The Study Of Financial Volatility Based On High-frequency Data

In finance field,uncertain risk,the volatility of assets returns process, is the origin of chasing benefits in the market,is also the base of active financial market; Assets returns process affected by many factors in market environment is objectively form-ing the uncertain fluctuation.Whatever for chasing benefits or assets value-keeping purpose people always hope being able to forecast the best results to the upcoming fluctuation.Relying on advanced computer and data storing technology it is more and more easy to gain high frequency finance data.People hope make the much better fore-casting to volatility by the high frequency finance data that is depicted more exquisite to market details.So,the study of assets price fluctuation under high frequency finance data is more and more concerned.The key points and main achievements are listed as follows:1.In this paper in order to deal with the influence of "Calendar Effects",the concept of "self-weighted volatility" is advanced.We avoid the effect of "Calendar Effects" to self-weighted volatility, and show the characteristics of volatility more exactitude. And the limit theorems of self-weighted volatility are gave on this paper. It is important for application of the volatility.2.We estimate volatility based on high-frequency data in this paper,using the method of Bayes estimate.Using the used data,we bring forward the prior distribution of parameter, and compare the results from different Loss Function,and then get a reasonable estimate.3.In the former study of high frequency data,the choose of observational times is always equal span.Firstly, the transformation tide of high frequency data isn’t memo-rized completely.Secondly,the influence of "Caledar Effects" to volatility isn’t consid- ered in this way. Aiming at this problem, the choose of observational times by the way of nor-equal span is advanced.The characteristic of data is saved to the maximum. And the ultimate estimated volatility is more dependable and exact.4.There are so many microstructure noise in high frequency dada,and the mi-crostructure noise increased by the increased of sampling frequency.We use two time scales to clear up the microstructure noise from the estimate volatility,and then the ultimate estimated volatility is more dependable and exact.