A randomised phase II study of two different schedules of pegylated liposomal doxorubicin in metastatic breast cancer (EORTC-10993).

One hundred and sixteen women with measurable metastatic breast cancer participated in a randomised phase II study of single agent liposomal pegylated doxorubicin (Caelyx) given either as a 60 mg/m2 every 6 weeks (ARM A) or 50 mg/m2 every 4 weeks (ARM B) schedule. Patients were over 65 years of age or, if younger, had refused or been unsuitable for standard anthracyclines. The aims of the study were to evaluate toxicity and dose delivery with the two schedules and obtain further information on the response rate of liposomal pegylated doxorubicin as a single agent in anthracycline nai ve advanced breast cancer. Twenty-six patients had received prior adjuvant chemotherapy (including an anthracycline in 10). Sixteen had received non-anthracycline-based first-line chemotherapy for advanced disease. One hundred and eleven patients were evaluable for toxicity and 106 for response. The delivered dose intensity (DI) was 9.8 mg/m2 (95% CI, 7.2-10.4) with 37 (69%) achieving a DI of >90% on ARM A and 11.9 mg/m2 (95% CI, 7.5-12.8) with 37 (65%) achieving a DI of >90% on ARM B. The adverse event profiles of the two schedules were distinctly different. Mucositis was more common with the every 6 weeks regimen (35% CTC grade 3/4 in ARM A, 14% in ARM B) but palmar plantar erythrodysesthesia (PPE) was more frequent with the every 4 weeks regimen (2% CTC grade 3/4 in ARM A, 16% in ARM B). Confirmed objective partial responses by RECIST criteria were seen with both schedules; 15/51 (29%) on ARM A and 17/56 (31%) on ARM B. Liposomal pegylated doxorubicin showed significant activity in advanced breast cancer with a generally favourable side-effect profile. The high frequency of stomatitis seen with 6 weekly treatment makes this the less preferred of the two schedules tested.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.