A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility

We show that, for three common SARV models, fitting a minimummean square linear filter is equivalent to fitting a GARCH model.This suggests that GARCH models may be useful for filtering,forecasting, and parameter estimation in stochastic volatilitysettings. To investigate, we use simulations to evaluate howthe three SARV models and their associated GARCH filters performunder controlled conditions and then we use daily currency andequity index returns to evaluate how the models perform in arisk management application. Although the GARCH models produceless precise forecasts than the SARV models in the simulations,it is not clear that the performance differences are large enoughto be economically meaningful. Consistent with this view, wefind that the GARCH and SARV models perform comparably in testsof conditional value-at-risk estimates using the actual data.     

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.     

Observable Consequences of Trading Structure Differences: On the Use of Variance Ratios in Microstructure Studies

This paper examines the variance ratio tests in studies of transitory volatility and concludes that the variance ratio is an appropriate test of trading structure differences only under certain assumptions regarding the evolution of underlying stock prices and the autocorrelation structure of returns. This result raises caution as to the interpretation of results bases upon the 24-hour variance ratio methodologies in studies of transitory volatility and trading structure effects. A numerical example indicates that errors in inferences can be severe.     

Finite Dimensional Affine Realisations of HJM Models in Terms of Forward Rates and Yields

Finite dimensional Markovian HJM term structure models provide ideal settings for the study of term structure dynamics and interest rate derivatives where the flexibility of the HJM framework and the tractability of Markovian models coexist. Consequently, these models became the focus of a series of papers including Carverhill (1994), Ritchken and Sankarasubramanian (1995), Bhar and Chiarella (1997), Inui and Kijima (1998), de Jong and Santa-Clara (1999), Björk and Svensson (2001) and Chiarella and Kwon (2001a). However, these models usually required the introduction of a large number of state variables which, at first sight, did not appear to have clear links to the market observed quantities, and the explicit realisations of the forward rate curve in terms of the state variables were unclear. In this paper, it is shown that the forward rate curves for these models are affine functions of the state variables, and conversely that the state variables in these models can be expressed as affine functions of a finite number of forward rates or yields. This property is useful, for example, in the estimation of model parameters. The paper also provides explicit formulae for the bond prices in terms of the state variables that generalise the formulae given in Inui and Kijima (1998), and applies the framework to obtain affine representations for a number of popular interest rate models.     

Binomial Option Pricing with Skewed Asset Returns

This research presents a method for estimating the parameters of the binomial option pricing model necessary to appropriately price calls on assets with asymmetric end-of-period return distributions. Parameters of the binomial model are shown to be a function of the mean, variance, and skewness of the underlying return distribution. It is also shown that failure to incorporate skewness results in the mispricing of the call.     

Dynamic analysis of stock return volatility in an integrated international capital market

This paper examines the dynamic behavior of the stock return volatility for Canada, Japan, Germany, and the United Kingdom. The evidence indicates that international stock return volatility is mainly influenced by the U.S. stock return volatility and the exchange rate volatility, supporting the international capital market integration hypothesis. There seems to be some correlation between stock return volatility and macroeconomic volatility, but the effect is relatively weaker. In addition to the economic fundamentals, the noise component is found to be time varying, confirming the AR(MA)CH specifications in the stock return models.     

R&D Progress, stock price volatility, and post-announcement drift: An empirical investigation into biotech firms

I investigate the effects of R&D progress on the dynamics of stock price volatility and the post announcement drift to provide insights into whether or not and how capital markets react to corporate R&D progress in the context of the biotech industry. I find both stock price volatility and the post announcement drift decrease in R&D progress. More importantly, the decrease is proportional to the increase in the drug development success rate driven by R&D progress. Findings suggest that R&D progress conveys useful risk-relevant information, and plays an important role in explaining stock price volatility change and market anomalies.     

Modelling return and conditional volatility exposures in global stock markets

This article empirically investigates the exposure of country-level conditional stock return volatilities to conditional global stock return volatility. It provides evidence that conditional stock market return volatilities have a contemporaneous association with global return volatilities. While all the countries included in the study exhibited a significant and positive relationship to global volatility, emerging market volatility exposures were considerably higher than developed market exposures. JEL Classification G12     

Consistency conditions for affine term structure models

Sufficient conditions for the application of the Feynman-Kac formula for option pricing for wide classes of affine term structure models in the jump-diffusion case are derived by generalizing earlier results for bond pricing in the pure-diffusion case The author is grateful to Mikhail Chernov and Darrel Duffie for useful discussions and suggestions.     

Do the Forward Sales of Real Estate Stabilize Spot Prices?

We examine the effect of forward sale (pre-sale) activities on the volatility of spot prices in the real estate market. The abundance of pre-sales data and major changes in regulatory control on the pre-sale market during the 90's in Hong Kong allow us to undertake empirical tests using Hong Kong's real estate data. Our results show that the volatility of spot prices increased significantly after forward sales were severely dampened by regulatory control measures introduced in 1994, but decreased again when the measures were partly relaxed in 1998. The results contribute to the long lasting debate on whether the introduction of a futures market reduces the volatility of spot prices. Previous studies were mainly conducted in markets with low transaction costs, notably financial markets. By utilizing the unique regulatory changes in the pre-sale market of Hong Kong, we are able to conduct an experiment on the conditional volatility of spot prices in a high information-cost environment, thereby shedding light on the important role of forward housing contracts in providing price expectation information for spot trading.