Index

A

• AAA-rated bond
  • yield, retrieving / YIELD of AAA-rated bond, Altman Z-score
• acallable bond / Bond evaluation
• adjusted beta
  • about / Adjusted beta
  • Scholes and William adjusted beta / Scholes and William adjusted beta
• Akaike Information Criterion (AIC)
  • about / Introduction to the Fama-French three-factor model, Durbin-Watson
• Altman Z-score
  • about / YIELD of AAA-rated bond, Altman Z-score
  • bankruptcy, predicting / Appendix A – data case #8 - predicting bankruptcy by using Z-score
• American option
  • versus European option / European versus American options
  • right and obligation / Understanding cash flows, types of options, rights and obligations
  • cash flows / Understanding cash flows, types of options, rights and obligations
  • type of options / Understanding cash flows, types of options, rights and obligations
  • binomial tree (CRR) method / Binomial tree (CRR) method for American options
  • about / European, American, and Bermuda options
• Amihud's illiquidity
  • estimating / Estimating Amihud's illiquidity
• Anaconda
  • Python, installing through / Installation of Python via Anaconda
• Annual Percentage Rate (APR) / Introduction to interest rates
  • about / Introducing futures
• aputtable bond / Bond evaluation
• ARCH (1) process
  • simulating / Simulating an ARCH (1) process
• ARCH model
  • about / The ARCH model
• asset pricing models
  • evaluating / Appendix D – data case #4 – which model is the best, CAPM, FF3, FFC4, or FF5, or others?
• average options
  • pricing / Pricing average options

B

• backtesting
  • about / Backtesting and stress testing
• barrier options
  • pricing, with Monte Carlo Simulation / Exotic option #2 – pricing barrier options using the Monte Carlo Simulation
  • up-and-out / Exotic option #2 – pricing barrier options using the Monte Carlo Simulation, Pricing barrier options
  • down-and-out / Exotic option #2 – pricing barrier options using the Monte Carlo Simulation, Pricing barrier options
  • up-and-in / Exotic option #2 – pricing barrier options using the Monte Carlo Simulation, Pricing barrier options
  • down-and-in / Exotic option #2 – pricing barrier options using the Monte Carlo Simulation, Pricing barrier options
  • pricing / Pricing barrier options
• Bayesian Information Criterion (BIC)
  • about / Introduction to the Fama-French three-factor model
• Bermuda option
  • about / European, American, and Bermuda options
• beta estimation
  • about / References
  • data, downloading / References
  • monthly risk-free rate, downloading / References
• binary-search
  • about / Binary-search
• binary file
  • output data, saving to / Saving our data to a binary file
  • output data, reading from / Reading data from a binary file
• binary option
  • about / Binary options
• binomial tree (CRR) method
  • about / Binomial tree and its graphic presentation
  • graphic presentation / Binomial tree and its graphic presentation
  • for European option / Binomial tree (CRR) method for European options
  • for American option / Binomial tree (CRR) method for American options
• Black-Scholes-Merton call
  • replicating, with simulation / Replicating a Black-Scholes-Merton call using simulation
  • Monte Carlo Simulation, used for pricing average / Exotic option #1 – using the Monte Carlo Simulation to price average
• Black-Scholes-Merton option model
  • on non-dividend paying stocks / Black-Scholes-Merton option model on non-dividend paying stocks
• bond evaluation / Bond evaluation
• bondSpread2014.p dataset
  • URL / Credit spread
• bootstrapping
  • with replacements / With/without replacements
  • without replacements / With/without replacements
• Breusch-Pangan measure
  • about / Test of heteroskedasticity, Breusch, and Pagan
• businessCycle.pkl
  • URL / Merging data with different frequencies

