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)
- 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
- 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)
- Bermuda option
- 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
- businessCycle.pkl
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)
- 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
- Consolidated Quote (CQ) dataset
- Consolidated Trade (CT) dataset
- 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
- 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
- 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
- Fama-French five-factor model
- Fama-French monthly dataset
- 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)
- ffDaily.pkl dataset
- ffMonthly.pkl dataset
- reference / Data input
- generating / Appendix B – Python program to generate ffMonthly.pkl
- ffMonthly.pkl datasets
- ffMonthly5.pkl dataset
- ffMonthly dataset
- fGarch package
- 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
- 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
- Greeks
- Gross Domestic Product (GDP) / Merging data with different frequencies
H
- hedging strategies
- about / Hedging strategies
- heteroskedasticity
- 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
- 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
- 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
- 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
- Lower Partial Standard Deviation (LPSD)
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)
- 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
- 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
- Pastor and Stambaugh (2003) liquidity measure
- payback period
- 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
- portfolio theory
- about / Introduction to portfolio theory
- present value (pv)
- Probability of informed (PIN)
- 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
- 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
- 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
- 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)
- 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
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)
- Roll's spread
- estimating / Estimating Roll's spread
- Root Mean Standard Square Error (RMSE)
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
- 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
- Sobol sequence
- social policies, comparison
- 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
- 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
- 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
- Trade and Quotation (TAQ) database
- 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
- up-and-in parity, barrier options
- up-and-out parity, barrier options
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
- 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
- Walmart (WMT) / Optimization – minimization
- about / Modified VaR
Y
- Yahoo!Finance
- reference / Data input, Term structure of interest rates
- Yahoo! Finance
- Yahoo! Finance bond
- Yahoo Finance
- URL / References
- yanMonthly.pkl dataset
- Yield to Maturity (YTM) / Term structure of interest rates, Bond evaluation