This DATASETNAME readme.txt file was generated on 20190408 by R.S.Tunaru/E.N.B.Quaye ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset: NNEG valuation dataset 2. Author Information Principal Investigator Contact Information Name:Radu S. Tunaru Institution: University of Kent Address: Kent Business School, Sibson, Parkwood Road, Canterbury, CT2 7FS Email: R.Tunaru@kent.ac.uk Associate or Co-investigator Contact Information Name: Enoch N.B.Quaye Institution: University of Kent Address: Kent Business School, Sibson, Parkwood Road, Canterbury, CT2 7FS Email:E.N.B.Quaye@kent.ac.uk Alternate Contact Information Name: Rebecca Groves Institution: University of Kent Address: Park Wood, Canterbury CT2 7FS Email: R.L.Groves@kent.ac.uk 3. Date of data collection (single date, range, approximate date) : 20181015-20181031 4. Geographic location of data collection (where was data collected?): United Kingdom 5. Information about funding sources that supported the collection of the data: Institute and Faculty of Actuaries UK and Association of British Insurers --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: Excel-NNEG Short description: Microsoft excel file. Presensets "Excel vba code for replicating matlab computations under baseline specifications used in the study. Both GBM and ARMA-EGARCH versions have been implemented" B. Filename: Matlab-NNEG Short description: Matlab coding files and corresponding excel dataset. Datafiles: ArmaEgarch_RWdataGJR, ArmaEgarch_RWdataNEW, conditionalvol, conditionalvolGJR, decrement_probabilities_new, HumanMortalityDataBase, RFyieldcurve Matlab Code:ArmaEgarchMC, ArmaEgarchMC_fr, ArmaEgarchMC_RW, ArmaEgarchMC_RW_fr, ArmaEgarchRN_singlerun, ArmaEgarchRW_singlerun, ArmaGJR, ArmaGJR_RW, figGen, figGen_fr, figGen_gjr, NNEG_valuation, NNEG_valuation_fr, NNEG_valuationGJR, NNEG_valuationGJR_fr, nnegGBM_RN, nnegGBM_RN_fr, nnegGBM_RW, nnegGBM_RW_fr, nnegImpVol ArmaEgarchMC: Matlab function for Monte Carlo valuation of NNEG under ARMA-EGARCH houseprice simulation under risk-neutral world. The function requires the following inputs age of borrower (age), risk-free rate (r), Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. ArmaEgarchMC_fr: Matlab function for Monte Carlo valuation of NNEG under ARMA-EGARCH houseprice simulation under risk-neutral world. The function requires the following inputs age of borrower (age), floating risk-free rate (r) using the risk-free yield curve on 26 December 2018, Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. ArmaEgarchMC_RW: Matlab function for Monte Carlo valuation of NNEG under ARMA-EGARCH houseprice simulation under real world. The function requires the following inputs age of borrower (age), risk-free rate (r), Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. ArmaEgarchMC_RW_fr: Matlab function for Monte Carlo valuation of NNEG under ARMA-EGARCH houseprice simulation under real world. The function requires the following inputs age of borrower (age), floating risk-free rate (r) using the risk-free yield curve on 26 December 2018, Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. ArmaEgarchRN_singlerun: Matlab code for generating NNEG values including corresponding cashflows. The output variables are [nneg, cashflowtab, avgcashflowtab, sim_hse_px]. The code is based on the ArmaEgarchMC function. The current parameters calculates the NNEG value and cashflows for a male borrower aged 60 with initial house price value 310000. Risk-free rate is 1.75%, roll-up rate is 4.15%, rental yield is 1%, LTV is 17% with six-months time-till-sale of underlying property at termination. Computation is under risk-neutral world. ArmaEgarchRW_singlerun: Matlab code for generating NNEG values including corresponding cashflows. The output variables are [nneg, cashflowtab, avgcashflowtab, sim_hse_px]. The code is based on the ArmaEgarchMC function. The current parameters calculates the NNEG value and cashflows for a male borrower aged 60 with initial house price value 310000. Floating risk-free interest rate (using the risk-free yield curve for December 16, 2018), roll-up rate is 5.25%, rental yield is 1%, LTV is 17% with six-months time-till-sale of underlying property at termination. Computation is under risk-neutral world. ArmaGJR: Matlab function for Monte Carlo valuation of NNEG under ARMA-GJR houseprice simulation under real-neutral analysis. The function requires the following inputs age of borrower (age), fixed risk-free rate (r), Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. The output of the function is [nneg, cashflowtab, avgcashflowtab, sim_hse_px]; where nneg = NNEG value, cashflowtab = Cash flow table, avgcashflowtab = table of average cash-flow. ArmaGJR: Matlab function for Monte Carlo valuation of NNEG under ARMA-GJR houseprice simulation under real world. The function requires the following inputs age of borrower (age), fixed risk-free rate (r), Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. The output of the function is [nneg, cashflowtab, avgcashflowtab, sim_hse_px]; where nneg = NNEG value, cashflowtab = Cash flow table, avgcashflowtab = table of average cash-flow. conditionalvol: This is an excel file for the simulated conditional