C

• .csv file
  • output data, saving to / Saving our data to a .csv file
• Canopy
  • Python, installing via / Python via Canopy
  • URL, for downloading / Python via Canopy
• capital
  • budgeting, with Monte Carlo Simulation / Capital budgeting with Monte Carlo Simulation
• Capital Asset Pricing Model (CAPM)
  • about / Introduction to CAPM
• CBOE
  • URL, for data / Put-call parity and its graphic presentation
• CBOE Volatility Index (VIX)
  • portfolio, hedging / Appendix A – data case 8 - portfolio hedging using VIX calls
  • references / Appendix A – data case 8 - portfolio hedging using VIX calls
• Center for Research in Security Prices (CRSP)
  • about / Several datasets related to CRSP and Compustat, Introduction to CRSP
  • datasets / Several datasets related to CRSP and Compustat
  • URL / Several datasets related to CRSP and Compustat
  • case study / Appendix C – data case #4 - which political party manages the economy better?
• chooser option
  • about / Chooser options
• Citi Group (C) / Optimization – minimization
• commands, Python module installation for Anaconda
  • conda list / How to install a Python module
  • conda list -n snowflakes / How to install a Python module
  • conda search beautiful-soup / How to install a Python module
  • conda install --name bunnies quant / How to install a Python module
  • conda info / How to install a Python module
• Compustat
  • datasets / Several datasets related to CRSP and Compustat
• Consolidated Quote (CQ) dataset
  • URL / Spread estimated based on high-frequency data
• Consolidated Trade (CT) dataset
  • URL / Python for high-frequency data
• continuum.io
  • reference / Installation of Python via Anaconda
• conventional volatility measure
  • estimating / Conventional volatility measure – standard deviation
• Corporate Bond Yield
  • references / YIELD of AAA-rated bond, Altman Z-score
• credit default swap (CDS)
  • about / Credit default swap
• creditRatigs3.pkl dataset
  • URL / Credit rating
• credit rating
  • about / Credit rating
• credit ratings agents
  • references / Credit rating
• credit risk analysis
  • about / Introduction to credit risk analysis
• credit spread
  • about / Credit spread
• crude oil, hedging
  • case study / Appendix A – data case 7 – hedging crude oil
  • dataset, URL / Appendix A – data case 7 – hedging crude oil
• custom financial calculator
  • writing, in Python / Writing your own financial calculator in Python, Appendix G – Writing your own financial calculator in Python
• custom module p4f
  • generating / Generating our own module p4f

D

• data
  • input / Data input
  • manipulation / Data manipulation
  • output / Data output
  • retrieving / Diving into deeper concepts
  • retrieving, from Yahoo!Finance / Retrieving data from Yahoo!Finance
  • retrieving, from Google Finance / Retrieving data from Google Finance
  • retrieving, from FRED / Retrieving data from FRED
  • retrieving, from Prof. French 's data library / Retrieving data from Prof. French's data library
  • retrieving, from Census Bureau / Retrieving data from the Census Bureau, Treasury, and BLS
  • retrieving, from Treasury / Retrieving data from the Census Bureau, Treasury, and BLS
  • retrieving, from BLS / Retrieving data from the Census Bureau, Treasury, and BLS
• datasets
  • merging / How to merge different datasets
  • merging, based on date variable / Merging datasets based on a date variable
  • pandas.date_range() function, used for time-series / Using pandas.date_range() to generate one dimensional time-series
  • returns, estimating / Return estimation
  • daily returns, converting to monthly ones / Converting daily returns to monthly ones
  • merging, by date / Merging datasets by date
• data types / Data manipulation
• date variable
  • datasets, merging / Merging datasets based on a date variable
• default probability
  • URL / Credit rating
• degree of freedom (F)
  • critical values, generating / Appendix B – critical values of F for the 0.05 significance level
• delta hedge
  • about / Hedging strategies
• dictionary / A new data type – dictionary
• Dimson (1979) adjustment for beta
  • implementation / Implementation of Dimson (1979) adjustment for beta
• distance to default (DD)
  • about / Distance to default
• distribution of annual returns
  • estimating / Distribution of annual returns
• Durbin-Watson
  • about / Durbin-Watson
• Durbin-Watson test
  • reference / Durbin-Watson
• dynamic hedging
  • about / Hedging strategies

