K.H. Ng

Estimation and forecasting with logarithmic autoregressive conditional duration models

Efficient estimation of Log-ACD models using the estimating functions (EF) method.Study the finite sample behavior of new estimators through a simulation study.Compare the results via the EF and maximum likelihood (ML) methods.Apply the EF and ML methods for duration data for ACD models.Compare forecast abilities for ACD models through the EF and ML methods. This paper presents a semi-parametric method of parameter estimation for the class of logarithmic ACD (Log-ACD) models using the theory of estimating functions (EF). A number of theoretical results related to the corresponding EF estimators are derived. A simulation study is conducted to compare the performance of the proposed EF estimates with corresponding ML (maximum likelihood) and QML (quasi maximum likelihood) estimates. It is argued that the EF estimates are relatively easier to evaluate and have sampling properties comparable with those of ML and QML methods. Furthermore, the suggested EF estimates can be obtained without any knowledge of the distribution of errors is known. We apply all these suggested methodology for a real financial duration dataset. Our results show that Log-ACD (1,1) fits the data well giving relatively smaller variation in forecast errors than in Linear ACD (1,1) regardless of the method of estimation. In addition, the Diebold-Mariano (DM) and superior predictive ability (SPA) tests have been applied to confirm the performance of the suggested methodology. It is shown that the new method is slightly better than traditional methods in practice in terms of computation; however, there is no significant difference in forecasting ability for all models and methods.

Calibration Intervals in Linear Regression Models

Many of the existing methods of finding calibration intervals in simple linear regression rely on the inversion of prediction limits. In this article, we propose an alternative procedure which involves two stages. In the first stage, we find a confidence interval for the value of the explanatory variable which corresponds to the given future value of the response. In the second stage, we enlarge the confidence interval found in the first stage to form a confidence interval called, calibration interval, for the value of the explanatory variable which corresponds to the theoretical mean value of the future observation. In finding the confidence interval in the first stage, we have used the method based on hypothesis testing and percentile bootstrap. When the errors are normally distributed, the coverage probability of resulting calibration interval based on hypothesis testing is comparable to that of the classical calibration interval. In the case of non normal errors, the coverage probability of the calibration interval based on hypothesis testing is much closer to the target value than that of the calibration interval based on percentile bootstrap.

Fludarabine, High Dose Cytarabine and Granulocyte Colony-Stimulating Factor (FLAG) as Consolidation Chemotherapy in Older Patients with Acute Myeloid Leukemia: A Retrospective Cohort Study

The study aimed to investigate the effect of consolidation treatment with fludarabine, high-dose cytarabine and granulocyte colony-stimulating factor or FLAG in older AML patients. The study included 41 eligible patients above 54xa0years old, who received both induction and consolidation chemotherapy for AML from 2008 to 2013. The study cohort had a minimum 24xa0months follow-up period. Survival analysis was carried out to assess patients’ overall survival and disease free survival based on types of consolidation regimens. The consolidation treatment with FLAG exerted a protective effect to both overall survival and disease free survival in older patients. Patients who were consolidated with FLAG regimen had a significant longer overall survival (log-rank, pxa0=xa00.0025) and disease free survival (log-rank, pxa0=xa00.0026). The median overall survival was longer (18.70xa0months) with the use of FLAG when compared to non-FLAG group (8.09xa0months). The median disease free survival was also longer (13.84xa0months) with use of FLAG when compared to the non-FLAG group (4.44xa0months). Regression analysis with Cox model yielded hazard ratio of 0.245 (pxa0=xa00.0094) in overall survival and 0.217 (pxa0=xa00.0068) in disease free survival. The use of FLAG as consolidation treatment was associated with approximately 60–80% reduction in hazard rates. The result was adjusted for age, race and gender in regression analysis. Older AML patients had longer remission and survival when consolidated with FLAG regimen after the induction chemotherapy.

Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models

Abstract Risk measures such as value-at-risk (VaR) and expected shortfall (ES) may require the calculation of quantile functions from quantile regression models. In a parametric set-up, we propose to regress directly on the quantiles of a distribution and demonstrate a method through the conditional autoregressive range model which has increasing popularity in recent years. Two flexible distribution families: the generalised beta type two on positive support and the generalised-t on real support (which requires log transformation) are adopted for the range data. Then the models are extended to allow the volatility dynamic and compared in terms of goodness-of-fit. The models are implemented using the module fmincon in Matlab under the classical likelihood approach and applied to analyse the intra-day high-low price ranges from the All Ordinaries index for the Australian stock market. Quantiles and upper-tail conditional expectations evaluated via VaR and ES respectively are forecast using the proposed models.