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The implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable features for representing the surface and its dynamics: a general dynamic factor model, restricted factor models designed to capture the key features of the surface along the moneyness and maturity dimensions, and in-between spline-based methods. Key findings are that: (i) the restricted and spline-based models are both rejected against the general dynamic factor model, (ii) the factors driving the surface are highly persistent, and (iii) for the restricted models option Δ is preferred over the more often used strike relative to spot price as measure for moneyness.

The aim of this study is to examine the “volatility smile” or/and “skew”, term structure and implied volatility surfaces based on those European options written in the standard and poor (S&P) Nifty equity index. The stochastic nature of implied volatility across strike price, time-to-expiration and moneyness violates the core assumption of the Black–Scholes option pricing model.

The potential determinants of implied volatility are the degree of moneyness, time-to-expiration and the liquidity of the strikes. The empirical work has been expressed by means of a simple ordinary least squares (OLS) framework and presents the estimation results according to moneyness, time-to-expiration and liquidity of options.

The options data give evidence of the existence of a classical U-shaped volatility smile for the Indian options market. Indeed, there is some evidence that the “volatility smirk” which pertains to 30-day options and also implied volatility remain higher for the shorter maturity options and decrease as the time-to-expiration increases. The results lead us to believe that in-the-money calls and out-of-the-money puts are of higher volatility than at-the-money options. Conclusion was drawn due to the persistence of the smile in the options market.

The practical implication of studying stylized patterns of implied volatility is that it educates the volatility traders about how in-the-money and out-of-the-money options are priced in the options market, and provides an estimate of volatility for the pricing of future options.

This study is an extension of previous work. The undertaking has been to examine the case of a more liquid and transparent options market, which is missing from the earlier work. The current study is more relevant because, since 2008, significant changes have been observed in the futures and options market in India.

This paper aims to describe a robust empirical approach to generating plausible historically based interest rate shocks, which can be applied to any market environment. These interest rate shocks can be readily linked to movements in other key risk factors, and used to measure market risk on institutions with large fixed-income portfolios.

Using yield curve factorization, we parameterize a time series of historical yield curves and measure interest rate shocks as the historical change in each of the model’s factors. We then demonstrate how to add these parameterized shocks to any market environment, while retaining positive rates and plausible credit spreads. Given a set of shocked interest rate curves, joint risk factor movements are calculated based upon historical, reduced form dependencies.

Our approach is based upon yield curve parameterization and requires a parsimonious yet flexible factorization model. In the process of selecting a model, we evaluate three variants of the Nelson–Siegel approach to yield curve approximation and find that, in the current low interest rate environment, a 5-factor parameterization developed by Björk and Christensen (1999) is best suited for accurately translating historical interest rate movements into plausible, current period shocks.

An accurate measure of market risk can help to inform institutions about the amount of capital needed to withstand a series of adverse market events. A plausible set of shocks is required to ensure market value, and cash flow projections are indicative of meaningful market sensitivities.

Due to disequilibrium between supply and demand in the option market, the option market‐maker is under exposure to certain risks because of their net option positions. This paper aims to pay attention to whether the risk award affects the option price and the shape of implied volatility in the market‐maker system.

The paper first eliminates the part of implied volatility explained by underlying asset's stochastic volatility‐jump price process, and second sorts out market investors' net demand data from TAIEX Options tick by tick deal data and then finally considers three market maker's risks – unhedgeable risk, capital constrain risk and asymmetric information risk, and how they affect implied volatility's level and slope.

Through the research in the TAIEX Option market, the paper finds that, under unhedgeable risk, net demand pressure has a significant impact on implied volatility. Especially, unhedgeable risk due to underlying asset's stochastic volatility has the best explanation for implied volatility level, and unhedgeable risk due to underlying asset's jump can explain implied volatility slope to some extent. Capital constrain risk and asymmetric information risk have an insignificant impact on implied volatility.

The findings in this study suggest that the risk award affects the option price and the shape of implied volatility in the market‐maker system and different risks have different effects on the level and slope of option implied volatility.

This paper finds the influence factors of the option price in the market‐maker system. It's useful for China's financial government and investors to learn the price tendency and regular pattern in the future China option market.

This is the first time that a net demand pressure based option pricing model is used, which is derived by Garleanu, Pedersen and Poteshman, to study the TAIEX Options' implied volatility. And the paper improves the methods eliminating the part of implied volatility explained by underlying asset's stochastic volatility‐jump price process.

