Financial Market Risk Measurement Using UHF Data under Multiple Dimensions Information——Based on UHF ACD Model and UHF-GARCH Model

超高频数据是交易的实时记录,是所有信息在股市上的最精确的表现。考虑使用高频数据来测度金融风险无疑能够提高风险测度的准确性。本文在现有研究成果的基础上,将交易者行为特征、交易量、买卖价差、交易速度等信息维度纳入金融高频风险的计量中,并与没有考虑这些信息维度时测度的准确性进行对比,结果表明,考虑多重信息维度的模型能够更准确地测度金融市场风险。UHF data are real-time trading records and the most accurate performance of all information in the stock market.Using high-frequency data to measure the financial risks can undoubtedly improve the accuracy of risk measurement.This paper,based on the results of existing research,brings the behavioral characteristics of traders,trading volume,bid-ask spread,transaction speed and other information dimension into the risk measurement model,and compares the accuracy of measurement with the model without considering such information dimensions.The results confirm that the model considered multiple dimensions of information can be more accurate for measuring financial market risk

Measuring Financial Risks Using Different Frequency Data——Based on Ultra-high Frequency,High Frequency and Low Frequency Data Model

首先计算了已实现波动率和超高频波动率,然后使用ArfIMA(0,d,0)-SkST模型计算了条件波动,最后对条件波动调整后的收益率进行了拟合并计算出了VAr值。实证结果发现,使用高频数据甚至超高频数据测量金融风险的准确性并不比低频数据高很多,如果选用模型恰当,完全能够使用低频数据得到高频数据的精度。Firstly,this paper calculate the realized volatility and the ultra-high frequency volatility,and then use the ARFIMA(0,d,0)-SKST model to calculate the conditional volatility,finally the author calculates and compare the VAR which was calculated by asset return adjusted by conditional volatility.The empirical results show that the use of high-frequency data and even ultra-high frequency data did not improve the accuracy of measurement of financial risk significantly,if selected sensibly,using low frequency data can also get the precision of high-frequency data,the article finally analyzes the applicability of the high-frequency data

(超)高频数据视角下金融风险度量研究进展

基于(超)高频数据的金融市场风险度量方法是一个崭新的研究领域。(超)高频数据因包含了更多的信息,能够提供更丰富的数据资源而备受关注。本文梳理了基于(超)高频数据的五种风险度量方法:分别基于“已实现“波动率模型、ACd族模型、高频极值理论、非参数核密度估计方法、分位数回归理论。本文对这五种方法的研究现状及研究中存在的问题进行了探讨,可为提高我国金融业风险管理水平提供必要的理论指导和实践方法

The Stability of Commercial Bank Deposits and Long-term Liquidity Management

稳定(核心)存款是存款总额中长期稳定的部分,具有成本低、利率敏感性低且稳定性高的特点。稳定存款的测算对于商业银行的流动性管理具有重要的意义。本文使用HP滤波法估算出商业银行的稳定存款,通过借鉴国际先进银行普遍采用的流动性缺口模型法,计算了各期的流动性缺口并将其应用到样本银行的长期流动性管理工作。结果表明该方法操作简便易于计算,测量结果与银行实际经营情况相符,对现阶段我国商业银行流动性管理实践具有较强的指导意义。Stable deposits is the long-term stability part of total deposits,it has the characteristics of low cost,low interest rate sensitivity and high stability.Estimating stable deposits has the important significance of liquidity management for the commercial banks.In this paper,we used the HP filter method to estimate banks' stable deposits,and we used the liquidity gap method which was generally adopted by many international advanced banks,X bank as an example,to estimate the liquidity gap in each stage.In the end,we did an empirical research on the application of stable deposits on long-term liquidity management.The results show that the method is simple and the result is consistent with banks' actual operation situation,which has strong practical significance to the management of liquidity at the present stage of China's commercial banks

中小银行资产负债动态最优化模型构建与实证研究

资产负债优化模型是商业银行风险管理和战略规划的重要工具。本文立足于中小银行实际情况,综合考虑其所处的经营环境约束,兼顾银行所面临的利率风险、流动性风险和信用集中度风险,从短期和中长期决策两个角度,构建了中小银行资产负债动态优化模型。实证结果表明,该模型贴近中小银行实际,能够为银行短期和中长期的资产负债结构优化提供方向,能够实现风险与收益更好的平衡,模型决策效果符合客观事实

上证180指数已实现波动率测度与特性分析——基于股改前后数据的对比

本文首先使用高频数据估计了上证180指数已实现波动率,然后对股权分置改革前后股指已实现波动率的特性进行分析和验证。实证结果表明,股改开始后,短期风险稍微减弱,中期风险显著增大,长期风险有所提高;上证180指数已实现波动率具有明显的持续性和长记忆性;在股改进行阶段和股市暴涨阶段,股指对数已实现波动率具有显著的不对称性,而在其他阶段不存在不对称性

