VIX term structure and VIX futures pricing with realized volatility

Using an extended LHARG model proposed by Majewski et al. (2015, J Econ, 187, 521–531), we derive the closed-form pricing formulas for both the Chicago Board Options Exchange VIX term structure and VIX futures with different maturities. Our empirical results suggest that the quarterly and yearly components of lagged realized volatility should be added into the model to capture the long-term volatility dynamics. By using the realized volatility based on high-frequency data, the proposed model provides superior pricing performance compared with the classic Heston–Nandi GARCH model under a variance-dependent pricing kernel, both in-sample and out-of-sample. The improvement is more pronounced during high volatility periods.     

Valuation of VIX and target volatility options with affine GARCH models

In this paper we propose semiclosed-form solutions, subject to an inversion of the Fourier transform, for the price of VIX options and target volatility options under affine GARCH models based on Gaussian and Inverse Gaussian distributions. We illustrate the advantage of the proposed analytic expressions by comparing them with those obtained from benchmark Monte–Carlo simulations. The empirical performance of the two affine GARCH models is tested using different calibration exercises based on historical returns and market quotes on VIX and SPX options.     

VIX futures and its closed‐form pricing through an affine GARCH model with realized variance

This paper studies the forecasting of volatility index (VIX) and the pricing of its futures by a generalized affine realized volatility model proposed by Christoffersen et al. This model is a weighted average of a GARCH and a pure realized variance (RV) model that incorporates each volatility component into the new dynamics. We rewrite the VIX in terms of both volatility components and then derive closed‐form formulas for the VIX forecasting and its futures pricing. Our empirical studies find that a unification of the GARCH and the RV in the modeling substantially improves the forecasting of this index and the pricing of its futures.     

Volatility information implied in the term structure of VIX

This study uses multiple maturity-independent variables to examine whether the volatility information implied in the term structure of volatility index can improve the prediction of realized volatility. The empirical results for the S&P 500 index show that, in terms of both the in-sample estimation and out-of-sample forecasting, the term structure variables provide substantial incremental contribution to the models with only level variables. Our empirical results are robust to various forms of volatility, alternative ways to develop the term structure variable, the impact of macroeconomic variables, and alternative underlying assets.     

Variance and skew risk premiums for the volatility market: The VIX evidence

We extract variance and skew risk premiums from volatility derivatives in a model-free way and analyze their relationships along with volatility index and equity index returns. These risk premiums can be synthesized through option trading strategies. Using a time series of option prices on the VIX, we find that variance swap excess return can be partially explained by volatility index and equity index excess returns while these latter variables carry little information for the skew swap excess return. The results sharply contrast with those obtained for the equity index option market underlining very specific characteristics of the volatility derivative market.     

Systemic risk in global volatility spillover networks: Evidence from option-implied volatility indices

With option-implied volatility indices, we identify networks of global volatility spillovers and examine time-varying systemic risk across global financial markets. The U.S. stock market is the center of the network and plays a dominant role in the spread of volatility spillover to other markets. The global systemic risks have intensified since the Federal Reserve exited from quantitative easing, hiked interest rate, and shrank its balance sheet. We further show that the U.S. monetary tightening is an important catalyst for the intensifying global systemic risk. Our findings highlight the pernicious effects of monetary tightening after an era of cheap money.     

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 short‐time behavior of VIX‐implied volatilities in a multifactor stochastic volatility framework

We consider a modeling setup where the volatility index (VIX) dynamics are explicitly computable as a smooth transformation of a purely diffusive, multidimensional Markov process. The framework is general enough to embed many popular stochastic volatility models. We develop closed‐form expansions and sharp error bounds for VIX futures, options, and implied volatilities. In particular, we derive exact asymptotic results for VIX‐implied volatilities, and their sensitivities, in the joint limit of short time‐to‐maturity and small log‐moneyness. The expansions obtained are explicit based on elementary functions and they neatly uncover how the VIX skew depends on the specific choice of the volatility and the vol‐of‐vol processes. Our results are based on perturbation techniques applied to the infinitesimal generator of the underlying process. This methodology has previously been adopted to derive approximations of equity (SPX) options. However, the generalizations needed to cover the case of VIX options are by no means straightforward as the dynamics of the underlying VIX futures are not explicitly known. To illustrate the accuracy of our technique, we provide numerical implementations for a selection of model specifications.     

Price transmission and volatility spillovers in Asian rice markets: Evidence from MGARCH and panel GARCH models

ABSTRACT

This article examines world rice price transmission and volatility spillovers across six major Asian rice markets over the period 2005-13. In addition to the conventional GARCH models, we use a panel GARCH framework to estimate the spillover effects along with the consideration of heterogeneity and interdependence among countries. Empirical results suggest that changes in the world rice price affected not only the price levels of domestic rice markets but also their conditional variances. Moreover, interdependence across rice markets contributed to a strong spillover of a price shock in one country to another within the region.     

Good jump,bad jump,and option valuation

I develop a new class of closed‐form option pricing models that incorporate variance risk premium and symmetric or asymmetric double exponential jump diffusion. These models decompose the jump component into upward and downward jumps using two independent exponential distributions and thus capture the impact of good and bad news on asset returns and option prices. The empirical results show that the model with an asymmetric double exponential jump diffusion improves the fit on Shanghai Stock Exchange 50ETF returns and options and provides relatively better in‐ and out‐of‐sample pricing performance.     