volatility under the ARMA-EGARCH model. There 10,000 simulated house prices for each month (552 months in all i.e. 100-55+1) conditionalvolGJR: This is an excel file for the simulated conditional volatility under the ARMA-GJR model. There 10,000 simulated house prices for each month.There 10,000 simulated house prices for each month and 552 months. decrement_probabilities_new: Termination probabilities are contained in here. For both male, female and joint-life borrowers, we have probability of decrement by mortality, probability of decrement by transition into long-term-care and probability of decrement by voluntary prepayment. The probabilities depend on Office of National Statistics (ONS) life table for 2015-2016. figGen: Matlab function for generating all figures in the report. The figures are for the risk-neutral case under GBM and ArmaEgarch. The final output is the graph. Required inputs are: risk-free interest rate, rollup-rate, rental-yield, estimated mu (under GBM), estimated sigma (under GBM), the loan-to-value (LTV) ration, mload (loading factor for mortality sensitivity), pload (loading factor for prepayment sensitivity). figGen_fr: Matlab function for generating all ArmaEgarch/GBM NNEG figures in the report. The figures are for the risk-neutral case under GBM and ArmaEgarch. The final output is the graph. Required inputs are: floating risk-free interest rate from December 26, 2018 yield curve), rollup-rate, rental-yield, estimated mu (under GBM), estimated sigma (under GBM), the loan-to-value (LTV) ration, mload (loading factor for mortality sensitivity), pload (loading factor for prepayment sensitivity). figGen_fr: Matlab function for generating ArmaEgarch/GBM NNEG figures in the report. It uses floating risk-free interest rates. The figures are for the risk-neutral case under GBM and ArmaEgarch. The final output is the graph. Required inputs are: floating risk-free interest rate from December 26, 2018 yield curve), rollup-rate, rental-yield, estimated mu (under GBM), estimated sigma (under GBM), the loan-to-value (LTV) ration, mload (loading factor for mortality sensitivity), pload (loading factor for prepayment sensitivity). figGen_gjr: Matlab function for generating ArmaGJR/GBM NNEG figures related to the. The figures are for the risk-neutral case under GBM and ArmaEgarch. The final output is the graph. Required inputs are: floating risk-free interest rate from December 26, 2018 yield curve), rollup-rate, rental-yield, estimated mu (under GBM), estimated sigma (under GBM), the loan-to-value (LTV) ration, mload (loading factor for mortality sensitivity), pload (loading factor for prepayment sensitivity). NNEG_valuation: Matlab code for running the entire ArmaEgarch and GBM models. Both the real-world and risk-neutral world models are presented in the code. It also demonstrates how the NNEG related graphs are generated in the report. NNEG_valuation_fr: Matlab code for running the entire ArmaEgarch and GBM models. Both the real-world and risk-neutral world models are presented in the code. It also demonstrates how the NNEG related graphs are generated in the report. All NNEG valuations are under floating risk-free interest rate assumption. NNEG_valuationGJR: Matlab code for running the entire ArmaGJR and GBM models. Both the real-world and risk-neutral world models are presented in the code. It also demonstrates how the NNEG related graphs are generated in the report. All NNEG valuations are under fixed risk-free interest rate assumption. NNEG_valuationGJR_fr: Matlab code for running the entire ArmaGJR and GBM models. Both the real-world and risk-neutral world models are presented in the code. It also demonstrates how the NNEG related graphs are generated in the report. All NNEG valuations are under floating risk-free interest rate assumption. nnegGBM_RN: Matlab function for Monte Carlo valuation of NNEG under GBM houseprice simulation under risk-neutral world. The function requires the following inputs age of borrower (age), risk-free rate (r), Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), sigma, length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. nnegGBM_RN_fr: Matlab function for Monte Carlo valuation of NNEG under GBM houseprice simulation under risk-neutral world. The function requires the following inputs age of borrower (age), floating risk-free rate from December 26, 2018 yield curve, Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), sigma, length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. nnegGBM_RW: Matlab function for Monte Carlo valuation of NNEG under GBM houseprice simulation under real world. The function requires the following inputs age of borrower (age), risk-free rate (r), Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), sigma, length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. nnegGBM_RW_fr: Matlab function for Monte Carlo valuation of NNEG under GBM houseprice simulation under real world. The function requires the following inputs age of borrower (age), floating risk-free rate from December 26, 2018 yield curve, Roll-up rate (R), rental yield (g), initial house price H0, gender of borrower (gender), sigma, length of time-till-sale