Electoral uncertainty and financial volatility: Evidence from two‐round presidential races in emerging markets

We study how the predictability and the decisiveness of electoral outcomes affect financial volatility. We argue that traders’ optimal investment strategies depend on their ability to make accurate electoral forecasts and the prospective losses associated with placing a bet on the wrong candidate. Using a triple difference‐in‐difference approach and data from two‐round presidential elections in five Latin American countries between 1999 and 2018, we find that financial volatility is greatest in the days immediately following unpredictable, decisive, elections. Postelectoral volatility also occurs following predictable, indecisive elections. The effect of learning the identity of the winning candidate on financial volatility is null when the election is unpredictable and indecisive, as well as when the election is decisive, but the outcome is predictable. These findings offer insights into investors seeking to hedge price risk around elections. They also have important implications regarding the relationship between public opinion polls and postelectoral financial volatility.     

Leverage effect on stochastic volatility for option pricing in Hong Kong: A simulation and empirical study

This paper explores the importance of incorporating the financial leverage effect in the stochastic volatility models when pricing options. For the illustrative purpose, we first conduct the simulation experiment by using the Markov Chain Monte Carlo (MCMC) sampling method. We then make an empirical analysis by applying the volatility models to the real return data of the Hang Seng index during the period from January 1, 2013 to December 31, 2017. Our results highlight the accuracy of the stochastic volatility models with leverage in option pricing when leverage is high. In addition, the leverage effect becomes more significant as the maturity of options increases. Moreover, leverage affects the pricing of in-the-money options more than that of at-the-money and out-of-money options. Our study is therefore useful for both asset pricing and portfolio investment in the Hong Kong market where volatility is an inherent nature of the economy.     

Option pricing based on hybrid GARCH-type models with improved ensemble empirical mode decomposition

The exploration of option pricing is of great significance to risk management and investments. One important challenge to existing research is how to describe the underlying asset price process and fluctuation features accurately. Considering the benefits of ensemble empirical mode decomposition (EEMD) in depicting the fluctuation features of financial time series, we construct an option pricing model based on the new hybrid generalized autoregressive conditional heteroskedastic (hybrid GARCH)-type functions with improved EEMD by decomposing the original return series into the high frequency, low frequency and trend terms. Using the locally risk-neutral valuation relationship (LRNVR), we obtain an equivalent martingale measure and option prices with different maturities based on Monte Carlo simulations. The empirical results indicate that this novel model can substantially capture volatility features and it performs much better than the M-GARCH and Black–Scholes models. In particular, the decomposition is consistently helpful in reducing option pricing errors, thereby proving the innovativeness and effectiveness of the hybrid GARCH option pricing model.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Asymmetric impact of earnings news on investor uncertainty

We describe a model that predicts an asymmetric impact of disclosure on investor uncertainty. We show that good news tends to resolve more uncertainty than bad news, and that uncertainty can be revised upwards if the investors' prior belief is sufficiently strong and the signal is sufficiently bad. This result is in contrast to classical disclosure models, where new information always resolves uncertainty and the change in uncertainty depends only on the relative precision of the news. Using option-implied volatility as a proxy for uncertainty, we find strong support for our predictions. We also show that our results are robust to competing explanations, notably to the leverage effect and volatility feedback, as well as to the jump risk induced in anticipation of the earnings announcements.     

Return and volatility spillovers between currency and bond markets in India

This paper examines the return and volatility spillovers between the foreign exchange and bond markets of India using a bivariate asymmetric BEKK-GARCH (1,1) model for the period 4 April 2005 to 31 March 2017. We find the evidence of bidirectional return and volatility spillovers with asymmetric effects between these two markets. The spillovers are evidenced even during the periods when foreign portfolio investments in the Indian bond markets were relatively low suggests the existence of strong inter-linkages between both the markets.     

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.     

Search efficiency,wage dynamics and welfare

Advances in information technology have improved the job-search process in the labor market. We analyze the effects of this improvement by constructing a search-and-matching model with two sectors: a risky sector with firm-specific productivity shocks and a risk-free sector. The risky sector is characterized by a low level of commitment between employers and workers – either party can end the employment relationship. We show that a better job-search process generates more job matches in the risky sector, and this benefits workers by improving their outside options. The effect on employers is subtle: while it is easier to fill vacancies, workers become more expensive. At the same time, the ease of finding new workers makes it harder for employers to keep their wage promises to workers and increases wage volatility. Our paper contributes to the literature by offering a novel explanation for the observed rise in wage volatility.     

The Effect of Corporate Governance on Firm Stock Volatility in Asia

The authors examine whether firm corporate governance (CG) contributes to lower stock-return volatility. Using the panel data of 1,252 public listed firms in Asia across 11 countries for 15 years, the authors document international evidence that CG has a stabilizing effect on firm stock-return volatility. The authors further examine whether increasing information efficiency, reducing foreign exposure, and a lower cost of capital contribute to the stabilizing effect of firm CG on stock-return volatility. The result implies that better CG will only reduce stock-return volatility for firms that have less foreign exposure.     

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.