数学名著系列丛书：计量金融精要 [Mathematics Monograph Series:The Elements of Financial Econometrics] 下载 mobi epub pdf 电子书 2024

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著名的普林斯顿大学教授倾力打造的精品英文版的金融数学方面的精品教材。

内容简介

《数学名著系列丛书：计量金融精要》是一本关于金融计量方面的基础用书，提供了核心基础资料，包括金融研究日益增长的科学前沿和金融工业方面重要的发展情况。《数学名著系列丛书：计量金融精要》对资产定价理论、投资组合优化和风险管理方法提供了简洁的和紧凑的处理。提供了单因素和多因素情况下的时间序列模型技术，在分析财务数据上下文的时候介绍了他们的均值和方差。真实的数据分析贯穿全书，是《数学名著系列丛书：计量金融精要》的一个明显的特征。

作者简介

范剑青，美国普林斯顿大学统计与金融工程终身教授，The Annals of Statistics 杂志主编。1982年毕业于复旦大学数学系，随后考入中国科学院应用数学所攻读硕士。1986年进入美国加州柏克萊大学攻读博士学位，师从国际著名的统计学家 Bickel 教授和Donoho教授，在过去的十多年里，范教授发表了一百多篇论文，已经出版两本英文专著。于2004年任 The Annals of Statistics 的主编，成为该杂志创刊70多年来**的亚裔主编。他还当选为美国统计学会院士（Fellow）、国际数理研究院院士和国际统计研究院院士。2005年出任中国科学院数学与系统科学研究院统计科学研究中心主任，2006年获得国家杰出海外青年基金。

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目录

Preface to Mathematics Monograph Series
Preface
Chapter　1　Asset Returns
1.1　Returns
1.1.1　One-period simple returns and gross returns
1.1.2　Multiperiod returns
1.1.3　Log returns and continuously compounding
1.1.4　Adjustment for dividends
1.1.5　Bond yields and prices
1.1.6　Excess returns
1.2　Behavior of?nancial return data
1.2.1　Stylized features of?nancial returns
1.3　E±cient markets hypothesis and statistical models for returns
1.4　Tests related to e±cient markets hypothesis
1.4.1　Tests for white noise
1.4.2　Remarks on the Ljung-Box test
1.4.3　Tests for random walks
1.4.4　Ljung-Box test and Dickey-Fuller test
1.5　Appendix: Q-Q plot and Jarque-Bera test
1.5.1　Q-Q plot
1.5.2　Jarque-Bera test
1.6　Further reading and software implementation
1.7　Exercises

Chapter　2　Linear Time Series Models
2.1　Stationarity
2.2　Stationary ARMA models
2.2.1　Moving average processes
2.2.2　Autoregressive processes
2.2.3　Autoregressive and moving average processes
2.3　Nonstationary and long memory ARMA processes
2.3.1　Random walks
2.3.2　ARIMA model and exponential smoothing
2.3.3　FARIMA model and long memory processes
2.3.4　Summary of time series models
2.4　Model selection using ACF， PACF and EACF
2.5　Fitting ARMA models: MLE and LSE
2.5.1　Least squares estimation
2.5.2　Gaussian maximum likelihood estimation
2.5.3　Illustration with gold prices
2.5.4　A snapshot of maximum likelihood methods
2.6　Model diagnostics: residual analysis
2.6.1　Residual plots
2.6.2　Goodness-of-?t tests for residuals
2.7　Model identi?cation based on information criteria
2.8　Stochastic and deterministic trends
2.8.1　Trend removal
2.8.2　Augmented Dickey-Fuller test
2.8.3　An illustration
2.8.4　Seasonality
2.9　Forecasting
2.9.1　Forecasting ARMA processes
2.9.2　Forecasting trends and momentum of?nancial markets
2.10　Appendix: Time series analysis in R
2.10.1　Start up with R
2.10.2　R-functions for time series analysis
2.10.3　TSA{ an add-on package
2.11　Exercises

