Timing of Sale,Pricing, and Cost Information: Evidence from the Airline Industry*

This study examines the association between when an airline sells its passenger seats and the pricing method (marginal cost or full cost) it employs. Prior literature suggests that when firms are able to change prices during the selling period, the optimality of full cost pricing or marginal cost pricing depends on when demand information is revealed during the period between capacity commitment decisions and time of sale. Full cost‐based pricing is appropriate in determining capacity commitment and prices simultaneously, while marginal cost provides more relevant information for pricing when capacity has been committed. Using the price and cost data from a sample of four U.S. domestic airlines, we find that full cost explains price variations of first‐day sales robustly. The adjusted R² of the marginal cost pricing model is larger in the sample of sales two days prior to departure than in the sample of first‐day sales. In the analysis of the sample of sales two days prior to departure, we find that, based on the adjusted R² of the full cost pricing and marginal cost pricing models, the explanatory power of marginal cost pricing is relatively weaker than full cost pricing. Our results document the use of different cost information along the dynamic change of price and provide implications in understanding the role of cost information in setting prices.     

Time-varying long-run mean of commodity prices and the modeling of futures term structures

The exploration of the mean-reversion of commodity prices is important for inventory management, inflation forecasting and contingent claim pricing. Bessembinder et al. [J. Finance, 1995, 50, 361–375] document the mean-reversion of commodity spot prices using futures term structure data; however, mean-reversion to a constant level is rejected in nearly all studies using historical spot price time series. This indicates that the spot prices revert to a stochastic long-run mean. Recognizing this, I propose a reduced-form model with the stochastic long-run mean as a separate factor. This model fits the futures dynamics better than do classical models such as the Gibson–Schwartz [J. Finance, 1990, 45, 959–976] model and the Casassus–Collin-Dufresne [J. Finance, 2005, 60, 2283–2331] model with a constant interest rate. An application for option pricing is also presented in this paper.     

Trading Frictions and House Price Dynamics

We model liquidity in housing markets. The model provides a simple characterization for the joint process of prices, sales, and inventory. We compare the implications of the model to certain properties of housing markets. The model can generate the large price changes and the positive correlation between prices and sales that we see in the data. Unlike the data, prices are negatively autocorrelated and high inventory predicts price appreciation. We investigate several amendments to the model. Informational frictions show promise.     

Prediction of Individual Bond Prices via a Dynamic Bond Pricing Model: Application to Japanese Government Bond Price Data

In this paper, we propose a dynamic bond pricing model and report the usefulness of our bond pricing model based on analysis of Japanese Government bond price data. We extend the concept of the time dependent Markov (TDM) model proposed by Kariya and Tsuda (Financial Engineering and the Japanese Markets, Kluwer Academic Publishers, Dordrecht, The Netherlands, Vol. 1, pp. 1–20) to a dynamic model, which can obtain information for future bond prices. A main feature of the extended model is that the whole stochastic process of the random cash-flow discount functions of each individual bond has a time series structure. We express the dynamic structure for the models by using a Bayesian state space representation. The state space approach integrates cross-sectional and time series aspects of individual bond prices. From the empirical results, we find useful evidence that our model performs well for the prediction of the patterns of the term structure of the individual bond returns.     

The Performance of Hedge Fund Strategies and the Asymmetry of Return Distributions

We present hedge fund performance estimates that adjust for stale prices, Fama‐French risk factors and skewness. We contrast these new performance estimates with traditional performance measures. Using three‐factor models to adjust for staleness in prices and to incorporate Fama‐French factors along with the Harvey‐Siddique (2000) two‐factor model that incorporates skewness, we find that for the period 1990–2003, all hedge fund categories achieve above average performance when measured against an aggregate market index. More significantly, however, when we estimate performance at the individual hedge fund level, we discover that only 40 to 47% of the funds are shown to achieve an above average performance over that time period depending on the model used. These results have important implications for investors, endowments and pensions when they choose hedge fund managers.     

A Comparison of Option Prices Under Different Pricing Measures in a Stochastic Volatility Model with Correlation

This paper investigates option prices in an incomplete stochastic volatility model with correlation. In a general setting, we prove an ordering result which says that prices for European options with convex payoffs are decreasing in the market price of volatility risk.As an example, and as our main motivation, we investigate option pricing under the class of q-optimal pricing measures. The q-optimal pricing measure is related to the marginal utility indifference price of an agent with constant relative risk aversion. Using the ordering result, we prove comparison theorems between option prices under the minimal martingale, minimal entropy and variance-optimal pricing measures. If the Sharpe ratio is deterministic, the comparison collapses to the well known result that option prices computed under these three pricing measures are the same.As a concrete example, we specialize to a variant of the Hull-White or Heston model for which the Sharpe ratio is increasing in volatility. For this example we are able to deduce option prices are decreasing in the parameter q. Numerical solution of the pricing pde corroborates the theory and shows the magnitude of the differences in option price due to varying q.JEL Classification: D52, G13     

