George S. Skiadopoulos

The Dynamics of the S&P 500 Implied Volatility Surface

This empirical study is motivated by the literature on “smile-consistent” arbitrage pricing with stochastic volatility. We investigate the number and shape of shocks that move implied volatility smiles and surfaces by applying Principal Components Analysis. Two components are identified under a variety of criteria. Subsequently, we develop a “Procrustes” type rotation in order to interpret the retained components. The results have implications for both option pricing and hedging and for the economics of option pricing.

Predictable Dynamics in Higher Order Risk-Neutral Moments: Evidence from the S&P 500 Options

We investigate whether there are predictable patterns in the dynamics of higher order risk-neutral moments extracted from the market prices of S&P 500 index options. To this end, we conduct a horse race among alternative forecasting models within an out-of-sample context over various forecasting horizons. We consider both a statistical and an economic setting. We find that higher risk-neutral moments can be statistically forecasted. However, only the one-day-ahead skewness forecasts can be economically exploited. This economic significance vanishes once we incorporate transaction costs. The results have implications for the dynamics of implied volatility surfaces.

Measuring the Market Risk of Freight Rates: A Value-at-Risk Approach

The fluctuation of shipping freight rates (freight rate risk) is an important source of market risk for all participants in the freight markets including hedge funds, commodity and energy producers. We measure the freight rate risk by the Value-at-Risk (VaR) approach. A range of parametric and non-parametric VaR methods is applied to various popular freight markets for dry and wet cargoes. Backtesting is conducted in two stages by means of statistical tests and a subjective loss function that uses the Expected Shortfall, respectively. We find that the simplest non-parametric methods should be used to measure freight rate risk. In addition, freight rate risk is greater in the wet cargoes markets. The margins in the growing freight derivatives markets should be set accordingly.

The Dynamics of Implied Volatility Surfaces

Motivated by the papers of Dupire (1992) and Derman and Kani (1997), we want to investigate the number of shocks that move the whole implied volatility surface, their interpretation and their correlation with percentage changes in the underlying asset. This work differs from Skiadopoulos, Hodges and Clewlow (1998) in which they looked at the dynamics of smiles for a given maturity bucket. We look at daily changes in implied volatilities under two different metrics: the strike metric and the moneyness metric. Since we are dealing with a three dimensional problem, we fix ranges of days to maturity, we pool them together and we apply the Principal Components Analysis (PCA) to the changes in implied volatilities over time across both the strike (moneyness) metric and the pooled ranges of days to maturity. We find similar results for both metrics. Two shocks explain the movements of the volatility surface, the first shock being interpreted as a shift, while the second one has a Z-shape. The sign of the correlation of the first shock with percentage changes in the underlying asset depends on the metric that we look at, while the sign is positive under both metrics regarding the second shock. The results suggest that the number of shocks, their interpretation and the sign of their correlation with changes in the underlying asset is the same for the whole implied volatility surface as it is for the smile corresponding to a fixed maturity bucket.

Advances in the Commodity Futures Literature: A Review

Investors seeking yield in difficult market conditions are increasingly considering “alternative” asset classes. These include commodities, which are mostly accessed through commodity futures. In this article, Skiadopoulos examines the evidence in the finance literature regarding three major issues that arise in treating commodity futures as investment assets. The first question is how much diversification benefit is gained through commodity futures? Much of the reported evidence suffers from methodological problems, such as failure to examine out-of-sample performance or to take account of the transactions costs imposed by the frequent rollover of expiring futures contracts. A second question is to what extent our familiar factor models for equities can be applied to commodity returns. The answer so far is that finding dependable common factors is not easy. Third, given the regulatory changes underway in Washington and elsewhere, he considers the evidence on how margins and margin changes affect futures markets. Overall, the article raises important questions about the use of commodity futures as an asset class in traditional investment portfolios, and shows that the answers available in the literature are less supportive than one might have liked.

The Effect of Mis-Estimating Correlation on Value-at-Risk

Value at risk (VaR) is an estimate of the worst loss over a target horizon with a given level of confidence. Despite its shortcomings, the Basel Committee on Banking Supervision has chosen it as the standard method to measure the market risk of a portfolio of financial assets. This measure of risk is widely used by practitioners and regulators because of its conceptual simplicity and flexibility. This article examines the systematic relationship between correlation misestimation and the corresponding value-at-risk miscalculation. To this end, first a semi-parametric approach, and then a parametric approach is developed. Both approaches are based on a simulation setup. Various linear and non-linear portfolios are considered, as well as variance-covariance and Monte-Carlo simulation methods are employed. Results show that the VaR error increases significantly as the correlation error increases, particularly in the case of well-diversified linear portfolios. In the case of option portfolios, this effect is more pronounced for short-maturity, in-the-money options. The use of MC simulation to calculate VaR magnifies the correlation bias effect. These results have important implications for measuring market risk accurately.

Simulating the Evolution of the Implied Distribution

Motivated by the implied stochastic volatility literature (Britten–Jones and Neuberger, forthcoming; Derman and Kani, 1997; Ledoit and Santa–Clara, 1998) this paper proposes a new and general method for constructing smile–consistent stochastic volatility models. The method is developed by recognising that option pricing and hedging can be accomplished via the simulation of the implied risk neutral distribution. We devise an algorithm for the simulation of the implied distribution, when the first two moments change over time. The algorithm can be implemented easily, and it is based on an economic interpretation of the concept of mixture of distributions. It can also be generalised to cases where more complicated forms for the mixture are assumed.

Implied Volatility Trees and Pricing Performance: Evidence from the S&P 100 Options

This paper examines the pricing performance of various discrete-time option models that accept the variation of implied volatilities with respect to the strike price and the time-to-maturity of the option (implied volatility tree models). To this end, data from the S&P 100 options are employed for the first time. The complex implied volatility trees are compared to the standard Cox–Ross–Rubinstein model and the ad-hoc traders model. Various criteria and interpolation methods are used to evaluate the performance of the models. The results have important implications for the pricing accuracy of the models under scrutiny and their implementation.

Modeling the Dynamics of Temperature with a View to Weather Derivatives

The accurate specification of the process that the temperature follows over time is a prerequisite for the pricing of temperature derivatives. To this end, a horse race of alternative specifications of the dynamics of temperature is conducted by evaluating their out-of-sample forecasting performance under different evaluation metrics and forecast horizons. An extensive dataset of the daily average temperature measured at different locations in Europe and the United States is employed. We find that a developed principal components model and a combination forecasts model perform best in the United States and Europe, respectively. Point forecasts for popular temperature indices are formed, as well. The results have implications for the pricing and trading of the fast growing class of temperature derivatives, as well as for forecasting temperature.

Capital Structure and Financial Flexibility: Expectations of Future Shocks

We test one of the main predictions of the financial flexibility paradigm that expectations about future firm-specific shocks affect the firms leverage. We extract the expectations of small and large future shocks from the market prices of equity options. We find that expectations for future shocks decrease leverage and are statistically significant even when we control for traditional determinants. Moreover, they have a first-order effect to capital structure decisions affecting more the small and financially constrained firms. Our findings confirm the De Angelo et al. (2011) model predictions and evidence drawn from surveys that managers seek for financial flexibility.