【管理学英文资料】量化风险管理PY Quantitative Risk Management Using Python

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Quantitative Risk Management Using Python An Essential Guide for Managing Market.pdf (2.36 MB, 需要: RMB 12 元)

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量化风险管理用Python
Quantitative Risk Management Using Python
In an increasingly complex financial landscape, effective risk management is acritical skill for professionals navigating the dynamic world of finance. This resource intends to provide a comprehensive and practical approach to understanding and applying risk management techniques using Python.
The resource serves as an essential resource for finance professionals, academics, and students looking to deepen their knowledge of quantitative risk  anagement. It bridges theoretical concepts with hands-on Python implementations, equipping readers with the tools needed to assess, mitigate, and manage financial risks effectively. Whether you are involved in investment management, banking, financial analytics, or fintech and beyond, this resource offers valuable insights into the intricate mechanisms that drive market, credit, and model risk.
What You Will Learn
The resource systematically introduces key aspects of financial risk management,
beginning with foundational principles and advancing to sophisticated techniques for managing risk in various financial contexts. Readers will gain expertise in
– Fundamentals of Risk and Return: Understanding different types of financial
risk, the role of diversification in portfolio management, and the trade-off
between risk and return
– Credit Risk Management: Assessing and managing risks associated with
default and counterparty credit exposure
– Market Risk Management: Identifying, measuring, and mitigating risks stemming from market fluctuations
– Risk Management Using Financial Derivatives: Exploring how derivatives
such as options and futures can be leveraged to manage risk
– Static and Dynamic Hedging Strategies: Applying hedging techniques to
minimize exposure and protect investment positions
– Model Risk Management: Evaluating risks in the development and deployment
of machine learning models within the financial sector
1 Introduction to Quantitative Risk Management ......................... 1
1.1 Understanding Different Types of Risk in Financial Markets ........ 5
1.1.1 Market Risk ..................................................... 6
1.1.2 Credit Risk ...................................................... 15
1.1.3 Liquidity Risk ................................................... 17
1.1.4 Operational Risk ................................................ 17
1.1.5 Model Risk ...................................................... 18
1.1.6 Legal and Regulatory Risk ..................................... 19
1.1.7 Systemic Risk ................................................... 19
1.1.8 Environmental, Social, and Governance (ESG) Risk ......... 20
1.1.9 A Summary of Common Risk Types .......................... 21
1.2 Common Financial Instruments ........................................ 22
1.2.1 Low-Risk Assets................................................ 22
1.2.2 Moderate-Risk Assets .......................................... 23
1.2.3 High-Risk Assets ............................................... 25
1.2.4 Derivatives ...................................................... 26
1.2.5 A Summary of Financial Instruments by Risk Level ......... 27
1.3 Summary ................................................................ 28
2 Fundamentals of Risk and Return in Finance ............................ 31
2.1 Understanding Return................................................... 31
2.2 Understanding Risk ..................................................... 33
2.3 Risk-Return Trade-Off .................................................. 37
2.4 Measuring Return ....................................................... 38
2.4.1 Absolute Return................................................. 38
2.4.2 Percentage Return .............................................. 38
2.4.3 Logarithmic Return ............................................. 39
2.4.4 Total Return vs. Price Return .................................. 39
2.4.5 Annualized Returns............................................. 40
2.4.6 Single-Period vs. Multi-Period Returns ....................... 41
2.5 Measuring Risk.......................................................... 42
2.5.1 Annualization of Risk Measures ............................... 43
2.5.2 Difference in Volatility Calculated Using Daily vs.
Monthly Data ................................................... 44
2.6 Measuring Risk-Adjusted Return ...................................... 46
2.6.1 Sharpe Ratio .................................................... 46
2.6.2 Sortino Ratio .................................................... 47
2.6.3 Treynor Ratio ................................................... 47
2.6.4 Evaluating Performance Measures in Portfolio
Optimization .................................................... 48
2.7 Summary ................................................................ 63
3 Managing Credit Risk ....................................................... 65
3.1 Expected and Unexpected Credit Loss................................. 67
3.1.1 Unexpected Loss................................................ 69
3.1.2 Stress Loss ...................................................... 69