E

• economics, Python module
  • reference link / Python modules related to finance
• Effective Annual Rate (EAR) / Introduction to interest rates
• efficient frontier
  • constructing, with n stocks / Constructing an efficient frontier with n stocks, Constructing an efficient frontier with n stocks
  • generating, based on two stocks with simulation / Finding an efficient frontier based on two stocks by using simulation
• empty space / Variable assignment, empty space, and writing our own programs
• equal-weighted market index (EWRETD) / Appendix C – data case #4 - which political party manages the economy better?
• equal variances
  • testing / Tests of equal variances
• European option
  • versus American option / European versus American options
  • cash flows / Understanding cash flows, types of options, rights and obligations
  • type of options / Understanding cash flows, types of options, rights and obligations
  • right and obligation / Understanding cash flows, types of options, rights and obligations
  • with known dividends / European options with known dividends
  • binomial tree (CRR) method / Binomial tree (CRR) method for European options
  • about / European, American, and Bermuda options
• Excel file
  • output data, saving to / Saving our data to an Excel file
• Expected shortfall (ES)
  • about / Expected shortfall

F

• F-test
  • about / T-test and F-test
• Fama-French-Carhart four-factor model
  • about / Fama-French-Carhart four-factor model and Fama-French five-factor model
• Fama-French five-factor model
  • about / Fama-French-Carhart four-factor model and Fama-French five-factor model
• Fama-French monthly dataset
  • URL / Fama-French three-factor model
• Fama-French three-factor model
  • about / Introduction to the Fama-French three-factor model, Fama-French three-factor model
• Fama-MacBeth regression
  • about / Fama-MacBeth regression
• fat tails
  • estimating / Estimating fat tails
  • reference / Estimating fat tails
• Federal Reserve Economics Data (FRED)
  • about / Introduction to the pandas_reader module
• ffDaily.pkl dataset
  • URL / Lower partial standard deviation and Sortino ratio
• ffMonthly.pkl dataset
  • reference / Data input
  • generating / Appendix B – Python program to generate ffMonthly.pkl
• ffMonthly.pkl datasets
  • URL / How to merge different datasets, Introduction to time-series analysis
• ffMonthly5.pkl dataset
  • URL / Fama-French-Carhart four-factor model and Fama-French five-factor model
• ffMonthly dataset
  • URL / Merging data with different frequencies
• fGarch package
  • URL / Simulating a GARCH (p,q) process using modified garchSim()
• finance
  • Python module, used / Python modules related to finance
• financial calculator
  • writing, in Python / Writing a financial calculator in Python
  • downloading / Appendix D – How to download a free financial calculat
• financial calculators
  • about / Two financial calculators
• fincal.cpython-35.syc
  • URL, for downloading / Two financial calculators
• floating strikes
  • lookback options, pricing with / Pricing lookback options with floating strikes
• French Data library
  • URL / References
• Frenchs Data Library
  • data, URL / Appendix A – data case #5 - which industry portfolio do you prefer?
• functions
  • using / Two general formulae for many functions
  • pv() / Two general formulae for many functions
  • fv() / Two general formulae for many functions
  • nper() / Two general formulae for many functions
  • pmt() / Two general formulae for many functions
  • rate() / Two general formulae for many functions
• futures
  • about / Introducing futures

G

• GARCH (p,q) process
  • simulating, with modified garchSim() function / Simulating a GARCH (p,q) process using modified garchSim()
• GARCH process
  • simulating / Simulating a GARCH process
• GDP dataset usGDPquarterly2.pkl
  • generating, with Python / Appendix A – Python program to generate GDP dataset usGDPquarterly2.pkl
• Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) / The GARCH model
• GJR_GARCH
  • about / GJR_GARCH by Glosten, Jagannanthan, and Runkle
• Greeks
  • Delta / Greeks
  • Gamma / Greeks
  • Theta / Greeks
  • Vega / Greeks
  • Rho / Greeks
• Gross Domestic Product (GDP)
  • about / Introduction to time-series analysis
  / Merging data with different frequencies

