also obtained by taking a long position in ZBS on the KOSPI 200 index options, as in most other markets. Even though these negative excess returns are not strongly significant, but they are found to be closely related to the volatility risk premium.

The Volatility Risk Premium in the KOSPI200 Index Options

2009 ◽  
Vol 17 (2) ◽  
pp. 67-86
Author(s):  
Hyoung-Jin Park
Keyword(s):  
Risk Premium ◽  
Empirical Method ◽  
Option Prices ◽  
Options Market ◽  
Index Options ◽  
Volatility Risk ◽  
Put Options ◽  
Volatility Risk Premium ◽  
Bid Ask Spread ◽  
Delta Hedging

This study examines whether the volatility risk premium is reflected on the prices of the KOSPI200 index options. By applying the empirical method of Bakshi and Kapadia (2003), we analyze the performance of delta-hedging strategy in the KOSPI200 index market. They showed the existence of the negative volatility risk premium in the S&P 500 index options as well as derived theoretical positive relationship between the volatility risk premium and delta-hedging gains. However, in the results of this study, contrary to those in the S&P 500 index options market, we do not observe the volatility risk premium. Delta-hedged gains of ATM options are not significantly negative. Delta-hedged gains are not more negative as historical volatility increases and as options' vega and remaining maturity increase. However, the notable finding of this study is that delta-hedged gains of OTM options and put options are negative and signigicant. This results can be caused by the phenomenon of overpriced puts or by market microstructure factors, such as relatively wide bid-ask spread and high tick size. In a simple examination of comparison between changes in traded option prices and changes in the Black-Scholes option prices during delta-hedging period, negative delta-hedged gains seem to be attributed to too small changes in call option prices and too large changes in put option prices. In conclusion, by analyzing the delta-hedged gains, the volatility risk risk premium seems not be observed in the KOSPI200 index options market.

Evidence on aggregate volatility risk premium for the French stock market

2019 ◽  
Vol 46 (1) ◽  
pp. 72-91
Author(s):  
Amal Zaghouani Chakroun ◽  
Dorra Mezzez Hmaied
Keyword(s):  
Risk Factor ◽  
Stock Market ◽  
Risk Premium ◽  
Factor Model ◽  
Excess Returns ◽  
Time Series Regression ◽  
Volatility Risk ◽  
Content Type ◽  
Volatility Risk Premium ◽  
Fama And French

Purpose The purpose of this paper is to examine alternative six- and seven-factor equity pricing models directed at capturing a new factor, aggregate volatility, in addition to market, size, book to market, profitability, investment premiums of the Fama and French (2015) and Fama and French’s (2018) aggregate volatility augmented model. Design/methodology/approach The models are tested using a time series regression and Fama and Macbeth’s (1973) methodology. Findings The authors show that both six- and seven-factor models best explain average excess returns on the French stock market. In fact, the authors outperform Fama and French’s (2018) model. The authors use sensitivity of aggregate volatility of a stock VCAC as a proxy to construct the aggregate volatility risk factor. The spanning tests suggest that Fama and French’s (1993, 2015, 2018) and Carhart’s (1997) models do not explain the aggregate volatility risk factor FVCAC. The results show that the FVCAC factor earns significant αs across the different multifactor models and even after controlling for the exposure to all the other in Fama and French’s (2018) model. The asset pricing tests show that it is systematically priced. In fact, the authors find a significant and negative (positive) relation between the aggregate volatility risk factor and the excess returns in the French stock market when it is rising (falling), in addition, periods with downward market movements tend to coincide with high volatility. Originality/value The authors contribute to the related literature in several ways. First, the authors test two new empirical six- and seven-factor model and the authors compare them to Fama and French’s (2018) model. Second, the authors give new evidence about the VCAC, using it for the first time to the authors’ knowledge, to construct a volatility risk premium.

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