Libor Market Models versus Swap Market Models for Pricing Interest Rate Derivatives: An Empirical Analysis

We empirically compare Libor and Swap Market Models for thepricing of interest rate derivatives, using panel data on pricesof US caplets and swaptions. A Libor Market Model can directlybe calibrated to observed prices of caplets, whereas a SwapMarket Model is calibrated to a certain set of swaption prices.For both models we analyze how well they price caplets and swaptionsthat were not used for calibration. We show that the Libor MarketModel in general leads to better prediction of derivative pricesthat were not used for calibration than the Swap Market Model.Also, we find that Market Models with a declining volatilityfunction give much better pricing results than a specificationwith a constant volatility function. Finally, we find that modelsthat arechosen to exactly match certain derivative prices areoverfitted; more parsimonious models lead to better predictionsfor derivative prices that were not used for calibration. JELClassification: G12, G13, E43.     

Libor Market Models versus Swap Market Models for Pricing Interest Rate Derivatives: An Empirical Analysis

We empirically compare Libor and Swap Market Models for the pricing of interest rate derivatives, using panel data on prices of US caplets and swaptions. A Libor Market Model can directly be calibrated to observed prices of caplets, whereas a Swap Market Model is calibrated to a certain set of swaption prices. For both models we analyze how well they price caplets and swaptions that were not used for calibration. We show that the Libor Market Model in general leads to better prediction of derivative prices that were not used for calibration than the Swap Market Model. Also, we find that Market Models with a declining volatility function give much better pricing results than a specification with a constant volatility function. Finally, we find that models that are chosen to exactly match certain derivative prices are overfitted; more parsimonious models lead to better predictions for derivative prices that were not used for calibration.     

Bayesian estimators of the lognormal–Pareto composite distribution

In this paper, Bayesian methods with both Jeffreys and conjugate priors for estimating parameters of the lognormal–Pareto composite (LPC) distribution are considered. With Jeffreys prior, the posterior distributions for parameters of interest are derived and their properties are described. The conjugate priors are proposed and the conditional posterior distributions are provided. In addition, simulation studies are performed to obtain the upper percentage points of Kolmogorov–Smirnov and Anderson–Darling test statistics. Furthermore, these statistics are used to compare Bayesian and likelihood estimators. In order to clarify and advance the validity of Bayesian and likelihood estimators of the LPC distribution, well-known Danish fire insurance data-set is reanalyzed.     

Trends in statistically based quarterly cash-flow prediction models

This paper provides a succinct review and synthesis of the literature on statistically based quarterly cash-flow prediction models. It reviews extant work on quarterly cash-flow prediction models including: (1) complex, cross-sectionally estimated disaggregated-accrual models attributed to 0170 and 0175 and Bernard and Stober (1989), (2) parsimonious ARIMA models attributed to Hopwood and McKeown (1992), (3) disaggregated-accrual, time-series regression models attributed to Lorek and Willinger (1996), and (4) parsimonious ARIMA models with both adjacent and seasonal characteristics attributed to 0120 and 0130. Due to the unavailability of long-term cash-flow forecasts attributed to analysts, increased importance has been placed upon the development of statistically based cash-flow prediction models given their use in firm valuation. Specific recommendations are also provided to enhance future research efforts in refining extant statistically based quarterly cash-flow prediction models.     

Explaining Real Commercial Rents Using an Error Correction Model with Panel Data

This paper presents rent models for retail and office property in the United Kingdom. Panel data are used covering eleven regions for 29 years, enabling us to overcome the limitations of a relatively short time series. We use an error correction model (ECM) framework to estimate long-run equilibrium relationships and short-term dynamic corrections. The combination of panel data and an ECM is an innovative approach that is still being developed in economics. We construct new supply series that combine infrequent stock data with more frequent construction data. Separate regional models are estimated for retail and office properties. The regions are then combined into a number of panels on the basis of the income and price elasticities in the long-run and short-run models. Unlike previous studies, we find no evidence of a board north–south divide between low growth and high growth regions. Like these studies we do find a London effect: in London, demand elasticities for space with respect to both price (rent) and income are much lower in magnitude. We conclude that, while the economic drivers may vary, there is no evidence of differences in the operation of the regional property markets outside London. Elasticities for retail and office are similar. Our final models are parsimonious with single measures of economic activity and of supply and always support the use of an ECM.     

Bayesian range-based estimation of stochastic volatility models

Alizadeh, Brandt, and Diebold [2002. Journal of Finance 57, 1047–1091] propose estimating stochastic volatility models by quasi-maximum likelihood using data on the daily range of the log asset price process. We suggest a related Bayesian procedure that delivers exact likelihood based inferences. Our approach also incorporates data on the daily return and accommodates a nonzero drift. We illustrate through a Monte Carlo experiment that quasi-maximum likelihood using range data alone is remarkably close to exact likelihood based inferences using both range and return data.     

