Lectures on the Theory and Application of Modern Finance with R and Chatgpt.pdf
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200多页的最新资料，金融科技量化金融研究生课程讲义，全部矢量文字，适合翻译使用！
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These lecture notes are thought for Master courses in Finance,
Fintech and Quantitative Finance programs. We fully subscribe to
the philosophy that Master students should be offered courses that
are really at the cutting edge of the technologies and advances that
are disrupting the financial industry and delve deep into topics such
as AI, machine learning, and their importance for Asset Management. in these notes the illustration of the theory of Finance is
paired with practical applications to real-life asset allocation problems. A hands-on approach is proposed to construct and manipulate
databases to build portfolios, assess their performance and manage
their risk. The course begins with a section on the fundamentals on
individual choice, to then move from individual choice to market valuation, covering the traditional Markowitz mean-variance approach,
market-based asset pricing and Arbitrage-based pricing theory.
Empirical modeling in finance is then introduced by illustrating
its working and its historical evolution. The translation of financial
theory into action on data is driven by building predictive models
for asset prices and returns. Basic models are explored, and programming emerges as an essential prerequisite for data manipulation.
Readers can acquaint themselves with the statistical software R and
exhibit the application of theoretical concepts to financial data, illustrated by sample programs, exercises, and corresponding solutions.
The main focus is on the hands-on implementation of this
approach using actual data, utilizing specific models to exemplify
its practicality. The text also shows how Chat GPT could be usedto help in doing Empirical Finance with R. The interaction with
chat GPT is illustrated via live session entitled “Ask Chat GPT”,
which provide guidance to efficient interaction with Chat GPT to
write codes designed for specific tasks and adapt and debug them, if
necessary.
The Lectures are meant to provide a background for flipped classrooms and interactive teaching with the demonstration and discussion of R codes, in which students are expected to engage in real-time
coding on their laptops and group discussions.

1 Individual Choice 1
1.1 Different Paradigms for Decision Theory 2
1.1.1 Decision theory under certainty . 2
1.1.2 Second: Decision theory under uncertainty . 3
1.1.3 Third: Decision theory under uncertainty,
asymmetric information and strategic
interaction . 5
1.2 A Paradigm for Rationality . 6
1.3 Decision Theory Under Uncertainty .......... 8
1.4 Risk Aversion . 12
1.4.1 Quantifying risk aversion . 16
1.4.2 Certainty equivalent and risk premium . 18
1.5 Insurance Pricing Principle . 19
1.6 The Portfolio Problem 21
1.6.1 Two securities and two states of the world . 21
1.6.2 The case with N assets 23
1.7 Subjective Valuation . 25
1.7.1 A numerical example . 29
1.8 Expected Utility Reloaded 33
1.8.1 Step 1: Choice over binary actions 35
1.8.2 Step 2: Deriving the expected utility
representation in a generic state space
with S states . 37
1.9 Deviations from Expected Utility 40
1.9.1 Independence axiom and the Allais
paradox 40
1.9.2 The paradox of Ellsberg and the distinction
between ambiguity and risk . 42
1.9.3 Preference for temporal resolution of
uncertainty 44
1.9.4 Anomalies observed by Khanemann and
Tversky 46
1.9.5 Individual choice and collective decisions 49
2 From Individual Choice to Market Pricing 51
2.1 The Markowitz Mean–Variance Approach . 52
2.1.1 A bit of terminology . 54
2.1.2 Minimum variance portfolio . 55
2.1.3 Risk parity portfolios . 55
2.1.4 The M–V model with two risky assets
and no risk-free 56
2.1.5 Optimal allocation when a risk-free security
is available . 61
2.1.6 The general solution to the N risky assets
and no risk free 63
2.1.7 The general solution in the presence