The purpose of this paper is to ascertain the pattern of the implied volatility function for currency options traded on the National Stock Exchange of India (NSE), identify its potential determinants and to investigate any seasonality in the pattern.

The paper examines four different specifications for the implied volatility smile of exchange-traded dollar-rupee options. These specifications are tested by running Ordinary Least Squares (OLS) regressions on a daily basis for all options over the entire sample period. Seven potential determinants for the shape of the volatility function are identified. Contemporaneous and lead-lag relationships between these determinants and the shape of the volatility function are examined using OLS and multivariate VAR. Impulse response functions are employed to test the strength and persistence of the lead-lag relations. Seasonality of the smile pattern is tested using OLS.

The study shows that the implied volatility function for dollar-rupee options is asymmetric and varies with the time to maturity of the option. Historical volatility, momentum and jumps in the exchange rate, time to maturity, traded volume of options and volatility in the stock market appear to Granger-cause the shape of the volatility smile. Feedback causality is observed from the shape of the smile to the volatility, momentum and jumps in the exchange rate and trading volume of currency options. A weak day-of-the-week effect is observed in the pattern of the volatility smile.

The study sheds light on the potential determinants of the smile and highlights the predictive power of the smile which findings can be useful to market practitioners for pricing and hedging of dollar-rupee options. The study has strong practical implications during a period of increased volatility in the dollar-rupee pair.

Most of the existing literature regarding implied volatility smiles has focused either on the volatility smile of US equity index options or that of major liquid currencies. There is a need for such studies in the context of options on emerging market currencies such as the Indian rupee which are characterized by thin trading and frequent central bank intervention and signaling. To the best of the author’s knowledge this study is the first to focus on the volatility smile of exchange-traded options on the US dollar–Indian rupee.

The volatility of a financial asset is an important input for financial decision‐making in the context of asset allocation, option pricing, and risk management. The authors compare and contrast four approaches to stochastic volatility to determine which is most appropriate to each of these various needs.

The purpose of this paper is to investigate the relationship between option’s implied volatility smirk (IVS) and excess returns in the Germany’s leading stock index Deutscher-Aktien Index (DAX) 30.

The study defines the IVS as the difference in implied volatility derived from out-of-the-money put options and at-the-money call options. This study employs the ordinary least square regression with Newey-West correction to analyse the relationship between IVS and excess DAX 30 index returns in Germany.

The authors find that the German market adjusts information in an efficient way. Consequently, there is no information linkage between option volatility smirk and market index returns over the nine years sample period after considering the control variables, global financial crisis dummies, and the subsample test.

This study finds that the option market and the DAX 30 index are informationally efficient. Implications of the findings are that the investors cannot profit from the information contained in the IVS since the information is simultaneously incorporated into option prices and the stock index prices. The findings of this study are applicable to other markets with European options and for market participants who seek to exploit short-term market divergence from efficiency.

The relationship between IVS and stock price changes has not been investigated sufficiently in academic literature. This study looks at this relationship in the context of European options using high-frequency transactions data. Prior studies look at this relationship for only American options using daily data. Pricing efficiency of the European option market using high-frequency data have not been studied in the prior literature. The authors find different results for the German market based on this high-frequency data set.

Previous studies have shown the VIX futures tend to roll-down the term structure and converge towards the spot as they grow closer to maturity. The purpose of this paper is to propose an approach to improve the volatility index fear factor-level (VIX-level) prediction.

First, the authors use a forward-looking technique, the Heath–Jarrow–Morton (HJM) no-arbitrage framework to capture the convergence of the futures contract towards the spot. Second, the authors use principal component analysis (PCA) to reduce dimensionality and save substantial computational time. Third, the authors validate the model with selected VIX futures maturities and test on value-at-risk (VAR) computations.

The authors show that the use of multiple factors has a significant impact on the simulated VIX futures distribution, as well as the computations of their VAR (gain in accuracy and computing time). This impact becomes much more compelling when analysing a portfolio of VIX futures of multiple maturities.

The authors’ approach assumes the variance to be stationary and ignores the volatility smile. Nevertheless, they offer suggestions for future research.

The VIX-level prediction (the fear factor) is of paramount importance for market makers and participants, as there is no way to replicate the underlying asset of VIX futures. The authors propose a procedure that provides efficiency to both pricing and risk management.

This paper is the first to apply a forward-looking method by way of a HJM framework combined with PCA to VIX-level prediction in a portfolio context.