H

• hedging strategies
  • about / Hedging strategies
• heteroskedasticity
  • testing / Test of heteroskedasticity, Breusch, and Pagan
• high-frequency data
  • with Python / Python for high-frequency data
  • spread, estimating / Spread estimated based on high-frequency data
• High Minus Low (HML)
  • about / Fama-French three-factor model
  / Selecting m stocks randomly from n given stocks
• histogram
  • for normal distribution / Histogram for a normal distribution
• historical price data
  • URL / Introduction to CAPM

I

• implied volatility
  • estimating / Implied volatility
• in-and-out parity, barrier options
  • about / Barrier in-and-out parity
• industry portfolio
  • preferences, determining / Appendix A – data case #5 - which industry portfolio do you prefer?
• installation
  • Python / Python installation
  • of Python, via Anaconda / Installation of Python via Anaconda
• interest rate
  • term structure / Term structure of interest rate
• interest rates
  • about / Introduction to interest rates
  • term structure / Term structure of interest rates
• Internal Rate of Return (IRR)
  • about / Definition of IRR and IRR rule
  • graphical presentation, in relationship with NPV profile / Appendix F – graphical presentation of NPV profile with two IRRs
  / Term structure of interest rates
• International Business Machine (IBM) / Optimization – minimization
• International Business Machine Corporation (IBM) / Appendix F – data case #2 – fund raised from a new bond issue
• International Rate of Return (IRR) / Bond evaluation
• interpolation
  • about / Understanding the interpolation technique
  • data, merging with different frequencies / Merging data with different frequencies
• intra-day high-frequency data
  • URL / Python for high-frequency data
• IRR rule
  • about / Definition of IRR and IRR rule

J

• January effect
  • testing / Testing the January effect

K

• KMV model
  • used, for estimating market value of total assets / Using the KMV model to estimate the market value of total assets and its volatility
  • used, for estimating volatility / Using the KMV model to estimate the market value of total assets and its volatility
• kurtosis
  • about / Skewness and kurtosis

L

• lognormal distribution
  • graphical presentation / Graphical presentation of a lognormal distribution
• long-term return forecast
  • estimating / Long-term return forecasting
• lookback options
  • pricing, with floating strikes / Pricing lookback options with floating strikes
• Loss given default (LGD) / Credit rating
• lower partial standard deviation
  • about / Lower partial standard deviation and Sortino ratio
• Lower Partial Standard Deviation (LPSD)
  • about / Performance measures, Constructing an efficient frontier with n stocks

M

• Markowitz Portfolio Optimization
  • about / Introduction to portfolio theory
• matplotlib
  • about / Introduction to matplotlib
  • installing / How to install matplotlib
  • using, for graphical presentations / Several graphical presentations using matplotlib
• migration1year.pkl dataset
  • URL / Credit rating
• migration5year.pkl dataset
  • URL / Credit rating
• modified garchSim() function
  • GARCH (p,q) process, simulating with / Simulating a GARCH (p,q) process using modified garchSim()
• modified VaR
  • about / Modified VaR
• Monte Carlo Simulation
  • about / Importance of Monte Carlo Simulation
  • used, for pricing average / Exotic option #1 – using the Monte Carlo Simulation to price average
  • barrier options, pricing / Exotic option #2 – pricing barrier options using the Monte Carlo Simulation
  • capital, budgeting / Capital budgeting with Monte Carlo Simulation
  • data case / Appendix A – data case #8 - Monte Carlo Simulation and blackjack
• Monte Carlo simulation
  • about / Simulation and VaR
  • efficiency / Efficiency, Quasi-Monte Carlo, and Sobol sequences
• moving beta
  • about / Moving beta

