scholarly journals Reverse Mortgage Risks. Time Evolution of VaR in Lump-Sum Solutions

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2043
Author(s):  
Iván de la Fuente ◽  
Eliseo Navarro ◽  
Gregorio Serna
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
House Price ◽  
Policy Decision ◽  
Simulation Method ◽  
Capital Requirements ◽  
Reverse Mortgage ◽  
Female Population ◽  
Lump Sum ◽  
Reverse Mortgages

In this study, we analyzed the risk faced by the reverse mortgage provider in the case of the lump-sum solution, which is increasingly becoming one of the most popular types of reverse mortgages. The risk faced by the mortgage provider was estimated by means of a value at risk (VaR) procedure that involves a Monte Carlo simulation method and an ARMA-EGARCH assumption for modeling house price returns in the United Kingdom from 1952 to 2019. The results showed that the reverse mortgage provider faced higher risk and consequently needed to allocate more funds to meet its regulatory capital requirements in the case of relatively young borrowers, especially when they reached their life expectancy and had high roll-up rates. The risk was even higher in the case of the female population. Furthermore, care must be taken when the rental yield rate is higher than the risk-free rate, as is currently the case, as the value of the no-negative-equity guarantee (NNEG) is relatively high and results in higher value at risk (VaR) and expected shortfall (ES) values. These results have important implications in terms of policy decision making when determining the countercyclical buffer for reverse mortgages in Basel III, as well as from a managerial perspective when determining the economic capital needed to support the risk taken by the lender.

Risk Analysis for Reverse Mortgages with Different Payout Designs

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Daniel Cho ◽  
Katja Hanewald ◽  
Michael Sherris
Keyword(s):  
House Price ◽  
Cash Flows ◽  
Interest Rate Risk ◽  
Reverse Mortgage ◽  
Vector Autoregressive ◽  
Discount Factors ◽  
Lump Sum ◽  
Stochastic Framework ◽  
Reverse Mortgages ◽  
Income Stream

AbstractWe analyze the risk and profitability of reverse mortgages with lump-sum or income stream payments from the lender’s perspective. Reverse mortgage cash flows and loan balances are modeled in a multi-period stochastic framework that allows for house price risk, interest rate risk and risk of delayed loan termination. A vector autoregressive (VAR) model is used to simulate economic scenarios and to derive stochastic discount factors for pricing the no negative equity guarantee embedded in reverse mortgage contracts. Our results show that lump-sum reverse mortgages are more profitable and require less risk-based capital than income stream reverse mortgages, which explains why this product design dominates in most markets. The loan-to-value ratio, the borrower’s age, mortality improvements and the lender’s financing structure are shown to be important drivers of the profitability and riskiness of reverse mortgages, but changes in these parameters do not change the main conclusions.

scholarly journals Multivariate Risk-Neutral Pricing of Reverse Mortgages under the Bayesian Framework

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Jackie Li ◽  
Atsuyuki Kogure ◽  
Jia Liu
Keyword(s):  
House Price ◽  
Interest Rate Risk ◽  
Price Risk ◽  
Multivariate Approach ◽  
Reverse Mortgage ◽  
Principle Of Maximum Entropy ◽  
Multivariate Risk ◽  
Reverse Mortgages ◽  
Principle Of Maximum ◽  
Financial Environment

In this paper, we suggest a Bayesian multivariate approach for pricing a reverse mortgage, allowing for house price risk, interest rate risk and longevity risk. We adopt the principle of maximum entropy in risk-neutralisation of these three risk components simultaneously. Our numerical results based on Australian data suggest that a reverse mortgage would be financially sustainable under the current financial environment and the model settings and assumptions.

scholarly journals THE VAR AT RISK

2010 ◽  
Vol 13 (04) ◽  
pp. 503-506 ◽  
Author(s):  
ALFRED GALICHON
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Capital Requirements ◽  
Regulatory Capital ◽  
Regulatory Arbitrage ◽  
Risk Budgeting ◽  
Holding Company

I show that the structure of the firm is not neutral with respect to regulatory capital budgeted under rules which are based on the Value-at-Risk. Indeed, when a holding company has the liberty to divide its risk into as many subsidiaries as needed, and when the subsidiaries are subject to capital requirements according to the Value-at-Risk budgeting rule, then there is an optimal way to divide risk which is such that the total amount of capital to be budgeted by the shareholder is zero. This result may lead to regulatory arbitrage by some firms.

scholarly journals Market Risk Measurement

2020 ◽  
Author(s):  
Emese Lazar ◽  
Ning Zhang
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Preliminary Analysis ◽  
Risk Measures ◽  
Market Risk ◽  
Capital Requirements ◽  
Risk Measurement ◽  
Regulatory Framework ◽  
Bank Capital ◽  
Bank Capital Requirements

This chapter presents a preliminary analysis on how some market risk measures dramatically increased during the COVID-19 pandemic, with measures computed over longer horizons experiencing more pronounced effects. We provide examples when regulatory market risk measurement proved to be suboptimal, overestimating risk. A further issue was the large number of Value-at-Risk ‘exceptions’ during the first few months of the crisis, which normally leads to overinflated bank capital requirements. The current regulatory framework should address these problems by suggesting improvements to the calculation of risk measures and/or by modifying the rules which determine capital requirements to make them appropriate and realistic in crisis situations.

