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Return Models (return + models)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	75%

Selected Abstracts

Market Valuation of Research and Development Spending under Canadian GAAP,

ACCOUNTING PERSPECTIVES, Issue 1 2004
ANTONELLO CALLIMACI
ABSTRACT Section 3450 of the Canadian Institute of Chartered Accountants (CICA) Handbook requires Canadian firms to capitalize development costs that meet certain criteria and to expense those that relate to research. International Accounting Standard (IAS) No. 38 favours a similar approach. In the United States, Statement of Financial Accounting Standard (SFAS) No. 2 recommends the immediate expensing of all research and development (R&D) spending. The only exception is SFAS No. 86, which requires software development costs to be capitalized when a product successfully passes a technological feasibility test. Consequently, the Canadian financial disclosure regime provides a rich setting for testing the market valuation of capitalized R&D. Our primary research question asks whether capitalized R&D provides useful information to market participants investing in Canadian firms. We use price-level and return models to assess the value relevance of capitalized R&D disclosed in the financial statements under Canadian GAAP. In line with expectations, using a price-level model, we find that capitalized R&D and R&D expense as disclosed in the financial statements provide information that is value relevant to market participants. However, we find that R&D capitalized during the year helps explain returns while R&D expense does not. Thus we conclude that the application of section 3450 of the CICA Handbook produces value-relevant information. [source]

Testing Option Pricing Models with Stochastic Volatility, Random Jumps and Stochastic Interest Rates

INTERNATIONAL REVIEW OF FINANCE, Issue 3-4 2002
George J. Jiang
In this paper, we propose a parsimonious GMM estimation and testing procedure for continuous-time option pricing models with stochastic volatility, random jump and stochastic interest rate. Statistical tests are performed on both the underlying asset return model and the risk-neutral option pricing model. Firstly, the underlying asset return models are estimated using GMM with valid statistical tests for model specification. Secondly, the preference related parameters in the risk-neutral distribution are estimated from observed option prices. Our findings confirm that the implied risk premiums for stochastic volatility, random jump and interest rate are overall positive and varying over time. However, the estimated risk-neutral processes are not unique, suggesting a segmented option market. In particular, the deep ITM call (or deep OTM put) options are clearly priced with higher risk premiums than the deep OTM call (or deep ITM put) options. Finally, while stochastic volatility tends to better price long-term options, random jump tends to price the short-term options better, and option pricing based on multiple risk-neutral distributions significantly outperforms that based on a single risk-neutral distribution. [source]

Econometric modelling of non-ferrous metal prices

JOURNAL OF ECONOMIC SURVEYS, Issue 5 2004
Clinton Watkins
Abstract., This article evaluates the significance of the empirical models and the distributional properties of prices in non-ferrous metal spots and futures markets published in leading refereed economics and finance journals between 1980 and 2002. The survey focuses on econometric analyses of pricing and return models applied to exchange-based spot and futures markets for the main industrially used non-ferrous metals, namely aluminium, copper, lead, nickel, tin and zinc. Published empirical research is evaluated in the light of the type of contract examined, frequency of data used, choice of both dependent and explanatory variables, use of proxy variables, type of model chosen, economic hypotheses tested, methods of estimation and calculation of SEs for inference, reported descriptive statistics, use of diagnostic tests of auxiliary assumptions, use of nested and non-nested tests, use of information criteria and empirical implications for non-ferrous metals. [source]

Temporal aggregation of Markov-switching financial return models

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2009
Wai-Sum Chan
Abstract In this paper we investigate the effects of temporal aggregation of a class of Markov-switching models known as Markov-switching normal (MSN) models. The growing popularity of the MSN processes in modelling financial returns can be attributed to their inherited flexibility characteristics, allowing for heteroscedasticity, asymmetry and excess kurtosis. The distributions of the process described by the basic MSN model and the model of the corresponding temporal aggregate data are derived. They belong to a general class of mixture normal distributions. The limiting behaviour of the aggregated MSN model, as the order of aggregation tends to infinity, is studied. We provide explicit formulae for the volatility, autocovariance, skewness and kurtosis of the aggregated processes. An application of measuring solvency risk with MSN models for horizons larger than 1 year and up to 10 years from the baseline U.S. S&P 500 stock market total return time series spanning about 50 years is given. Copyright © 2008 John Wiley & Sons, Ltd. [source]