N

• n-stock portfolio
  • forming / Forming an n-stock portfolio
• National Association of Insurance Commissioners (NAIC)
  • about / Credit rating
• Net Present Value (NPV) / Python loops, if...else conditions
  • about / Definition of NPV and NPV rule, Importance of Monte Carlo Simulation
  • graphical presentation, in relationship with R / Appendix E – The graphical presentation of the relationship between NPV and R
• normal distribution
  • random numbers, generating / Generating random numbers from a standard normal distribution
  • random samples, drawing / Drawing random samples from a normal distribution
  • random numbers, generating from / Random numbers from a normal distribution
  • histogram / Histogram for a normal distribution
• normality tests
  • about / Tests of normality, Tests of normality
  • fat tails, estimating / Estimating fat tails
  • T-test / T-test and F-test
  • F-test / T-test and F-test
  • equal variances, testing / Tests of equal variances
  • January effect, testing / Testing the January effect
• Normality tests
  • about / Normality tests
• NPV profile
  • graphical presentation, in relationship with IRR / Appendix F – graphical presentation of NPV profile with two IRRs
• NPV rule
  • about / Definition of NPV and NPV rule
• NumPy
  • about / Introduction to NumPy
  • installation / Appendix A – Installation of Python, NumPy, and SciPy

O

• objective function
  • minimizing / Optimization – minimization
• one dimensional time-series
  • generating, with pandas.date_range() function / Using pandas.date_range() to generate one dimensional time-series
• optimal portfolio
  • constructing / Constructing an optimal portfolio
• ordinary least square (OLS)
  • about / Introduction to statsmodels
• Ordinary Least Squares (OLS)
  • about / Introduction to the Fama-French three-factor model
• output data
  • extracting / Extracting output data
  • extracting, to text files / Outputting data to text files
  • saving, to .csv file / Saving our data to a .csv file
  • saving, to Excel file / Saving our data to an Excel file
  • saving, to pickle dataset / Saving our data to a pickle dataset
  • saving, to binary file / Saving our data to a binary file
  • reading, from binary file / Reading data from a binary file