Improved estimation of the covariance matrix of stock returns with an application to portfolio selection

This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and single-index covariance matrix. This method is generally known as shrinkage, and it is standard in decision theory and in empirical Bayesian statistics. Our shrinkage estimator can be seen as a way to account for extra-market covariance without having to specify an arbitrary multifactor structure. For NYSE and AMEX stock returns from 1972 to 1995, it can be used to select portfolios with significantly lower out-of-sample variance than a set of existing estimators, including multifactor models.     

The Construction of Residential Housing Price Indices: A Comparison of Repeat-Sales,Hedonic-Regression,and Hybrid Approaches

This article examines a number of hypotheses that underpin the repeat-sales and hedonic approaches to the construction of housing price indices, as well as the practical problems associated with the implementation of either approach. We also examine a hybrid procedure that combines elements of both the repeat-sales and hedonic-regression techniques. For our sample of individual home sales in Oakland and Fremont California over an 18-year period, repeat-sales methods are subject to sample selection bias; the maintained assumption of time constancy of implicit prices of housing attributes is violated; the repeat-sales estimator is extremely sensitive to influential observations; and the usual method used to correct for heteroskedasticity in repeat-sale housing returns is inappropriate in our sample. Hedonic techniques are better suited to contend with index number problems per se, as they can accommodate changing attribute prices over time. They also appear to give rise to more reliable estimates of price indices, as unusual observations have less effect on estimated price indices. Drawbacks of the hedonic approach include the usual concern with omitted attributes, and their effect on the estimated price index.     

A Forecasting Model for Stock Market Diversity

We apply the recently introduced generalized tree-structured (GTS) model to the analysis and forecast of stock market diversity. Diversity is a measure of capital concentration across a market that plays a central role in the search for arbitrage. The GTS model allows for different conditional mean and volatility regimes that are directly related to the behavior of macroeconomic fundamentals through a binary threshold construction. Testing on US market data, we collect empirical evidence of the model’s strong potential in estimating and forecasting diversity accurately in comparison with other standard approaches. In addition, the GTS model allows for the construction of very simple portfolio strategies that systematically beat the standard cap-weighted S&P500 index. Financial support by the Foundation for Research and Development of the University of Lugano and by the National Centre of Competence in Research “Financial Valuation and Risk Management” (NCCR FINRISK) is gratefully acknowledged. The authors thank four anonymous referees for helpful comments.     

The Role of Stock Size and Trading Intensity in the Magnitude of the "Interval Effect" in Beta Estimation: Empirical Evidence from the Polish Capital Market

In this paper, we present empirical evidence about the "interval effect" in estimation of beta parameters for stocks listed on the Warsaw Stock Exchange. We analyze models constructed for the returns calculated using intervals of different length—that is, 1, 5, 10, and 21 trading days (corresponding to, roughly, 1 day, 1 week, 2 weeks, and 1 month, respectively). In the cases in which heteroskedasticity was present, we estimated ARCH models. The results indicate that the estimates of betas for the same stock differ considerably when various return intervals are used. We further explore the source of differences in betas for every stock by investigating the relations between them and such factors as stock size and its trading intensity. The empirical results provide evidence that a statistically significant relationship exists between these two characteristics of stocks. This finding has important practical implications for beta estimation in practice.     

Beyond Single-Factor Affine Term Structure Models

This article proposes a new approach to testing for the hypothesisof a single priced risk factor driving the term structure ofinterest rates. The method does not rely on any parametric specificationof the state variable dynamics or the market price of risk.It simply exploits the constraint imposed by the no-arbitragecondition on instantaneous expected bond returns. In order toachieve our goal, we develop a Kolmogorov-Smirnov test and applyit to data on Treasury bills and bonds for both the United Statesand Spain. We find that the single risk factor hypothesis cannotbe rejected for either dataset.     

An Extended Set of Risk Neutral Valuation Relationships for the Pricing of Contingent Claims

This article establishes an extended set of risk neutral valuation relationships (RNVR's), assuming a representative agent who has an extended power utility function, of the HARA class of utility functions. The utility function of the representative agent displays either increasing, constant or decreasing proportional risk aversion. Aggregate consumption and the random payoff of the underlying asset are bivariate three-parameter lognormal distributed. As an application of the RNVR's, closed-form solutions for the price of a call written on a stock are derived. These include an extra parameter, the threshold parameter, not contained in the Black-Scholes formula.