A fresh engineering approach for the forecast of financial index volatility and hedging strategies

This thesis attempts a new light on a problem of importance in Financial Engineering. Volatility is a commonly accepted measure of risk in the investment field. The daily volatility is the determining factor in evaluating option prices and in conducting different hedging strategies. The volatility estimation and forecast are still far from successfully complete for industry acceptance, judged by their generally lower than 50% forecasting accuracy. By judiciously coordinating the current engineering theory and analytical techniques such as wavelet transform, evolutionary algorithms in a Time Series Data Mining framework, and the Markov chain based discrete stochastic optimization methods, this work formulates a systematic strategy to characterize and forecast crucial as well as critical financial time series. Typical forecast features have been extracted from different index volatility data sets which exhibit abrupt drops, jumps and other embedded nonlinear characteristics so that accuracy of forecasting can be markedly improved in comparison with those of the currently prevalent methods adopted in the industry. The key aspect of the presented approach is "transformation and sequential deployment": i) transform the data from being non-observable to observable i.e., from variance into integrated volatility; ii) conduct the wavelet transform to determine the optimal forecasting horizon; iii) transform the wavelet coefficients into 4-lag recursive data sets or viewed differently as a Markov chain; iv) apply certain genetic algorithms to extract a group of rules that characterize different patterns embedded or hidden in the data and attempt to forecast the directions/ranges of the one-step ahead events; and v)apply genetic programming to forecast the values of the one-step ahead events. By following such a step by step approach, complicated problems of time series forecasting become less complex and readily resolvable for industry application. To implement such an approach, the one year, two year and five year S&PlOO historical data are used as training sets to derive a group of 100 rules that best describe their respective signal characteristics. These rules are then used to forecast the subsequent out-of-sample time series data. This set of tests produces an average of over 75% of correct forecasting rate that surpasses any other publicly available forecast results on any type of financial indices. Genetic programming was then applied on the out of sample data set to forecast the actual value of the one step-ahead event. The forecasting accuracy reaches an average of 70%, which is a marked improvement over other current forecasts. To validate the proposed approach, indices of S&P500 as well as S&P 100 data are tested with the discrete stochastic optimization method, which is based on Markov chain theory and involves genetic algorithms. Results are further validated by the bootstrapping operation. All these trials showed a good reliability of the proposed methodology in this research work. Finally, the thus established methodology has been shown to have broad applications in option pricing, hedging, risk management, VaR determination, etc

Acceleration of on-axis and ring-shaped electron beams in wakefields driven by Laguerre-Gaussian pulses

The acceleration of electron beams with multiple transverse structures in wakefields driven by Laguerre-Gaussian pulses has been studied through three-dimensional (3D) particle-in-cell simulations. Under different laser-plasma conditions, the wakefield shows different transverse structures. In general cases, the wakefield shows a donut-like structure and it accelerates the ring-shaped hollow electron beam. When a lower plasma density or a smaller laser spot size is used, besides the donut-like wakefield, a central bell-like wakefield can also be excited. The wake sets in the center of the donut-like wake. In this case, both a central on-axis electron beam and a ring-shaped electron beam are simultaneously accelerated. Further, reducing the plasma density or laser spot size leads to an on-axis electron beam acceleration only. The research is beneficial for some potential applications requiring special pulse beam structures, such as positron acceleration and collimation

Acceleration and evolution of a hollow electron beam in wakefields driven by a Laguerre-Gaussian laser pulse

We show that a ring-shaped hollow electron beam can be injected and accelerated by using a Laguerre-Gaussian laser pulse and ionization-induced injection in a laser wakefield accelerator. The acceleration and evolution of such a hollow, relativistic electron beam are investigated through three-dimensional particle-in-cell simulations. We find that both the ring size and the beam thickness oscillate during the acceleration. The beam azimuthal shape is angularly dependent and evolves during the acceleration. The beam ellipticity changes resulting from the electron angular momenta obtained from the drive laser pulse and the focusing forces from the wakefield. The dependence of beam ring radius on the laser-plasma parameters (e.g., laser intensity, focal size, and plasma density) is studied. Such a hollow electron beam may have potential applications for accelerating and collimating positively charged particles