我国房地产统计方法制度的改革方向

文章从考察我国房地产统计方法制度的现状入手,借鉴国外房地产统计方法制度的成熟经验,分调查体系、组织管理体系、指标体系、标准体系等四个部分,对我国当前房地产统计方法制度存在的诸多问题进行了深入分析,在此基础上提出了我国房地产统计方法制度的改革建议以及具体操作方法,并对房地产统计方法制度的定位、总体发展目标和房地产统计方法制度体系的构建模式进行了探讨

Research on Assets and Liabilities Structure Optimization and Structure Risk Management of Small and Medium-sized Banks

近年来，商业银行面临诸多挑战，主要包括：一是市场化进程的深刻影响，行业竞争加剧；二是经营权的垄断逐渐融化，市场约束增强；三是社会融资结构发生深刻变化，金融脱媒现象明显；四是经济波动风险加大，银行亲周期业务面临风险考验；五是技术脱媒加速，互联网导致银行支付及融资功能边缘化，将重构商业银行盈利模式及基本经营资源。这“五大挑战”正在动摇银行传统经营模式，使得传统的基本经营资源和信用结构正在发生潜移默化地演变。 同时，《资本管理办法》地实施将迫使银行在盈利性、安全性和流动性之间作出新的平衡，并引导银行业重新审视目前的资产负债结构，将深刻地影响商业银行的经营管理模式。利率市场化地推进将彻底改变商业银行...In recent years, commercial banks face many challenges: the first challenge is the profound influence of marketization and industry competition. The second challenge is the monopoly melts and market restraint strengthen. The third challenge is social financing structure has undergone profound changes and financial disintermediation obviously. The fourth challenge is the economic fluctuation risk a...学位：博士后院系专业：经济学院_金融学(含保险学)学号：201217000