3.2 Probability of Default ................................................... 70
3.2.1 Logistic Regression ............................................. 71
3.2.2 Decision Trees and Random Forests........................... 72
3.2.3 Other Machine Learning Classifiers ........................... 73
3.3 Loss Given Default...................................................... 73
3.4 Exposure at Default ..................................................... 76
3.5 Expected Credit Loss ................................................... 78
3.5.1 Capital Regulation Using Risk-Weighted Asset............... 79
3.6 Building a PD Model ................................................... 82
3.6.1 Data Processing and Exploration .............................. 83
3.6.2 Dealing with Outliers........................................... 84
3.6.3 Dealing with Missing Data ..................................... 86
3.6.4 Dealing with Categorical Data ................................. 86
3.6.5 Train-Test Split ................................................. 87
3.6.6 Developing Logistic Regression Model ....................... 88
3.6.7 Model Evaluation ............................................... 88
3.6.8 ROC Curve ...................................................... 90
3.7 Summary ................................................................ 93
4 Managing Market Risk...................................................... 95
4.1 Variance ................................................................. 96
4.1.1 Unbiasedness in Sample Variance ............................. 97
4.1.2 Variance in Practice ............................................. 100
4.1.3 Limitations of Variance As a Risk Measure ................... 101
4.2 Maximum Drawdown (Max Drawdown) .............................. 107
4.2.1 Distinctive Features of Maximum Drawdown................. 108
4.2.2 Calculating Max Drawdown ................................... 110
4.3 Value at Risk ............................................................ 113
4.3.1 Historical Simulation Approach ............................... 114
4.3.2 Variance-Covariance (Parametric) Approach.................. 115
4.3.3 Monte Carlo Simulation ........................................ 120
4.4 Summary ................................................................ 122
5 Risk Management Using Financial Derivatives........................... 125
5.1 Hedging with Futures Contracts........................................ 127
5.1.1 Hedging Mechanism Using Futures ........................... 127
5.1.2 Optimal Hedge Ratio ........................................... 129
5.1.3 Scenario Analysis at Maturity.................................. 132
5.1.4 Consideration of Basis Risk .................................... 133
5.1.5 Implementing the Dynamic Hedging Strategy ................ 134
5.2 Hedging with Option Contracts ........................................ 141
5.2.1 Protective Put Strategy ......................................... 142
5.2.2 Implementing the Protective Put Strategy ..................... 147
5.2.3 Covered Call Strategy .......................................... 152
5.2.4 Implementing the Covered Call Strategy ...................... 157
5.3 Summary ................................................................ 161
6 Static and Dynamic Hedging ............................................... 163
6.1 Dynamic Hedging ....................................................... 166
6.1.1 Dynamic Delta Hedging Strategy .............................. 167
6.1.2 Continuous Rebalancing and Gamma Hedging ............... 168
6.1.3 Dynamic Hedging in Action ................................... 170
6.2 Static Hedging........................................................... 177
6.2.1 Static Hedging for a Forward Contract ........................ 177
6.2.2 Static Hedging for a European Put Option .................... 182
6.2.3 Static Hedging for Digital Option.............................. 190
6.2.4 Static Hedging with Constant Volatility ....................... 191
6.2.5 Static Hedging with Changing Volatility ...................... 194
6.2.6 Static Hedging of Digital Call Option in Action .............. 196
6.3 Summary ................................................................ 199
7 Managing Model Risk in Finance .......................................... 201
7.1 Model Risk Due to Data ................................................ 202
7.1.1 Data Risks in Financial Machine Learning .................... 205
7.1.2 Mitigation Strategies............................................ 211
7.2 Model Risk Due to Model Selection ................................... 214
7.2.1 Model Bias and Approximation Error ......................... 215
7.2.2 Mitigation Strategies............................................ 217
7.3 Model Risk Due to Cost Function...................................... 218
7.3.1 Mitigation Strategies............................................ 220
7.4 Model Risk Due to Optimization Procedure ........................... 221
7.4.1 Estimation Error ................................................ 222
7.4.2 Mitigation Strategies............................................ 224
7.5 Conclusion .............................................................. 225
References......................................................................... 229
Index ............................................................................... 231

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