Publications
Publications
Selected papers:
Pricing VIX futures and its ETN options with fractionally integrated volatility of volatility (April 2025).
This paper studies the impact of fractional integration of volatility of volatility on the pricing of volatility derivatives. We develop a heterogeneous autoregressive model (HAR) model for the VIX index, where the volatility of the VIX index adopts an ARCH (∞) representation and is a combination of a fractionally integrated long-memory process and a short-memory process. Pricing formulas are derived for VIX futures and options, and VXX options – the most actively traded VIX futures exchange traded note (ETN) options. In the empirical study, we focus on VIX futures and VXX options. By using extensive data on the VIX futures and VXX options over the period from 2012 to 2024, we find that models that incorporate long memory volatility of the VIX index significantly outperform the model with only short memory component in the joint pricing, indicating fractional integration is an important characteristic of volatility of the VIX index.
Pricing VXX options with observable volatility dynamics from high-frequency VIX index (2025). Journal of Futures Markets. Sole-authored. (ABS3)
This paper develops a discrete-time joint analytical framework of pricing VIX and VXX options consistently. We show that our framework is more flexible than continuous-time VXX models as it allows the information contained in the high-frequency VIX index to be incorporated for the joint pricing of VIX and VXX options; and the joint pricing formula is derived. Our empirical analysis shows that the model that utilizes the realized variance computed from the high-frequency VIX index data significantly outperforms the model that does not reply on the VIX realized variance in the joint pricing both in-sample and out-of-sample, reinforcing the beliefs that high-frequency data are informative about the derivatives pricing.
Joint calibration of VIX and VXX options: does volatility clustering matter? (2023). European Journal of Finance. Sole-authored. (ABS3)
This paper studies the effects of volatility clustering on the joint calibration of VIX and VXX options. We find that model which incorporates volatility clustering outperforms other models without this feature in joint calibration of VIX and VXX options both in-sample and out-of-sample; the superiority of the model with volatility clustering is statistically significant. Moreover, the information contained in the VXX options is not fully spanned by the VIX options, as a result, one can achieve better joint pricing performance by employing both VIX and VXX derivatives data when calibrating the model, compared to the case when only VIX data are used in calibration.
Influencing subjective well-being for business and sustainable development using big data and predictive regression analysis (2021). Journal of Business Research. With V. Weerakkody, U. Sivarajah, K. Mahroof, and T. Maruyama. (ABS3)
Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people’s well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people’s personal lifestyles now offers a new dimension into well- being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people’s perceived well-being, long-term health conditions, one’s marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data.
Monte Carlo analysis of methods for extracting risk-neutral densities with affine jump diffusions (2019). Journal of Futures Markets. Sole-authored. (ABS3)
[Published version][ResearchGate]
This article compares several widely used and recently developed methods to extract risk-neutral densities (RNDs) from option prices in terms of estimation accuracy. It shows that the positive convolution approximation method consistently yields the most accurate RND estimates, and is insensitive to the discreteness of option prices. RND methods are less likely to produce accurate RND estimates when the underlying process incorporates jumps and when estimations are performed on sparse data, especially for short time-to-maturities, though sensitivity to the discreteness of the data differs across different methods.
Forecasting the term structure of volatility of crude oil price changes (2016). Economics Letters. With E. Balaban. (ABS3)
[Published version][ResearchGate]
This is a pioneering effort to test the comparative performance of two competing models for out-of-sample forecasting the term structure of volatility of crude oil price changes employing both symmetric and asymmetric evaluation criteria. Under symmetric error statistics, our empirical model using the estimated growth factor of volatility through time is overall superior, and it beats in most cases the benchmark model of the square-root-of-time ($\sqrt{T}$) for holding periods between one and 250 days. Under asymmetric error statistics, if over-prediction (under-prediction) of volatility is undesirable, the empirical (benchmark) model is consistently superior. Relative performance of the empirical model is much higher for holding periods up to fifty days.
Testing the predictive ability of corridor implied volatility under GARCH models (2019). Asia-Pacific Financial Markets. Sole-authored. (ABS2)
[Published version][ResearchGate]
This paper studies the predictive ability of corridor implied volatility (CIV) measure. It is motivated by the fact that CIV is measured with better precision and reliability than the model-free implied volatility due to the lack of liquid options in the tails of the risk-neutral distribution. By adding CIV measures to the modified GARCH specifications, the out-of-sample predictive ability of CIV is measured by the forecast accuracy of conditional volatility. It finds that the narrowest CIV measure, covering about 10% of the RND, dominate the 1-day ahead conditional volatility forecasts regardless of the choice of GARCH models in high volatile period; as market moves to non volatile periods, the optimal width broadens. For multi-day ahead forecasts narrow and mid-range CIV measures are favoured in the full sample and high volatile period for all forecast horizons, depending on which loss functions are used; whereas in non turbulent markets, certain mid-range CIV measures are favoured, for rare instances, wide CIV measures dominate the performance. Regarding the comparisons between best performed CIV measures and two benchmark measures (market volatility index and at-the-money Black–Scholes implied volatility), it shows that under the EGARCH framework, none of the benchmark measures are found to outperform best performed CIV measures, whereas under the GARCH and NAGARCH models, best performed CIV measures are outperformed by benchmark measures for certain instances.