scholarly journals A stochastic implementation of the APCI model for mortality projections

2019 ◽  
Vol 24 ◽  
Author(s):  
S. J. Richards ◽  
I. D. Currie ◽  
T. Kleinow ◽  
G. P. Ritchie
Keyword(s):  
At Risk ◽  
Stochastic Model ◽  
Value At Risk ◽  
Capital Requirements ◽  
Mortality Forecasting ◽  
Forecasting Models ◽  
Mortality Projections ◽  
Model Fits

AbstractThe Age-Period-Cohort-Improvement (APCI) model is a new addition to the canon of mortality forecasting models. It was introduced by Continuous Mortality Investigation as a means of parameterising a deterministic targeting model for forecasting, but this paper shows how it can be implemented as a fully stochastic model. We demonstrate a number of interesting features about the APCI model, including which parameters to smooth and how much better the model fits to the data compared to some other, related models. However, this better fit also sometimes results in higher value-at-risk (VaR)-style capital requirements for insurers, and we explore why this is by looking at the density of the VaR simulations.

scholarly journals Evaluation of the Reverse Mortgage Option in Korea: A Long Straddle Perspective

2020 ◽  
Vol 8 (3) ◽  
pp. 55
Author(s):  
Kyung Jin Choi ◽  
Byungkwon Lim ◽  
Jaehwan Park
Keyword(s):  
Option Pricing ◽  
House Price ◽  
Analytical Models ◽  
Call Option ◽  
Option Value ◽  
Pricing Model ◽  
Reverse Mortgage ◽  
Option Pricing Model ◽  
Reverse Mortgages ◽  
Key Variables

This study explored the option value embedded in a reverse mortgage in Korea through an empirical analysis, using the Black–Scholes option-pricing model. The value of a reverse mortgage is affected by the variation in house prices. However, older homeowners using reverse mortgages are able to choose this option due to the unique characteristics of reverse mortgages, such as non-recourse clauses or being able to redeem the loan. This paper found the following results. First, the call option value is 5.8% of the house price at the age of 60, under the assumption of a KRW three hundred million house value, while the put option value is only 2.0%. Contrary to what it is at sixty years of age, only the call option value will remain when the homeowner reaches the age of 80. Second, this article analyzed the sensitivity of the key variables of real-option analytical models, such as the change of the exercise price, the change of the risk-free rate, volatility, and maturity, on the option value of a reverse mortgage. The sensitivity results of the key variables supported economic rationales for the option pricing model.

PENGUKURAN RISIKO PORTFOLIO DENGAN MENGGUNAKAN METODE VALUE AT RISK (VAR)

2013 ◽  
Author(s):  
Fajri Adrianto ◽  
Laela Susdiani
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Measurement Method ◽  
Simulation Method ◽  
Risk Measurement ◽  
Portfolio Risk ◽  
Historical Simulation ◽  
Stock Investment ◽  
Risk Investment

Value at Risk (VAR) is a risk measurement method that use in risk investment calculation. VAR shows risk in nominal. This research calculate risk portfolio of stock using VAR method and measure whether VAR value overvalued or underestimated. Using historical simulation method is found VAR value tend to decrease when stock investment consist more stocks in the portfolio. Risk investment calculation consistent with standar devistion as risk measurement, which the more investment diversified the less the risk in the investment. Then, using backtesting reveal that VAR tend too high in portfolio consisting small number of stocks. VAR value can accepted in the portfolio that consist many stocks or the more investment diversified the more accurate VAR value as risk measurement.

An Improved Historical Simulation Method to Estimate the Amount of Refined Oil Retail Value at Risk VaR

2013 ◽  
Vol 734-737 ◽  
pp. 1711-1718
Author(s):  
Yong Tao Wan ◽  
Zhi Gang Zhang ◽  
Lu Tao Zhao
Keyword(s):  
At Risk ◽  
Crude Oil ◽  
Value At Risk ◽  
Financial Risk ◽  
Rapid Development ◽  
Simulation Method ◽  
Support Vector ◽  
Refined Oil ◽  
Historical Simulation ◽  
Oil Market

The international crude oil market is complicated in itself and with the rapid development of China in recent years, the dramatic changes of the international crude oil market have brought some risk to the security of Chinas oil market and the economic development of China. Value at risk (VaR), an effective measurement of financial risk, can be used to assess the risk of refined oil retail sales as well. However, VaR, as a model that can be applied to complicated nonlinear data, has not yet been widely researched. Therefore, an improved Historical Simulation Approach, historical stimulation of genetic algorithm to parameters selection of support vector machine, HSGA-SVMF, in this paper, is proposed, which is based on an approach the historical simulation with ARMA forecasts, HSAF. By comparing it with the HSAF and HSGA-SVMF approach, this paper gives evidence to show that HSGA-SVMF has a more effective forecasting power in the field of amount of refined oil.

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