Chapter　3　Heteroscedastic Volatility Models
3.1　ARCH and GARCH models
3.1.1　ARCH models
3.1.2　GARCH models
3.1.3　Stationarity of GARCH models
3.1.4　Fourth moments
3.1.5　Forecasting volatility
3.2　Estimation for GARCH models
3.2.1　Conditional maximum likelihood estimation
3.2.2　Model diagnostics
……
Chapter　4　Multivariate Time Series Analysis
Chapter　5　Effcient Portfolios and Capital Asset Pricing Model
Chapter　6　Factor Pricing Models
Chapter　7　Portfolio Allocation and Risk Assessment
Chapter　8　Consumption based CAPM
Chapter　9　Present-value Models
References
Author Index
Subject Index

精彩书摘

　　《数学名著系列丛书：计量金融精要》：
　　Chapter 1
　　Asset Returns The primary goal of investing in a -nancial market is to make pro-ts without taking excessive risks. Most common investments involve purchasing -nancial assets such as stocks， bonds or bank deposits， and holding them for certain periods. Posi- tive revenue is generated if the price of a holding asset at the end of holding period is higher than that at the time of purchase （for the time being we ignore transaction charges）. Obviously the size of the revenue depends on three factors： （i） the initial capital （i.e. the number of assets purchased）， （ii） the length of holding period， and （iii） the changes of the asset price over the holding period. A successful investment pursues the maximum revenue with a given initial capital， which may be measured explicitly in terms of the so-called return . A return is a percentage de-ned as the change of price expressed as a fraction of the initial price. It turns out that asset returns exhibit more attractive statistical properties than asset prices themselves.
　　Therefore it also makes more statistical sense to analyze return data rather than price series.
　　1.1 Returns
　　Let Pt denote the price of an asset at time t. First we introduce various de-nitions for the returns for the asset.
　　1.1.1 One-period simple returns and gross returns
　　Holding an asset from time t ? 1 to t， the value of the asset changes from Pt?1 to Pt. Assuming that no dividends paid are over the period. Then the one-period simple return is de-ned as
　　It is the pro-t rate of holding the asset from time t ? 1 to t. Often we write Rt = 100Rt%， as 100Rt is the percentage of the gain with respect to the initial capital Pt?1. This is particularly useful when the time unit is small （such as a day or an hour）； in such cases Rt typically takes very small values. The returns for lessrisky assets such as bonds can be even smaller in a short period and are often quoted in basis points ， which is 10； 000Rt. The one period gross return is de-ned as Pt=Pt?1 = Rt 1. It is the ratio of the new market value at the end of the holding period over the initial market value. 1.1.2 Multiperiod returns
　　The holding period for an investment may be more than one time unit. For any integer k > 1， the returns for over k periods may be de-ned in a similar manner.
　　For example， the k-period simple return from time t ? k to t is and the k-period gross return is Pt=Pt?k = Rt（k） 1. It is easy to see that the multiperiod returns may be expressed in terms of one-period returns as follows：
　　If all one-period returns Rt；　　 ；Rt?k 1 are small， （1.3） implies an approximation
　　This is a useful approximation when the time unit is small （such as a day， an hour or a minute）.
　　1.1.3 Log returns and continuously compounding
　　In addition to the simple return Rt， the commonly used one period log return is
　　de-ned as
　　Note that a log return is the logarithm （with the natural base） of a gross return and log Pt is called the log price. One immediate convenience in using log returns is that the additivity in multiperiod log returns， i.e. the k period log return rt（k） ′
　　log（Pt=Pt?k） is the sum of the k one-period log returns：
　　An investment at time t ? k with initial capital A yields at time t the capitalwhere 1r = （rt rt?1 ￠ ￠ ￠ rt?k 1）=k is the average one-period log returns. In this book returns refer to log returns unless speci-ed otherwise.
　　Note that the identity （1.6） is in contrast with the approximation （1.4） which is only valid when the time unit is small. Indeed when the values are small， the two returns are approximately the same：
　　However， rt < Rt. Figure 1.1 plots the log returns against the simple returns for the Apple Inc share prices in the period of January 1985 { February 2011. The returns are calculated based on the daily close prices for the three holding periods： a day， a week and a month. The -gure shows that the two de-nitions result almost the same daily returns， especially for those with the values between ?0.2 and 0.2. However when the holding period increases to a week or a month， the discrepancy between the two de-nitions is more apparent with a simple return always greater than the corresponding log return.
　　……

前言/序言

数学名著系列丛书：计量金融精要 [Mathematics Monograph Series:The Elements of Financial Econometrics] 下载 mobi epub pdf txt 电子书 格式

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