of a risk-free security . 69
2.1.8 The FOC for a mean–variance investor when
a risk-free asset is present 72
2.2 Market-Based Asset Pricing . 73
2.2.1 The capital asset pricing model . 73
2.2.2 The market portfolio . 74
2.2.3 The capital market line 75
2.2.4 The security market line . 75
2.2.5 SML and CML 76
2.2.6 Market price as the present value of
cash flows . 78
2.2.7 The zero-beta CAPM (Black) 79
2.2.8 The Roll’s critique 81
2.3 A Structural Interpretation . 82
2.4 Arbitrage-Based Pricing Theory . 85
2.4.1 The linear factor model 85
Contents xi
2.4.2 Arbitrage portfolios 87
2.4.3 The Ross (1973) arbitrage pricing
argument 89
2.4.4 Economic interpretation of the λ
coefficients . 91
2.4.5 APT when the risk factors are portfolio
excess returns . 91
2.4.6 APT and CAPM . 92
2.4.7 APT tests and factor-mimicking portfolios . 93
2.5 APT and Risk-Neutral Valuation 95
2.5.1 CAPM and SDF . 97
3 Empirical Tests of Asset Pricing Models 101
3.1 The View from the 1960s: Efficient Markets and
Constant Expected Returns . 101
3.2 The Cross-Sectional Evidence: CAPM Verification 102
3.2.1 Black, Jensen, and Scholes (1972) and portfolio
aggregation 104
3.2.2 Capital market efficiency and the two-step
Fama–McBeth (1973) procedure . 106
3.3 The Frazzini and Pedersen (2014) BAB
Factor Construction . 109
3.4 Empirical Tests of APT, Anomalies and Fama and
French (1993) . 112
3.5 Direct Construction of Traded Portfolios 114
3.6 Time-Series Analysis of Returns . 116
3.6.1 The behavior of returns at high-frequency . 116
3.6.2 A more realistic description . 117
3.7 Time Series Anomalies 118
3.8 Returns at Different Horizons and the Dynamic
Dividend Growth Model of Shiller (1981) . 118
3.9 Conditional Asset Pricing with Predictable
Returns 120
3.10 Predictive Models in Finance 122
4 From Theory to Practice 125
4.1 The Econometric Modeling Process . 126
4.2 The Challenges for Financial Econometrics 127
4.3 Returns 128
xii Lectures on the Theory and Application of Modern Finance
4.3.1 Simple and log returns 128
4.3.2 Statistical models for asset prices and
returns . 129
4.3.3 Multi-period returns and annualized
returns . 130
4.3.4 Working with returns . 131
4.4 Stock and Bond Returns . 134
4.4.1 Stock returns and the dynamic dividend growth
model . 134
4.4.2 Bond returns: Yields-to-maturity and holding
period returns . 138
4.5 Going to the Data with R 142
4.5.1 Getting started 143
4.5.2 Data objects in R . 144
4.5.3 Data handling in R 146
4.5.4 Data exploration and graphics 148
4.5.5 Interacting with Chat GPT . 157
4.6 Appendix: The Data . 165
5 The Constant Expected Return Model 169
5.1 Model Specification 169
5.1.1 Stocks for the long run 171
5.2 Model Estimation . 173
5.2.1 Parameters estimation in a linear model 174
5.3 Model Simulation . 176
5.4 The CER Model at Work with R 177
5.4.1 Asset allocation with the CER 177
5.4.2 Model simulation: Backtesting and VaR 182
6 Factor Models 189
6.1 Time-Series Representation . 189
6.2 Cross-Sectional Representation . 191
6.3 Factor-Based Portfolios and Factor Exposures . 191
6.4 Asset Allocation with the CER and the
CAPM in R 192
6.5 Validating Factor Models 199
6.5.1 Which factors? 200
6.6 Factor Models with Predictability 200
6.6.1 An illustration with R 205
Contents xiii
7 Models for Risk Measurement 211
7.1 Risk Measurement 211
7.1.1 Value at risk (VaR) 211
7.2 VaR without Predictability . 212
7.2.1 VaR with the CER 212
7.2.2 VaR with the CAPM . 212
7.3 The Evidence from High-Frequency Data . 213
7.4 A General Model for High-Frequency Data 214
7.4.1 GARCH models for heteroscedasticity . 214
7.4.2 GARCH properties 215
7.4.3 GARCH forecasting 215
7.4.4 Testing for GARCH 215
7.5 Estimation of GARCH Models . 216
7.5.1 Quasi MLE estimation 217
7.6 From GARCH to VaR 218
7.6.1 GARCH with factors . 218
7.7 Measuring Risk: An Illustration with R 218
7.8 Backtesting VaR . 223
7.8.1 Unconditional coverage testing 224
7.8.2 Independence testing . 225
7.8.3 Conditional coverage testing . 226
7.8.4 Backtesting VaR in R . 226