Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

An Introduction to Volatility Models with Indices

AbstractThis paper considers a class of volatility models generated by autoregressive (AR) type models with indices. Some results associated with the autocorrelation function (acf) of this class are given and the spectral density is obtained in terms of the kurtosis of the error distribution and model parameters

Random coefficient volatility models

In financial modeling, the moments of the observed process, the kurtosis and the moments of the conditional volatility play important roles. They are very important in model identification and in forecasting the volatility (see Thavaneswaran et al. [(2005b). Forecasting volatility. Statist. Probab. Lett. 75, 1-10.]). This paper introduces random coefficient GARCH models including the class random coefficient GARCH (RC-GARCH) models and derive their higher order moments and kurtosis.Stochastic volatility Random coefficient Kurtosis Sign-switching

Forecasting volatility

This paper studies the problem of volatility forecasting for some financial time series models. We consider several stochastic volatility models including GARCH, Power GARCH and non-stationary GARCH for illustration. In particular, a martingale representation is used to obtain the l-steps-ahead forecast error variance for the class of GARCH models. Some closed-form expressions for the variance of l-steps-ahead forecasts errors are given in terms of [psi] weights and the kurtosis of the error distribution.Forecasting GARCH models Stochastic volatility Innovations Heteroscedasticity Random Conditional expectation

Recent developments in volatility modeling and applications

In financial modeling, it has been constantly pointed out that volatility clustering and conditional nonnormality induced leptokurtosis observed in high frequency data. Financial time series data are not adequately modeled by normal distribution, and empirical evidence on the non-normality assumption is well documented in the financial literature (details are illustrated by Engle (1982) and Bollerslev (1986)). An ARMA representation has been used by Thavaneswaran et al., in 2005, to derive the kurtosis of the various class of GARCH models such as power GARCH, non-Gaussian GARCH, nonstationary and random coefficient GARCH. Several empirical studies have shown that mixture distributions are more likely to capture heteroskedasticity observed in high frequency data than normal distribution. In this paper, some results on moment properties are generalized to stationary ARMA process with GARCH errors. Application to volatility forecasts and option pricing are also discussed in some detail

Organophotocatalytic N‐Demethylation of Oxycodone Using Molecular Oxygen

peer reviewedN-Demethylation of oxycodone is one of the key steps in the synthesis of important opioid antagonists like naloxone or analgesics like nalbuphine. The reaction is typically carried out using stoichiometric amounts of toxic and corrosive reagents. Herein, we present a green and scalable organophotocatalytic procedure that accomplishes the N-demethylation step using molecular oxygen as the terminal oxidant and an organic dye (rose bengal) as an effective photocatalyst. Optimization of the reaction conditions under continuous flow conditions using visible-light irradiation led to an efficient, reliable, and scalable process, producing noroxycodone hydrochloride in high isolated yield and purity after a simple workup

A Portable remote medical consultation system for the use of distant rural communities

Remote medical monitoring and consultation has become indispensable in order to enhance the availability of better health-care services to the patients in remote rural areas in the country. This paper proposes an inexpensive, easy to handle Remote Medical Consultation System (RMCS) which supports the healthcare workers to carry out their services through bi-directional video and voice communication between the remote end and doctor’s end as well as automated measuring of medical parameters that can be controlled from both ends. RMCS is consisted of a wearable sensors kit, a centralized hardware platform which connects to the medical sensors and devices and a software platform with database for operating and managing the system. RMCS is capable of remotely measuring patient’s blood pressure, heart rate, body temperature, electrocardiogram (ECG), heart sounds and the system’s platform supports to add-on more medical sensors or devices. The key aspect of the system is that it reduces most of the complexity in operation and facilitates the doctors to monitor and diagnose the patients in real-time. RMCS was essentially developed to eliminate the issues of low quality healthcare services in rural areas and to assist in monitoring immobilized patients