A simulation study on the measurement of D0-D0bar mixing parameter y at BES-III

We established a method on measuring the \dzdzb mixing parameter $y$ for BESIII experiment at the BEPCII $e^+e^-$ collider. In this method, the doubly tagged $\psi(3770) \to D^0 \overline{D^0}$ events, with one $D$ decays to CP-eigenstates and the other $D$ decays semileptonically, are used to reconstruct the signals. Since this analysis requires good $e/\pi$ separation, a likelihood approach, which combines the $dE/dx$, time of flight and the electromagnetic shower detectors information, is used for particle identification. We estimate the sensitivity of the measurement of $y$ to be 0.007 based on a $20fb^{-1}$ fully simulated MC sample.Comment: 6 pages, 7 figure

Concomitant Active Tuberculosis Prolongs Survival in Non-Small Cell Lung Cancer: A Study in a Tuberculosis-Endemic Country

BACKGROUND: Adjuvant tumor cell vaccine with chemotherapy against non-small cell lung cancer (NSCLC) shows limited clinical response. Whether it provokes effective cellular immunity in tumor microenvironment is questionable. Concomitant active tuberculosis in NSCLC (TBLC) resembles locoregional immunotherapy of tumor cell vaccine; thus, maximally enriches effective anti-tumor immunity. This study compares the survival and immunological cell profile in TBLC over NSCLC alone. METHODS: Retrospective review of NSCLC patients within 1-year-period of 2007 and follow-up till 2010. RESULTS: A total 276 NSCLC patients were included. The median survival of TBLC is longer than those of NSCLC alone (11.6 vs. 8.8 month, p<0.01). Active tuberculosis is an independent predictor of better survival with HR of 0.68 (95% CI, 0.48 ~ 0.97). Squamous cell carcinoma (SCC) (55.8 vs. 31.7%, p<0.01) is a significant risk factor for NSCLC with active TB. The median survival of SCC with active tuberculosis is significantly longer than adenocarcinoma or undetermined NSCLC with TB (14.2 vs. 6.6 and 2.8 months, p<0.05). Active tuberculosis in SCC increases the expression of CD3 (46.4 ± 24.8 vs. 24.0 ± 16.0, p<0.05), CXCR3 (35.1 ± 16.4 vs. 19.2 ± 13.3, p<0.01) and IP-10 (63.5 ± 21.9 vs. 35.5 ± 21.0, p<0.01), while expression of FOXP3 is decreased (3.5 ± 0.5 vs. 13.3 ± 3.7 p<0.05, p<0.05). Survival of SCC with high expression of CD3 (12.1 vs. 3.6 month, p<0.05) and CXCR3 (12.1 vs. 4.4 month, p<0.05) is longer than that with low expression. CONCLUSIONS: Active tuberculosis in NSCLC shows better survival outcome. The effective T lymphocyte infiltration in tumor possibly underlies the mechanism. Locoregional immunotherapy of tumor cell vaccine may deserve further researches

A Novel Classification of Lung Cancer into Molecular Subtypes

The remarkably heterogeneous nature of lung cancer has become more apparent over the last decade. In general, advanced lung cancer is an aggressive malignancy with a poor prognosis. The discovery of multiple molecular mechanisms underlying the development, progression, and prognosis of lung cancer, however, has created new opportunities for targeted therapy and improved outcome. In this paper, we define “molecular subtypes” of lung cancer based on specific actionable genetic aberrations. Each subtype is associated with molecular tests that define the subtype and drugs that may potentially treat it. We hope this paper will be a useful guide to clinicians and researchers alike by assisting in therapy decision making and acting as a platform for further study. In this new era of cancer treatment, the ‘one-size-fits-all’ paradigm is being forcibly pushed aside—allowing for more effective, personalized oncologic care to emerge

Targeting tumor-associated macrophages by anti-tumor Chinese materia medica

Tumor-associated macrophages (TAMs) play a key role in all stages of tumorigenesis and tumor progression. TAMs secrete different kinds of cytokines, chemokines, and enzymes to affect the progression, metastasis, and resistance to therapy depending on their state of reprogramming. Therapeutic benefit in targeting TAMs suggests that macrophages are attractive targets for cancer treatment. Chinese materia medica (CMM) is an important approach for treating cancer in China and in the Asian region. According to the theory of Chinese medicine (CM) and its practice, some prescriptions of CM regulate the body's internal environment possibly including the remodeling the tumor microenvironment (TME). Here we briefly summarize the pivotal effects of TAMs in shaping the TME and promoting tumorigenesis, invasion, metastasis and immunosuppression. Furthermore, we illustrate the effects and mechanisms of CMM targeting TAMs in antitumor therapy. Finally, we reveal the CMM's dual-regulatory and multi-targeting functions on regulating TAMs, and hopefully, provide the theoretical basis for CMM clinical practice related to cancer therapy