P

• .pickle dataset
  • URL / Volatility smile and skewness
• pandas.date_range() function
  • used, for generating one dimensional time-series / Using pandas.date_range() to generate one dimensional time-series
• pandas module
  • about / Introduction to pandas
• pandas_reader module
  • about / Introduction to the pandas_reader module
• Pastor and Stambaugh (2003) liquidity measure
  • estimating / Estimating Pastor and Stambaugh (2003) liquidity measure
• payback period
  • about / Definition of payback period and payback period rule
• payback period rule
  • about / Definition of payback period and payback period rule
• payoff function
  • for put option / Payoff and profit/loss functions for call and put options
  • for call option / Payoff and profit/loss functions for call and put options
• performance measures
  • about / Performance measures
• pickle dataset
  • output data, saving to / Saving our data to a pickle dataset
• pi value
  • estimating, with simulation / Using simulation to estimate the pi value
• Poisson distribution
  • random numbers, generating from / Generating random numbers from a Poisson distribution
• portfolio
  • hedging, with CBOE Volatility Index (VIX) calls / Appendix A – data case 8 - portfolio hedging using VIX calls
• portfolio insurance
  • about / Appendix A – data case 6: portfolio insurance
  • data case / Appendix A – data case 6: portfolio insurance
• portfolio theory
  • about / Introduction to portfolio theory
• present value (pv)
  • about / Two general formulae for many functions
  • deriving / Appendix C – Derivation of present value of annuity from present value of one future cash flow and present value of perpetuity
• Probability of informed (PIN)
  • about / Generating random numbers from a Poisson distribution
• profit/loss functions
  • for call option / Payoff and profit/loss functions for call and put options
  • for put option / Payoff and profit/loss functions for call and put options
• programs
  • writing / Variable assignment, empty space, and writing our own programs
• put-call parity
  • about / Put-call parity and its graphic presentation
  • graphic presentation / Put-call parity and its graphic presentation
  • put-call ratio, with trend for short period / The put-call ratio for a short period with a trend
• put call ratio
  • URL / Put-call parity and its graphic presentation
• Python
  • installation / Python installation, Appendix A – Installation of Python, NumPy, and SciPy
  • installation, via Anaconda / Installation of Python via Anaconda
  • launching, via Spyder / Launching Python via Spyder
  • direct installation / Direct installation of Python
  • reference / Direct installation of Python
  • URL, for tutorial / What is a Python module?
  • financial calculator, writing in / Writing a financial calculator in Python
  • custom financial calculator, writing / Writing your own financial calculator in Python, Appendix G – Writing your own financial calculator in Python
  • URL / Appendix A – Installation of Python, NumPy, and SciPy
  • installing, via Canopy / Python via Canopy
  • used, for high-frequency data / Python for high-frequency data
  • GDP dataset usGDPquarterly2.pkl, generating / Appendix A – Python program to generate GDP dataset usGDPquarterly2.pkl
• Python dataset
  • reference / Appendix F – data case #2 – fund raised from a new bond issue
• Python datasets
  • URL / Appendix A – list of related Python datasets
  • ibm3factor.pkl / Appendix A – list of related Python datasets
  • ffMonthly.pkl / Appendix A – list of related Python datasets
  • ffMomMonthly.pkl / Appendix A – list of related Python datasets
  • ffcMonthly.pkl / Appendix A – list of related Python datasets
  • ffMonthly5.pkl / Appendix A – list of related Python datasets
  • yanMonthly.pkl / Appendix A – list of related Python datasets
  • ffDaily.pkl / Appendix A – list of related Python datasets
  • ffcDaily.pkl / Appendix A – list of related Python datasets
  • ffDaily5.pkl / Appendix A – list of related Python datasets
  • usGDPquarterly.pkl / Appendix A – list of related Python datasets
  • usDebt.pkl / Appendix A – list of related Python datasets
  • usCPImonthly.pkl / Appendix A – list of related Python datasets
  • tradingDaysMonthly.pkl / Appendix A – list of related Python datasets
  • tradingDaysDaily.pkl / Appendix A – list of related Python datasets
  • businessCycleIndicator.pkl / Appendix A – list of related Python datasets
  • businessCycleIndicator2.pkl / Appendix A – list of related Python datasets
  • uniqueWordsBible.pkl / Appendix A – list of related Python datasets
• Python function
  • writing / Writing a Python function
• Python loops
  • about / Python loops
  • if...else conditions / Python loops, if...else conditions
• Python module
  • about / What is a Python module?
  • related to finance / Python modules related to finance
  • installing / How to install a Python module
  • dependency / Module dependency
  • advantages / Module dependency
  • disadvantages / Module dependency
• Python module, in finance
  • Numpy.lib.financial / Python modules related to finance
  • pandas_datareader / Python modules related to finance
  • googlefinance / Python modules related to finance
  • yahoo-finance / Python modules related to finance
  • Python_finance / Python modules related to finance
  • tstockquote / Python modules related to finance
  • finance / Python modules related to finance
  • quant / Python modules related to finance
  • tradingmachine / Python modules related to finance
  • economics / Python modules related to finance
  • FinDates / Python modules related to finance
• Python module, installation for Anaconda
  • reference link / How to install a Python module
• Python Module Index (v2.7)
  • reference link / Python modules related to finance
• Python Module Index (v3.5)
  • reference link / Python modules related to finance
• Python Package Index (PyPI)
  • reference link / Python modules related to finance
• Python Packaging Index (PIP)
  • about / Appendix A – Installation of Python, NumPy, and SciPy
• Python program
  • return distribution, versus normal distribution / Appendix A – Python program for return distribution versus a normal distribution
  • candle-stick picture, drawing / Appendix B – Python program to a draw candle-stick picture
  • for price movement / Appendix C – Python program for price movement
  • for displaying stock's intra-day movement / Appendix D – Python program to show a picture of a stock's intra-day movement
  • pandas DataFrame, properties / Appendix E –properties for a pandas DataFrame
  • Python dataset with .pkl extension, generating / Appendix F –how to generate a Python dataset with an extension of .pkl or .pickle
  • Python dataset with .pickle extension, generating / Appendix F –how to generate a Python dataset with an extension of .pkl or .pickle
  • several Python datasets, generating / Appendix G – data case #1 -generating several Python datasets
  • for rateYan.py / Appendix A – simple interest rate versus compounding interest rate
  • for interest conversion / Appendix A – simple interest rate versus compounding interest rate
  • for stock price based n-period model estimation / Appendix D – Python program to estimate stock price based on an n-period model
  • for bond duration estimation / Appendix F – data case #2 – fund raised from a new bond issue
• Python SimPy module
  • about / Python SimPy module