Inferring information from the S&P 500, CBOE VIX,and CBOE SKEW indices

This paper compares the information extracted from the S&P 500, CBOE VIX, and CBOE SKEW indices for the S&P 500 index option pricing. Based on our empirical analysis, VIX is a very informative index for option prices. Whether adding the SKEW or the VIX term structure can improve the option pricing performance depends on the model we choose. Roughly speaking, the VIX term structure is informative for some models, while the SKEW is very noisy and does not contain much important information for option prices. This paper also extends Zhang et al. (2017, J Futures Markets, 37, 211–237) into three typical affine models.     

The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

In this paper, we find new evidence for the carbon futures volatility prediction by using the spillovers of fossil energy futures returns as a powerful predictor. The in-sample results show that the spillovers have a significantly positive effect on carbon futures volatility. From the out-of-sample analysis with various loss functions, we find that fossil energy return spillovers significantly outperform the benchmark and show better forecasting performance than the competing models using dimension reduction, variable selection, and combination approaches. The predictive ability of the spillovers also holds in long-term forecasting and does not derive from other carbon-related variables. It can bring substantial economic gains in the portfolio exercise within carbon futures. Finally, we provide economic explanations on the predictive ability of the fossil energy return spillover by the channels of the carbon emission uncertainty and the investor sentiment on the warming climate.     

Improving momentum strategies using residual returns and option-implied information

This paper provides an alternative method for enhancing momentum profits by combining residual returns and option-implied information. The results show that the main benefit of applying residual returns to construct momentum portfolios is generating stable returns. Additionally, the incorporation of implied volatility (IV) spread or IV skew into a residual momentum portfolio is found to significantly raise profits, particularly during bad times and high-sentiment periods. This is because IV spread and IV skew can dissociate winners/losers with a price underreaction from those with a price overreaction, which suggests that informed traders who perceive price underreactions/overreactions trade in option markets.     

Exchange volatility and export performance in Egypt: New insights from wavelet decomposition and optimal GARCH model

To assess the link between exchange rate uncertainty and exports performance in Egypt, this article relies on an optimal GARCH model chosen by information criteria among decomposed series on a scale-by-scale basis (wavelet decomposition). The observed outcomes reveal that this relationship depends intensely on the frequency-to-frequency variation and slightly on the leverage effect and switching regime. Indeed, it is well shown that at the low frequency, the coefficient associated to exchange rate volatility's effect on exports is greater than that at the high frequency and conversely when subtracting energy's share. We attribute the apparently conflicting results to the co-movement between energy prices and those of other commodities, the excessive speculation and the composition of trade partners.     

Heterogeneous firm-level responses to trade liberalization: A test using stock price reactions

This paper presents novel empirical evidence on key predictions of heterogeneous firm models by examining stock market reactions to the Canada–United States Free Trade Agreement of 1989 (CUSFTA). I derive testable predictions for a class of models based on Melitz (2003). Using the uncertainty surrounding CUSFTA's ratification, I show that the pattern of abnormal returns of Canadian manufacturing firms was strongly consistent with predictions related to export (U.S.) tariff reductions, but less so with predictions related to import (Canadian) tariff reductions. Lower Canadian tariffs did have an effect through the implied reduction in intermediate input tariffs, however.     

A rational asset pricing model for premiums and discounts on closed‐end funds: The bubble theory

This paper provides a new explanation for closed‐end fund (CEF) discounts and premiums using the local martingale theory of asset price bubbles. This is a rational asset pricing model that is shown to be consistent with the existing empirical evidence on CEF discounts/premiums. Additional testable implications of the model are derived, which await subsequent research for their resolution. This bubble theory also applies equally well to understanding discounts and premiums on exchange traded funds.     

Business cycles and monetary regimes in emerging economies: A role for a monopolistic banking sector

This study shows that the presence of imperfect competition in the banking system propagates external shocks and amplifies the business cycle. Strategic limit pricing, aimed at protecting retail niches from potential competitors, generates countercyclical bank markups. Markup increments during recessions directly increase borrowing costs for firms and indirectly damage the financial position of firms' balance-sheets, increasing the risk perception of lenders. I use Bayesian techniques and data from Argentina to show that the inclusion of monopolistic banking improves the fit of the New Keynesian small open economy model.     

Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning

A basic aim of marketing research is to predict consumers’ preferences and the success of marketing campaigns at the population-level. However, traditional marketing tools have various limitations, calling for novel measures to improve predictive power. In this study, we use multiple types of measures extracted from electroencephalography (EEG) recordings and machine learning (ML) algorithms to improve preference prediction based on self-reports alone. Subjects watched video commercials of six food products as we recorded their EEG activity, after which they responded to a questionnaire that served as a self-report benchmark measure. Thereafter, subjects made binary choices over the food products. We attempted to predict within-sample and population level preferences, based on subjects’ questionnaire responses and EEG measures extracted during the commercial viewings. We reached 68.5% accuracy in predicting between subjects’ most and least preferred products, improving accuracy by 4.07 percentage points compared to prediction based on self-reports alone. Additionally, EEG measures improved within-sample prediction of all six products by 20%, resulting in only a 1.91 root mean squared error (RMSE) compared to 2.39 RMSE with questionnaire-based prediction alone. Moreover, at the population level, assessed using YouTube metrics and an online questionnaire, EEG measures increased prediction by 12.7% and 12.6% respectively, compared to only a questionnaire-based prediction. We found that the most predictive EEG measures were frontal powers in the alpha band, hemispheric asymmetry in the beta band, and inter-subject correlation in delta and alpha bands. In summary, our novel approach, employing multiple types of EEG measures and ML models, offers marketing practitioners and researchers a valuable tool for predicting individual preferences and commercials’ success in the real world.