Ihsan Ullah Badshah

Asymmetric Return-Volatility Relation, Volatility Transmission and Implied Volatility Indexes

The purpose of this study is twofold: First, to investigate the asymmetric return-volatility phenomenon with newly adapted robust volatility indexes VIX, VXN, VDAX and VSTOXX. Second, we examine the dynamic implied volatility transmissions across the implied volatility indexes using techniques such as Granger causality, generalized impulse response function and variance decomposition. We find pronounced negative and asymmetric return-volatility relationships between each volatility index and its corresponding stock market index. The VIX volatility index presents the highest asymmetric return-volatility relationship followed by the VSTOXX, VDAX and VXN volatility indexes, respectively. Moreover, there are significant spillover effects across the volatility indexes, bi-directional causality running between the volatility indexes. Further, in innovation accounting investigations, the VIX volatility index influences the other three volatility indexes considerably. However, in the European context, VDAX is the dominant source of information. Our findings have implications for trading strategies, hedging portfolios, pricing and hedging volatility derivatives, and risk management.

Modeling the Dynamics of Implied Volatility Surfaces

The purpose of this study is to model implied volatility surfaces and identify risk factors that account for most of the randomness in the volatility surfaces. The approach is similar to that of the Dumas, Fleming and Whaley (DFW) (1998) study. We use moneyness in implied forward price and out-of-the-money put-call options on the FTSE 100 stock index. After adjustments, a nonlinear parametric optimization technique is used to estimate different DFW models to characterize and produce smooth implied volatility surfaces. Next, principal component analysis is applied to the implied volatility surfaces to extract principal components that account for most of the dynamics in the shape of the surfaces. We then estimate and obtain smooth implied volatility surfaces with the parametric models that account for both smirk(skew) and time to maturity. We find the constant volatility model fails to explain the variations in the surfaces. However, the first three principal components (or factors) can explain about 69-88% of the variances in the implied volatility surfaces: in which on average 56% explains by the level factor, 15% by the term structure factor, and additional 7% by the jump-fear factor. The applications of our study can be in options trading, hedging of derivatives positions, risk management of options, and policy making.

Volatility Spillover from the Fear Index to Developed and Emerging Markets

ABSTRACT This article examines cross-market volatility linkages among the fear index (VIX), the developed-market index (VXEFA), and the emerging-market index (VXEEM). Analysis on the first moments of volatilities reveals that the fear index has a leading role and has information content for VXEFA and VXEEM. A shock to the fear index spillovers to VXEFA and VXEEM and contributes 57.07% and 63.77% to their shocks, respectively. Further analysis on the second moments of volatilities confirms that the volatility indices are highly dynamically correlated while the fear index drives the correlation dynamics with the VXEEM. Correlations increase in turbulent periods and decrease in tranquil periods.

Testing the Information-Based Trading Hypothesis in the Option Market: Evidence from Share Repurchases

The informed options trading hypothesis posits that option prices lead stock prices. In this paper, we extended the research on this hypothesis to open-market share repurchases. Empirical tests showed that the implied volatility spread was not significantly related to buy-and-hold abnormal stock returns. However, further evidence reveal a significant relationship between implied volatility spread and subsequent stock return volatility around open-market share repurchase events. We concluded that option traders have private information on the volatility of stock returns and superior information processing ability that accounts for prescient pricing behavior in options relative to stocks.