Conditional Volatility and the GARCH Option Pricing Model with Non‐Normal Innovations

On the basis of the theory of a wedge between the physical and risk‐neutral conditional volatilities in Christoffersen, P., Elkamhi, R., Feunou, B., & Jacobs, K. (2010), we develop a modification of the GARCH option pricing model with the filtered historical simulation proposed in Barone‐Adesi, G., Engle, R. F., & Mancini, L. (2008). The one‐day‐ahead conditional volatilities under physical and risk‐neutral measures are the same in the previous model, but should have been allowed to be different. Using extensive data on S&P 500 index options, our approach, which employs one‐day‐ahead risk‐neutral conditional volatility estimated from the cross‐section of the option prices (in contrast to the existing GARCH option pricing models), maintains theoretical consistency under conditional non‐normality, and improves the empirical performances. Remarkably, the risk‐neutral volatility dynamics are stable over time in this model. In addition, the comparison between the VIX index and the risk‐neutral integrated volatility economically validates our approach. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark 33:1–28, 2013     

Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model

We develop a new generalized autoregressive conditional heteroskedasticity (GARCH) model that accounts for the information spillover between two markets. This model is used to detect the usefulness of the CBOE volatility index (VIX) for improving the performance of volatility forecasting and option pricing. We find the significant ability of VIX to predict stock volatility both in-sample and out-of-sample. VIX information also helps to greatly reduce the option pricing error. The proposed volatility spillover GARCH model performs better than the related approaches proposed by Kanniainen et al. (2014, J Bank Finance, 43, pp. 200-211) and P. Christoffersen et al. (2014, J Financ Quant Anal, 49, pp. 663–697).     

Are the East Asian markets integrated? Evidence from the ICAPM

We test a conditional international asset pricing model with both world market and domestic risk included as independent pricing factors for five East Asian markets, the US and World markets. We model second moments and risk exposures using a bi-diagonal multivariate GARCH(1,1) process. We document that this novel GARCH specification provides a significantly better fit of the return process than a standard diagonal specification. Although exposure to world market risk carries a significant premium across all markets, we find little support for the hypothesis that exposure to residual country risk is rewarded. However, residual country returns are significantly related to exchange rate changes. Hence, we find surprisingly little evidence of market segmentation in East Asia over the period 1985–1998.     

A Note on Hedging in ARCH and Stochastic Volatility Option Pricing Models

Recently, Duan (1995) proposed a GARCH option pricing formula and a corresponding hedging formula. In a similar ARCH-type model for the underlying asset, Kallsen and Taqqu (1994) arrived at a hedging formula different from Duan's although they concur on the pricing formula. In this note, we explain this difference by pointing out that the formula developed by Kallsen and Taqqu corresponds to the usual concept of hedging in the context of ARCH-type models. We argue, however, that Duan's formula has some appeal and we propose a stochastic volatility model that ensures its validity. We conclude by a comparison of ARCH-type and stochastic volatility option pricing models.     

Jump variance risk: Evidence from option valuation and stock returns

We study jump variance risk by jointly examining both stock and option markets. We develop a GARCH option pricing model with jump variance dynamics and a nonmonotonic pricing kernel featuring jump variance risk premium. The model yields a closed-form option pricing formula and improves in fitting index options from 1996 to 2015. The model-implied jump variance risk premium has predictive power for future market returns. In the cross-section, heterogeneity in exposures to jump variance risk leads to a 6% difference in risk-adjusted returns annually.     

The economic significance of conditional skewness in index option markets

This study examines whether conditional skewness forecasts of the underlying asset returns can be used to trade profitably in the index options market. The results indicate that a more general skewness‐based option‐pricing model can generate better trading performance for strip and strap trades. The results show that conditional skewness model forecasts, when combined with forward‐looking option implied volatilities, can significantly improve the performance of skewness‐based trades but trading costs considerably weaken the profitability of index option strategies. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:378–406, 2010     

The finite sample properties of the GARCH option pricing model

The authors explore the finite sample properties of the generalized autoregressive conditional heteroscedasticity (GARCH) option pricing model proposed by S. L. Heston and S. Nandi (2000). Simulation results show that the maximum likelihood estimators of the GARCH process may contain substantial estimation biases, even when samples as large as 3,000 observations are used. However, it was found that these biases cause significant mispricings only for short‐term, out‐of‐the‐money options. It is shown that, given an adequate estimation sample, this bias can be reduced considerably by employing the jackknife resampling method. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:599–615, 2007     

Switching asymmetric GARCH and options on a volatility index

Few proposed types of derivative securities have attracted as much attention and interest as option contracts on volatility. Grunbichler and Longstaff (1996) is the only study that proposes a model to value options written on a volatility index. Their model, which is based on modeling volatility as a GARCH process, does not take into account the switching regime and asymmetry properties of volatility. We show that the Grunbichler and Longstaff (1996) model underprices a three‐month option by about 10%. A Switching Regime Asymmetric GARCH is used to model the generating process of security returns. The comparison between the switching regime model and the traditional uni‐regime model among GARCH, EGARCH, and GJR‐GARCH demonstrates that a switching regime EGARCH model fits the data best. Next, the values of European call options written on a volatility index are computed using Monte Carlo integration. When comparing the values of the option based on the Switching Regime Asymmetric GARCH model and the traditional GARCH specification, it is found that the option values obtained from the different processes are very different. This clearly shows that the Grunbichler‐Longstaff model is too stylized to be used in pricing derivatives on a volatility index. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:251–282, 2004     

APPROXIMATING GARCH-JUMP MODELS, JUMP-DIFFUSION PROCESSES, AND OPTION PRICING

This paper considers the pricing of options when there are jumps in the pricing kernel and correlated jumps in asset prices and volatilities. We extend theory developed by Nelson (1990) and Duan (1997) by considering the limiting models for our approximating GARCH Jump process. Limiting cases of our processes consist of models where both asset price and local volatility follow jump diffusion processes with correlated jump sizes. Convergence of a few GARCH models to their continuous time limits is evaluated and the benefits of the models explored.     

Option pricing under Markov‐switching GARCH processes

This study proposes an N ‐state Markov‐switching general autoregressive conditionally heteroskedastic (MS‐GARCH) option model and develops a new lattice algorithm to price derivatives under this framework. The MS‐GARCH option model allows volatility dynamics switching between different GARCH processes with a hidden Markov chain, thus exhibiting high flexibility in capturing the dynamics of financial variables. To measure the pricing performance of the MS‐GARCH lattice algorithm, we investigate the convergence of European option prices produced on the new lattice to their true values as conducted by the simulation. These results are very satisfactory. The empirical evidence also suggests that the MS‐GARCH model performs well in fitting the data in‐sample and one‐week‐ahead out‐of‐sample prediction. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:444–464, 2010