of underlying property upon termination (delta), loan-to-value ratio (LTV), mortality adjustment factor (mort_load), prepayment adjustment factor (prepay_load) and the required data. nnegImpVol: Matlab function for estimating the implied volatility that equates the Arma-Egarch NNEG price to the GBM NNEG price. The output variables are [put_price, impvol, gbmprice] while the input variables are [age, risk-free rate, roll-up rate, rental-yield, initial house price, loan-to-value (LTV) ratio, and data]. C. Filename: R-timeseries_fitting_diagnostics Short description: All files required for analysis in the R-software. These include coding scripts and related excel data files. There are 4 excel datafiles used in R i.e. Halifax-Dataset.xlsx, hpimonthly.xls, hpiquarterly.xlsx, insamplefit.csv. The R-scripts are labeled 1) ArmaEgarch-2yr_OutsampleAnalysis_MonthlyNWIdataset_1991-2018, 2) ArmaEgarch-5yr_OutsampleAnalysis_QuarterlyNWEdataset_1952-2018, and 3) Halifax-GBM_ParameterEstimation. Each fille name indicates the purpose of the file. ArmaEgarch-2yr_OutsampleAnalysis_MonthlyNWIdataset_1991-2018: This file is the R-script for the 2-year out-of-sample ARMA(4,3)-EGARCH(1,1) time-series model. The code uses monthly Nationwide houseprices from 1991-2018. Comments on how to run the script including required packages that need to be installed before running the script are also reported in the script. The output of the script is a list that reports the estimated ARMA(4,3)-EGARCH(1,1) regression parameters together with the regression diagnostics. ArmaEgarch-5yr_OutsampleAnalysis_QuarterlyNWIdataset_1952-2018: This file is the R-script for the 5-year out-of-sample ARMA(4,3)-EGARCH(1,1) time-series model. The code uses quarterly Nationwide houseprices from 1952-2018. Comments on how to run the script including required packages that need to be installed before running the script are also reported in the script. The output of the script is a list that reports the estimated ARMA(4,3)-EGARCH(1,1) regression parameters together with the regression diagnostics. Halifax-GBM_ParameterEstimation: This is an R-script for estimating the GBM parameters for houseprices using the Halifax dataset. Three methods of estimations are implemented, i.e. method of moments (MM), maximum likelihood estimation (MLE), and the generalized method of moments (GMM). 2. Relationship between files: The simulations in Matlab is based on the time series parameters computed with the files in R. The NNEG calculations with the Excel file ArmaEgarch_plus_GBM_excelversion.xlsm make use of the simulated values in the R-timeseries_fitting_diagnostics files. 3. Additional related data collected that was not included in the current data package: No 4. Are there multiple versions of the dataset? No If yes, list versions: Name of file that was updated: i. Why was the file updated? ii. When was the file updated? Name of file that was updated: i. Why was the file updated? ii. When was the file updated? -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: 2. Methods for processing the data: Model calibrations on Nationwide houseprice index are based on the non-seasonal average house prices 3. Instrument- or software-specific information needed to interpret the data: Microsoft Excel, Matlab, and R 4. Standards and calibration information, if appropriate: Maximum Likelihood Estimation, Method of Moments Estimation, GMM estimation, conditional Esscher martingale measure, ARMA-EGARCH, ARMA-GJR, GBM 5. Environmental/experimental conditions: inputs taken from Legal & General and ERC websites as of Autumn 2018. 6. Describe any quality-assurance procedures performed on the data: Cross-checking between PI and RA. 7. People involved with sample collection, processing, analysis and/or submission: Radu Tunaru and Enoch Quaye, review group. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: hpimonthly ----------------------------------------- 1. Number of variables: 2 2. Number of cases/rows: 333 3. Variable List A. Name: Date Description: Date of recording houseprice index B. Name: Price Description: Monthly Nationwide average houseprice series for the given date. (1991-2018) 4. Missing data codes: N/A 5. Specialized formats of other abbreviations used: N/A ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: hpiquarterly ----------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 265 3. Variable List A. Name: Date Description: Date of recording houseprice index B. Name: Index Description: Quarterly Nationwide average houseprice index for the given date. (1952-2018) C. Name: Price Description: Quarterly Nationwide average houseprice series for the given date. (1952-2018) 4. Missing data codes: N/A 5. Specialized formats of other abbreviations used: N/A ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: Halifax-Dataset ----------------------------------------- 1. Number of variables: 2 2. Number of cases/rows: 383 3. Variable List A. Name: Date Description: Date of recording houseprice index series B. Name: Price Description: Monthly Halifax houseprice return series for the given date. (1983-2014) 4. Missing data codes: N/A 5. Specialized formats of other abbreviations used: N/A