Charles River Associates

About: Charles River Associates is a company organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Competition (economics) & Price discrimination. The organization has 287 authors who have published 557 publications receiving 22915 citations.

Modeling and Forecasting Realized Volatility

Abstract: This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariance matrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen /Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatitilies perform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quintile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.

Modeling and forecasting realized volatility

Abstract: This paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariance matrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark / Dollar and Yen / Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quantile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.

Choosing How to Compete: Strategies and Tactics in Standardization

Abstract: In a network industry, each firm must decide whether or not it wants its product to be compatible with those of rivals. This horizontal compatibility strategy determines whether competition is a battle to establish a standard or the more conventional competition within a standard. The two forms of competition involve different tactics and may differ in the extent to which they dissipate industry profits. In some cases, all firms in an industry may prefer the same form of competition. In other cases, firms may prefer different forms of competition and either may prevail.

Measuring the quality of relationships in consumer services: an empirical study

Abstract: Increasingly, firms are recognizing the value of establishing close relationships with their customers as a means of retaining existing customers. Also, firms are realizing that the intangible aspects of a relationship are not easily duplicated by competition, thus providing a sustainable competitive advantage to the firm. In this paper, we provide firms with a scale for measuring the quality of these intangible relationships between service firms and their customers. We then test this scale against the related, yet dissimilar scale for service quality to determine whether the relationship quality (RQ) scale adds any further explanation of behavioral intentions. Our results indicate that relationship quality is a distinct construct from service quality and that RQ is a better predictor of behavioral intentions than service quality.