Strategic Sequential Bidding for Government Land Auction Sales – Evidence from Singapore

This paper studies the extent to which equilibrium land auction prices are pushed up sequentially due to strategic bidding behaviors in government land auction sales. Using a unique dataset that covers the universe of tendering prices submitted by all developers for all residential land auction sales in Singapore, we find that a tenderer’s bids are significantly higher where there was a previous land parcel sold within two years and located within four kilometers. The identified price margin decreases with time and geographic distance. Tracking sequential bids submitted by the same developer over time, we find that the incumbent winner of a previous auction is more likely to participate in subsequent nearby land sales as compared to the second-highest bidder of the same auction. However, it does not necessarily win the subsequent sites. We argue that the incumbent deliberately bids up the subsequent land prices to gain pricing advantages to their own parcels.     

Trade Imbalances and Inventory Effects in Long-term S&P 500 Index Options

This article investigates how trade imbalances affect prices in the S&P 500 Long‐term Equity Anticipation Securities (LEAPS) market. From 1994 to 1996, put volume was 30 times higher than call volume, and public purchases of puts vastly outnumbered sales. We find that LEAPS put quotes are revised following trade imbalances by more than can be explained by information effects, suggesting that put prices are subject to price pressure or inventory effects. The results suggest market frictions are important in the pricing of options, at least in settings in which arbitrage is particularly costly and public demand leans toward one type of order.     

Inventory investment and the cost of capital

We examine the relation between inventory investment and the cost of capital in the time series and the cross section. We find consistent evidence that risk premiums, rather than real interest rates, are strongly negatively related to future inventory growth at the aggregate, industry, and firm levels. The effect is stronger for firms in industries that produce durables rather than nondurables, exhibit greater cyclicality in sales, require longer lead times, and are subject to more technological innovation. We then construct a production-based asset pricing model with two types of capital, fixed capital and inventories, to explain these empirical findings. Convex adjustment costs and a countercyclical price of risk lead to negative time series and cross-sectional relations between expected returns and inventory growth.     

The Crash of ʼ87: Was It Expected? The Evidence from Options Markets

Transactions prices of S&P 500 futures options over 1985-1987 are examined for evidence of expectations prior to October 1987 of an impending stock market crash. First, it is shown that out-of-the-money puts became unusually expensive during the year preceding the crash. Second, a model is derived for pricing American options on jump-diffusion processes with systematic jump risk. The jump-diffusion parameters implicit in options prices indicate that a crash was expected and that implicit distributions were negatively skewed during October 1986 to August 1987. Both approaches indicate no strong crash fears during the 2 months immediately preceding the crash.     

Intertemporal substitution and new car purchases

This article presents a dynamic demand model for motor vehicles. This approach accounts for the change in the mix of consumers over the model year and measures consumers' substitution patterns across products and time. I find intertemporal substitution is significant; consumers are more likely to change the timing of their purchase in reaction to a price increase rather than buy another vehicle in the same period. Further, I find automakers' use of large cash‐back rebates at the end of the model year, although boosting overall sales, induces large numbers of consumers to delay their purchases and so pay lower prices.     

The multivariate Variance Gamma model: basket option pricing and calibration

In this paper, we propose a methodology for pricing basket options in the multivariate Variance Gamma model introduced in Luciano and Schoutens [Quant. Finance 6(5), 385–402]. The stock prices composing the basket are modelled by time-changed geometric Brownian motions with a common Gamma subordinator. Using the additivity property of comonotonic stop-loss premiums together with Gauss-Laguerre polynomials, we express the basket option price as a linear combination of Black & Scholes prices. Furthermore, our new basket option pricing formula enables us to calibrate the multivariate VG model in a fast way. As an illustration, we show that even in the constrained situation where the pairwise correlations between the Brownian motions are assumed to be equal, the multivariate VG model can closely match the observed Dow Jones index options.     

The Value of Corporate Risk Management

We model and estimate the value of corporate risk management. We show how risk management can add value when revenues and costs are nonlinearly related to prices and estimate the model by regressing quarterly firm sales and costs on the second and higher moments of output and input prices. For a sample of 34 oil refiners, we find that hedging concave revenues and leaving concave costs exposed each represent between 2% and 3% of firm value. We validate our approach by regressing Tobin's q on the estimated value and level of risk management and find results consistent with the model.     

