Lecture 2:Statistical inference and testing
Xin T Tong
Sunday 21st August, 2016
Xin Tong
Statistics
1 / 46
Last time
Your grader: KOU Han, email: [email protected]
There will be webcasts.
Asset return: time series with investment value.
Risk and uncertaint
Lecture 2: statistical inference and hypothesis testing
Xin Tong
1
Statistical Inference
As we mentioned in previous lecture, this class focuses on how to construct a quantitative
stochastic model for financial time series. Just like any scientific discip
Lecture 3: linear model and regression
Xin Tong
One of the most important model we will study is the AR model. In the simplest AR(1)
model, the stock return of tomorrow is given a fraction of today and a shock term:
rt = + rt1 + at .
The general AR(k) mod
Lecture 1: Asset Returns and Uncertainty
Xin Tong
1
Preliminaries
This lecture note and its sequels are intended for the class QF5210 at NUS, titled financial
time series: theory and computation. These notes are largely still under construction. Any
comme
QF5210: FINANCIAL TIME SERIES: THEORY AND COMPUTATION
HW 1, due on 30th August 2016
Notes:
All tests are based on the 5% significance level ( = 5%).
The package fBasics of R is helpful in doing these exercises. All the useful packages are installed
on th
QF5210: FINANCIAL TIME SERIES: THEORY AND COMPUTATION
Homework Assignment 3: Solution
6 March 2014
Notes:
Unless specifically assigned, all tests are based on the 5% significance level ( = 5%).
Do not hand in whole computer output. Use copy-and-paste to
QF5210: FINANCIAL TIME SERIES: THEORY AND COMPUTATION
Homework Assignment 3
6 March 2014
Notes:
Unless specifically assigned, all tests are based on the 5% significance level ( = 5%).
Do not hand in whole computer output. Use copy-and-paste to summarise
QF5210: FINANCIAL TIME SERIES: THEORY AND COMPUTATION
Homework Assignment 4: Solution
20 March 2014
Notes:
All tests are based on the 5% significance level ( = 5%).
For daily series, use ten (10) lags in all ACF or ARCH-effect tests. For monthly series,
QF5210: FINANCIAL TIME SERIES: THEORY AND COMPUTATION
Homework Assignment 4
20 March 2014
Notes:
All tests are based on the 5% significance level ( = 5%).
Do not hand in whole computer output. Use copy-and-paste to summarise the output.
To hand in befo
Multivariate Time Series in Finance
Multivariate volatility models
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
11th Lecture,
Multivariate volatility models
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
13th Lecture, 17 April 2014
Chao ZHOU
NUS
Financia
High-frequency nancial data and Market microstructure
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
10th Lecture, 27 March 2014
Risk measures and risk management
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
9th Lecture, 20 March 2014
Chao ZHOU
NUS
Financ
High-frequency nancial data and Market microstructure
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
9th Lecture, 20 March 2014
Conditional Heteroscedastic Models
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
6th Lecture, 20 February 2014
Chao ZHOU
NUS
Fi
Risk measures and risk management
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
8th Lecture, 13 March 2014
Chao ZHOU
NUS
Financ
Conditional Heteroscedastic Models
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
5th Lecture, 13 February 2014
Chao ZHOU
NUS
Fi
Other returns models
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
4th Lecture, 8 February 2014
Chao ZHOU
NUS
Financial Time Se
ARMA Models
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
3nd Lecture, 6 February 2014
Chao ZHOU
NUS
Financial Time Series
1 /
Financial Data and R Program
Returns time series
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
S17 Oce 8-14
Oce hour: Thursday 10:00 AM to 11:30 AM
16 January 2014
C
ARMA Models
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
2nd Lecture, 23 January 2014
Chao ZHOU
NUS
Financial Time Series
1 /
Nonlinear models and their applications
Financial Time Series: Theory and Computation
Chao ZHOU
Centre for Quantitative Finance
Department of Mathematics, NUS
[email protected]
Oce hour: Thursday 4:00 PM to 5:30 PM
7th Lecture, 6 March 2014
Chao ZHOU
NUS
F