Q

• Quasi Monte Carlo
  • about / Efficiency, Quasi-Monte Carlo, and Sobol sequences

R

• R
  • graphical presentation, in relationship with NPV / Appendix E – The graphical presentation of the relationship between NPV and R
• rainbow options
  • about / Rainbow options
• random numbers
  • generating, from normal distribution / Generating random numbers from a standard normal distribution, Random numbers from a normal distribution
  • generating, with seed / Generating random numbers with a seed
  • histogram, for normal distribution / Histogram for a normal distribution
  • lognormal distribution, graphical presentation / Graphical presentation of a lognormal distribution
  • generating, from uniform distribution / Generating random numbers from a uniform distribution
  • generating, from Poisson distribution / Generating random numbers from a Poisson distribution
• recovery rates
  • reference / Credit rating
• risk-free rate / Term structure of interest rate
• risk-free rate (Rf)
  • about / Performance measures, Constructing an efficient frontier with n stocks
• Roll's spread
  • estimating / Estimating Roll's spread
• Root Mean Standard Square Error (RMSE)
  • about / Introduction to the Fama-French three-factor model

S

• 2-Step Approach / Introduction to interest rates
• 2-stock portfolio
  • about / A 2-stock portfolio
• S&P500 Index (SPX) / Appendix A – data case 8 - portfolio hedging using VIX calls
• S&P500 monthly returns
  • replicating / Appendix B – data case #6 - replicate S
• Scholes and William adjusted beta
  • about / Scholes and William adjusted beta
• SciPy
  • about / Introduction to SciPy
  • installation / Appendix A – Installation of Python, NumPy, and SciPy
• scipy.optimize.minimize() function
  • NelderMead / Optimization – minimization
  • Powell / Optimization – minimization
  • CG / Optimization – minimization
  • BFGS / Optimization – minimization
  • NewtonCG / Optimization – minimization
  • LBFGSB / Optimization – minimization
  • TNC / Optimization – minimization
  • COBYLA / Optimization – minimization
  • SLSQP / Optimization – minimization
  • trustncg / Optimization – minimization
  • dogleg / Optimization – minimization
• seed
  • random numbers, generating / Generating random numbers with a seed
• Shapiro-Wilk test / Tests of normality
• shout option
  • about / Shout options
• simple interest rate
  • versus compounding interest rate / Appendix A – simple interest rate versus compounding interest rate
• SimPy
  • about / Python SimPy module
• simulation
  • pi value, estimating with / Using simulation to estimate the pi value
  • Black-Scholes-Merton call, replicating with / Replicating a Black-Scholes-Merton call using simulation
  • methods, liking for VaR / Liking two methods for VaR using simulation
  • used, for obtaining efficient frontier based on stocks / Finding an efficient frontier based on two stocks by using simulation
• skewness
  • volatility smile / Volatility smile and skewness
  • about / Skewness and kurtosis
• Small Minus Big (SMB)
  • about / Fama-French three-factor model
  / Selecting m stocks randomly from n given stocks
• Sobol sequence
  • about / Efficiency, Quasi-Monte Carlo, and Sobol sequences
• social policies, comparison
  • example / Comparison between two social policies – basic income and basic job
• Sortino ratio
  • about / Lower partial standard deviation and Sortino ratio
• Spyder
  • Python, launching through / Launching Python via Spyder
  • Python, launching via / Launching Python via Spyder
• statsmodels
  • about / Introduction to statsmodels
• stock price movements
  • simulation / Simulation of stock price movements
• stock prices
  • graphical presentation, at options maturity dates / Graphical presentation of stock prices at options' maturity dates
• stocks
  • selecting, randomly from given stocks / Selecting m stocks randomly from n given stocks
• stock valuation / Stock valuation
• stress testing
  • about / Backtesting and stress testing
• strftime
  • URL / Using pandas.date_range() to generate one dimensional time-series
• string manipulation
  • about / Simple string manipulation