A general closed form option pricing formula

A new method to retrieve the risk-neutral probability measure from observed option prices is developed and a closed form pricing formula for European options is obtained by employing a modified Gram–Charlier series expansion, known as the Gauss–Hermite expansion. This expansion converges for fat-tailed distributions commonly encountered in the study of financial returns. The expansion coefficients can be calibrated from observed option prices and can also be computed, for example, in models with the probability density function or the characteristic function known in closed form. We investigate the properties of the new option pricing model by calibrating it to both real-world and simulated option prices and find that the resulting implied volatility curves provide an accurate approximation for a wide range of strike prices. Based on an extensive empirical study, we conclude that the new approximation method outperforms other methods both in-sample and out-of-sample.

     

Long‐run price competition

We generalize the standard repeated‐games model of dynamic oligopolistic competition to allow for consumers who are long‐lived and forward looking. Each period leaves some residual demand to future periods and pricing in one period affects consumers' expectations about future prices. We analyze this setting for an indivisible durable good with price‐setting firms and overlapping cohorts of consumers. The model nests the repeated‐game model and the Coasian durable‐goods model as its two extreme cases. The analysis is mostly focused on constant‐price collusion but conditions for collusive recurrent sales are also identified.     

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.     

Trading strategies based on term structure model residuals

The term structure of interest rates is an important input for basically every pricing model and is mostly calibrated on coupon bond prices. Therefore, the estimated interest rates should accurately explain the market prices of these bonds. However, nearly all empirical papers on interest rate estimation, e.g. Svensson, L.E.O. 1994. Estimating and interpreting forward interest rates: Sweden 1992–1994, IMF Working Paper, International Monetary Fund, report significant pricing errors in their sample. So an important question is what drives these pricing errors of the bonds. One simple explanation would be different tax treatment or different liquidity, but most papers on this research topic, e.g. Elton, E., and T.C. Green. 1998. Tax and liquidity effects in pricing government bonds. Journal of Finance 53: 1533–62, cannot fully explain the observed pricing errors. Therefore, these errors must be at least partially caused by either model misspecification or by the deviation of particular bond prices from general market conditions, i.e. mispricing revealing insufficient market efficiency. We provide empirical evidence for the German government bond market that risk-adjusted trading strategies based on bond pricing errors can yield about 15 basis points p.a. abnormal return compared to benchmark portfolios. Furthermore, the abnormal returns are continuously achieved over the whole time period and not randomly on a few days and show a relation to changes in the level and the curvature of the term structure of interest rates. Therefore, pricing errors contain economic information about deviations of bond prices from general market conditions and are not exclusively caused by model misspecification and/or differences in liquidity and tax treatment of individual bonds.     

The Dynamics of Gasoline Prices: Evidence from Daily French Micro Data

Using millions of individual gasoline prices collected at a daily frequency, we examine the speed at which market prices of refined oil are transmitted to retail gasoline prices in France. For that, we estimate a reduced‐form model of state‐dependent pricing where thresholds triggering price changes are allowed to vary over time and depend on the duration since the last price change. We find that the degree of pass‐through of wholesale prices to retail gasoline prices is on average 0.77 for diesel and 0.67 for petrol and depend on local market characteristics. The duration for a shock to be fully transmitted into prices is about 10 days. There is no significant asymmetry in the transmission of wholesale price to retail prices. Finally, the duration since the last price change has a significant effect on thresholds triggering price changes but a large variance of idiosyncratic shocks on thresholds is also crucial to replicate the size distribution of price changes.     

Stochastic volatility options pricing with wavelets and artificial neural networks

Abstract

The paper describes an alternative options pricing method which uses a binomial tree linked to an innovative stochastic volatility model. The volatility model is based on wavelets and artificial neural networks. Wavelets provide a convenient signal/noise decomposition of the volatility in the nonlinear feature space. Neural networks are used to infer future volatility from the wavelets feature space in an iterative manner. The bootstrap method provides the 95% confidence intervals for the options prices. Market options prices as quoted on the Chicago Board Options Exchange are used for performance comparison between the Black‐Scholes model and a new options pricing scheme. The proposed dynamic volatility model produces as good as and often better options prices than the conventional Black‐Scholes formulae.     

The Stochastic Seasonal Behaviour of Natural Gas Prices

Previous studies have explored the seasonal behaviour of commodity prices as a deterministic factor. This paper goes further by proposing a general (n+2m)‐factor model for the stochastic behaviour of commodity prices, which nests the deterministic seasonal model by Sorensen (2002) . We consider seasonality as a stochastic factor, with n non‐seasonal and m seasonal factors. The non‐seasonal factors are as defined in Schwartz (1997) , Schwartz and Smith (2000) and Cortazar and Schwartz (2003) . The seasonal factors are trigonometric components generated by stochastic processes. The model has been applied to the Henry Hub natural gas futures contracts listed by NYMEX. We find that models allowing for stochastic seasonality outperform standard models with deterministic seasonality. We obtain similar results with other energy commodities. Moreover, we find that stochastic seasonality implies that the volatility of futures returns follows a seasonal pattern. This result has important implications in terms of option pricing.