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E-raamat: Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk

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Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at RiskSuurem pilt
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This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models. 

CHAPTER 1 Introduction.- CHAPTER 2 Time Series Modelling.- CHAPTER 3 Options and Options Pricing Models.- CHAPTER 4 Neural Networks and Financial Forecasting.- CHAPTER 5 Important Problems in Financial Forecasting.- CHAPTER 6 Volatility Forecasting.- CHAPTER 7 Option Pricing.- CHAPTER 8 Value-at-Risk.- CHAPTER 9 Conclusion and Discussion.

Arvustused

The book describes how to deal with the different sorts of nancial market risk. The book can be used by advanced undergraduate students and graduate students in its entirety. It is also interesting for the specialists in nancial market risk and is of considerable importance to practitioners in the eld. (Yuliya S. Mishura, zbMath 1410.91004, 2019)

1 Introduction
1(8)
1.1 Volatility Forecasting
1(2)
1.2 Option Pricing
3(2)
1.3 Risk Management Methods
5(1)
1.4 Neural Networks Approach
6(1)
1.5 Book Layout
7(2)
2 Time Series Modelling
9(22)
2.1 Time Series Properties
10(2)
2.1.1 White Noise
10(1)
2.1.2 Stochastic Processes
10(1)
2.1.3 Stationarity in Time Series
11(1)
2.1.4 Autoregressive Models
12(1)
2.2 Time Series Models
12(5)
2.2.1 The Wiener Process
13(1)
2.2.2 Geometric Brownian Motion with Drift
14(1)
2.2.3 Ito Process
14(1)
2.2.4 Linear Time Series Models
15(1)
2.2.5 Moving Average Model
16(1)
2.2.6 Auto Regressive Moving Model (ARMA)
17(1)
2.3 Financial Time Series Modelling
17(14)
2.3.1 Distributional Properties of the Return Series
18(1)
2.3.2 Stylised Properties of Returns
19(1)
2.3.3 Conditional Mean
20(2)
2.3.4 Volatility Modelling
22(2)
2.3.5 Conditional Heteroscedasticity Models
24(1)
2.3.6 ARCH Model
24(1)
2.3.7 GARCH Model
25(1)
2.3.8 GARCH-in-Mean
25(1)
2.3.9 Exponential GARCH
26(1)
2.3.10 Time Varying Volatility Models Literature Review
27(4)
3 Options and Options Pricing Models
31(20)
3.1 Options
31(3)
3.1.1 Call Options
32(1)
3.1.2 Put Options
32(1)
3.1.3 Options Moneyness
33(1)
3.1.4 Intrinsic Value
33(1)
3.1.5 Time Value
34(1)
3.2 Option Pricing Models and Hedging
34(11)
3.2.1 The Black-Scholes Options Pricing Model
35(1)
3.2.2 Black-Schole Equation
35(1)
3.2.3 Implied Volatility
36(1)
3.2.4 Black-Scholes Option Pricing Model (BSOPM)
37(3)
3.2.5 GARCH Option Pricing Models
40(5)
3.3 Hedging
45(6)
3.3.1 Delta Hedging
46(5)
4 Neural Networks and Financial Forecasting
51(30)
4.1 Neural Net Models
51(12)
4.1.1 Preceptron
52(1)
4.1.2 Multi-layer Preceptron (MLP)
53(2)
4.1.3 Training MLP
55(2)
4.1.4 Back Propagation
57(1)
4.1.5 Mixture Density Networks
58(3)
4.1.6 Radial Base Function Network
61(2)
4.2 Neural Networks in Financial Forecasting
63(18)
4.2.1 Neural Networks for Time Series Forecasting
63(4)
4.2.2 Neural Networks in Conditional Volatility Forecasting
67(5)
4.2.3 Volatility Forecasting with MDN
72(2)
4.2.4 Application of Neural Networks Option Pricing
74(7)
5 Important Problems in Financial Forecasting
81(10)
5.1 Terms and Concepts Used
81(6)
5.1.1 Financial Time Series
81(3)
5.1.2 Options
84(3)
5.2 Problem Definition
87(2)
5.2.1 Volatility Forecasting
87(1)
5.2.2 Option Pricing
88(1)
5.2.3 Delta Hedging
88(1)
5.3 Choice of Methodology
89(1)
5.4 Research Method
90(1)
6 Volatility Forecasting
91(22)
6.1 Volatility Models
91(6)
6.1.1 GARCH Model
92(1)
6.1.2 EGARCH Model
93(1)
6.1.3 Mixture Density Networks
93(4)
6.2 Issues Investigated
97(1)
6.3 Solution Overview
98(3)
6.4 Forecast Evaluation
101(1)
6.5 Data Analysis
102(1)
6.6 Experimentation
103(1)
6.7 Results
103(8)
6.7.1 In-Sample Testing
104(1)
6.7.2 Out-of-Sample Forecast Performance
105(3)
6.7.3 One-Day Volatility Forecast
108(1)
6.7.4 10 and 30 Days Forecast
109(2)
6.8 Conclusion
111(2)
7 Option Pricing
113(24)
7.1 Option Pricing Models
114(7)
7.1.1 GARCH Option Pricing Model (GOPM)
115(2)
7.1.2 BSOPM Option Pricing Model
117(1)
7.1.3 Implied Volatility
117(1)
7.1.4 Artificial Neural Network (ANN)
118(1)
7.1.5 Neural Networks for Option Pricing
119(1)
7.1.6 My Option Pricing Model
120(1)
7.2 Issues Investigated
121(1)
7.3 New Option Pricing Model and Solution Overview
121(2)
7.4 Data
123(1)
7.5 Experimental Design
124(1)
7.5.1 GOPM Parameters
124(1)
7.5.2 Neural Network Training Methods
125(1)
7.6 Performance Measures
125(1)
7.6.1 Pricing Accuracy
125(1)
7.7 Delta Hedging
126(1)
7.7.1 Solution Overview
126(1)
7.7.2 New Method Developed
127(1)
7.8 Results
127(4)
7.9 The Empirical Dynamics of the Volatility Smile
131(3)
7.10 Summary and Conclusion
134(3)
8 Value-at-Risk
137(12)
8.1 Value-at-Risk Review
137(1)
8.2 Value-at-Risk Definition
138(1)
8.3 Modelling Value at Risk with Neural Networks
139(4)
8.3.1 Historical Simulation Method
140(1)
8.3.2 Variance-Covariance Method
141(1)
8.3.3 Monte Carlo Simulation Method
142(1)
8.4 Modelling Value-at-Risk with Neural Networks
143(3)
8.5 Value-at-Risk (VaR)---Future Work
146(3)
9 Conclusion and Discussion
149(10)
9.1 Volatility Forecasting
150(3)
9.2 Option Pricing and Hedging
153(2)
9.3 Recapitulation
155(2)
9.4 Contributions of This Research
157(2)
Appendix A Detailed Results 159(4)
References 163
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