T

• T-test
  • about / T-test and F-test
• text files
  • output data, extracting to / Outputting data to text files
• time-series analysis
  • about / Introduction to time-series analysis
• time value of money
  • about / Introduction to time value of money
  • visual presentation / Appendix B – visual presentation of time value of money
• Trade, Order, Report, and Quotation (TORQ) database
  • about / Python for high-frequency data
  • URL / Python for high-frequency data
• Trade and Quotation (TAQ) database
  • URL / Python for high-frequency data
• trading strategies
  • about / Various trading strategies
  • covered-call / Covered-call – long a stock and short a call
  • scenario / Straddle – buy a call and a put with the same exercise prices
  • butterfly, with calls / Butterfly with calls
  • input values and option values, relationship between / The relationship between input values and option values
  • Greeks / Greeks
• two dozen datasets
  • generating / Generating two dozen datasets

U

• uniform distribution
  • random numbers, generating / Generating random numbers from a uniform distribution
• uniqueWordsBible.pkl file
  • URL / Simple string manipulation
• up-and-in parity, barrier options
  • graph / Graph of up-and-out and up-and-in parity
• up-and-out parity, barrier options
  • graph / Graph of up-and-out and up-and-in parity

V

• value-weighted market returns (VWRETD) / Appendix C – data case #4 - which political party manages the economy better?
• Value at Risk (VaR)
  • about / Introduction to VaR
  • estimation / Simulation and VaR
  • for portfolios / VaR for portfolios
  • estimation, case study / Appendix A – data case 7 – VaR estimation for individual stocks and a portfolio
• VaR
  • methods, liking with simulation / Liking two methods for VaR using simulation
• VaR based on historical returns
  • about / VaR based on sorted historical returns
• variable assignment / Variable assignment, empty space, and writing our own programs
• volatility
  • equivalency, testing over two periods / Test of equivalency of volatility over two periods
• volatility clustering
  • graphical presentation / Graphical presentation of volatility clustering
• volatility skewness
  • about / Volatility smile and skewness
• volatility smile
  • skewness / Volatility smile and skewness
  • about / Volatility smile and skewness
  • implications / Appendix B – data case 8 - volatility smile and its implications

W

• 52-week high and low trading strategy
  • about / 52-week high and low trading strategy
• Walmart (WMT) / Optimization – minimization
  • about / Modified VaR

Y

• Yahoo!Finance
  • reference / Data input, Term structure of interest rates
• Yahoo! Finance
  • URL, for dataset / Introduction to the Fama-French three-factor model, Appendix B – data case 8 - volatility smile and its implications
  • option data, retrieving / Retrieving option data from Yahoo! Finance
  • URL / Retrieving option data from Yahoo! Finance, Volatility smile and skewness
• Yahoo! Finance bond
  • URL / Understanding the interpolation technique
• Yahoo Finance
  • URL / References
• yanMonthly.pkl dataset
  • URL / Scholes and William adjusted beta, Forming an n-stock portfolio, Constructing an efficient frontier with n stocks, Selecting m stocks randomly from n given stocks
• Yield to Maturity (YTM) / Term structure of interest rates, Bond evaluation