Measurement of Financial Market Risk Based on High Frequency Data

随着全球经济的迅速发展，金融市场呈现出前所未有的波动，商业企业和金融机构都面临着日趋严重的金融市场风险，加强对金融市场风险的管理已经成为了金融机构和工商企业生存和发展的关键因素，而金融市场风险管理的核心是对风险的定量评估，即风险测度。因此，深入细致地探讨市场风险测度的方法，无疑具有一定的理论价值和实际意义。 目前我国的金融市场风险测度研究一般都采用低频日数据，这必然会损失部分日内信息，影响测度的准确性。一般而言，金融市场上的信息对金融资产价格变化的影响是个连续过程，离散模型必然会造成信息的丢失，数据频率越低，则信息丢失就越多。因此，考虑基于分钟、小时甚至秒、分笔等（超）高频数据计算金融市场风险，无疑为深化对金融市场微观结构的认识，提高金融风险测度的准确性提供了一个新的思路和方法。本文正是在这一思想的引导下，开展了基于高频数据和超高频数据的金融市场风险测度方法的研究。 文中以我国股票市场为研究对象，选取了上证指数、上证180指数、招商银行和贵州茅台四支股票进行实证分析，数据频率分别为分笔、5分钟、10分钟、15分钟、20分钟、30分钟和60分钟，时间跨度为2004-2010年。 本文旨在构建金融市场风险的高频数据计量体系，为使用高频或超高频数据测度金融市场风险的研究抛砖引玉，因此，本文在第一章对金融市场风险高频计量的相关文献进行了归纳总结，并在第二章对金融市场风险测度相关的金融市场理论基础及交易环境的变化情况进行梳理，指明了金融市场风险高频计量的研究方向，为该领域的研究向纵深方向发展打下了坚实基础；本文第三章对金融市场风险的一些基本概念进行了梳理，对金融市场风险测度的方法进行了综述，指出本文主要使用VaR方法来计算金融市场风险；使用高频数据测度金融风险的一个重要前提就是对金融高频数据的特征有很好的把握，因此，本文在第四章对我国股票市场上的高频数据特征和超高频数据特征进行了深入的研究；超高频数据的采集是随机间隔的，而高频数据是等间距采集的，数据特征的迥异决定了对两种类型的数据建立的模型不同，本文第五章针对我国股市上的高频数据特征，选用了已实现波动率模型对金融市场风险进行测度；第六章针对超高频数据的特征，选用超高频波动率模型对金融市场风险进行测度，选用蒙特卡洛模拟法对日内风险价值进行了测度，实证检验也验证了这些模型在我国金融市场上的适用性。本文研究的创新点可概括如下： 1．本文系统地梳理了基于高频数据的金融市场风险测度方法的相关研究，构建了金融市场风险测度的高频建模体系，对金融市场风险高频测度的进一步研究起到了抛砖引玉的作用。 2．比较深入地考察了高频数据和超高频数据的特征，数据特征的考察对于金融市场风险的测度建模至关重要，但以往研究经常忽略。 3．本文在测度方法的使用中，比较注重结合中国金融市场实际，对经典模型进行适当地改进及创新，建立了适合我国金融市场风险测度实际的高频数据模型体系。 4.本文依据统一的参照方法，对不同分布下的低频模型、高频模型和超高频模型测度方法的优劣性及适用性进行了评判，深入地研究了使用高频数据计算金融市场风险的精确性问题 。本文还对不同频率数据在测度金融市场风险时各自的适用性进行了研究和界定，这些在目前的研究中还不曾见到。Along with rapid development of global economic, financial market is showing unprecedented volatility.Commercial companies and financial institutions are facing growing financial market risk, financial market risk management has become a core competence of financial institutions and even all enterprise's survival and development, and its core is quantitative assessment of risk, namely risk measurement. Therefore, the in-depth and detailed discussion of financial market risk measurement method has certain theoretical value and practical significance. At present, Financial market risk estimation research generally use the low-frequency data, which will inevitably lose some intraday information,and affect the accuracy of risk measurement. Generally speaking, financial market information is a continuous process of change on financial asset prices, the discrete model is bound to lose information, the lower frequency, the more missing information. Therefore, considering calculating financial market risk based on data whose frequency is minutes, hours or even seconds, tick-by-tick,have no doubt providing a new ideas and methods for deepening the understanding of the financial market structure, improving accuracy of the financial risk measurement .This article is in this thought, carried out the research of financial market risk measure methods based on high frequency data and uhf data. With Chinese stock market to be research object,we select the Shanghai Composite Index, SSE 180 Index, China Merchants Bank and Guizhou Maotai for empirical analysis. Data frequency contains tick-by-tick , 5m, 10m, 15m, 20m, 30m and 60m. The year spans from 2004 to 2010. This paper aims at building high-frequency financial market risk measurement system for better research of financial market risk measurement based on (ultra) high frequency data.Therefore, in the first chapter,we summarized related literature of financial market risk measurement based on high frequency data, and in the second chapter we summarized the financial market risk measurement theory and trading environment changes, pointed out the direction of the research of financial market risk measurement based on high-frequency data.In the third chapter of this paper ,we defined some basic concepts of financial risk, summarized the measure methods of market risk, and pointed out that this article mainly using VaR methods to calculate the financial market risk.An important prerequisite of financial risk measure using high frequency data is having a good grasp of the characteristics of high frequency data. Therefore, in the fourth chapter,we did a in-depth study of the characteristics of ultra high frequency data of the shanghai stock market.The ultra-high frequency Data acquisition is random intervals, and high-frequency data is collected equally spaced, the different data characteristics determines that data model are different. In the fifth chapter,according to high frequency data characteristics, we constructed realized volatility model on financial markets to measure risk.In the sixth chapter, according to ultra high frequency data characteristics, we constructed the UHFV models to measure the financial market risk, and constructed Monte Carlo simulation methods to carried out Intra-day value at risk. The empirical research verified the suitability of these methods. The innovation of this study can be summarized as follows: 1. This is the first systematic study of the frame of financial market risk measure methods based on high frequency data. We constructed the high-frequency modeling system of risk measurement, and played the role of catalyst in the research of financial market risk measurement based on high frequency data. 2. We did study on the the characteristics of high frequency data and ultra-high frequency data in depth. The study of the characteristics of the data is essential for modeling financial market risk, but previous studies often ignored. 3.This paper paid more attention to the improvement and innovation of classical model according to China's financial markets situation, and established a high frequency data model system which is suitable to China's financial market . 4. In accordance with the unified referencing methods, we assessed the inferiority and applicability of low-frequency model, high-frequency model and uhf model under different distribution, and did an in-depth researched on the calculating accuracy of the financial market risks based on high frequency data. The article also researched and defined the suitability of different frequency data in the measurement of financial markets risk, which has not yet seen in the current study .学位：经济学博士院系专业：经济学院计划统计系_统计学学号：1542008015025

影子银行体系及其对商业银行的影响探析

近年来,影子银行在我国迅速发展起来,在一定程度上推进了商业银行的业务创新,扩大了中间业务收入,弥补了商业银行业务的覆盖不足。但同时影子银行通过各种方式套取了银行的资金,扭曲了金融资源的配置效率,规避了监管,抵消了央行调控效果,放大了商业银行的系统风险。本文对影子银行的定义、影子银行对商业银行的影响等进行了深入的剖析,并从国家、监管部门和银行的角度,分别提出了应对影子银行体系的一些建议