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 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.

Scenario-based measurement of interest rate risks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sebastian Schlütter
Keyword(s):  
At Risk ◽  
Interest Rate ◽  
Stochastic Model ◽  
Interest Rates ◽  
Lower Bounds ◽  
Value At Risk ◽  
Yield Curve ◽  
Principal Component ◽  
Insurance Companies ◽  
Content Type

PurposeThis paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.Design/methodology/approachThe authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.FindingsThe backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.Research limitations/implicationsThe paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.Practical implicationsThe authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.Originality/valueThe authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

Value-at-risk and related measures for the Bitcoin

2018 ◽  
Vol 19 (2) ◽  
pp. 127-136 ◽  
Author(s):  
Stavros Stavroyiannis
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Capital Allocation ◽  
Capital Requirements ◽  
Spot Price ◽  
Content Type ◽  
Type Iv ◽  
Pearson Type ◽  
Standard And Poor's

Purpose The purpose of this paper is to examine the value-at-risk and related measures for the Bitcoin and to compare the findings with Standard and Poor’s SP500 Index, and the gold spot price time series. Design/methodology/approach A GJR-GARCH model has been implemented, in which the residuals follow the standardized Pearson type-IV distribution. A large variety of value-at-risk measures and backtesting criteria are implemented. Findings Bitcoin is a highly volatile currency violating the value-at-risk measures more than the other assets. With respect to the Basel Committee on Banking Supervision Accords, a Bitcoin investor is subjected to higher capital requirements and capital allocation ratio. Practical implications The risk of an investor holding Bitcoins is measured and quantified via the regulatory framework practices. Originality/value This paper is the first comprehensive approach to the risk properties of Bitcoin.

scholarly journals Avaliação de Modelos de Cálculo de Exigência de Capital para Risco Cambial

2005 ◽  
Vol 3 (2) ◽  
pp. 223
Author(s):  
Claudio H. da S. Barbedo ◽  
Gustavo S. Araújo ◽  
João Maurício S. Moreira ◽  
Ricardo S. Maia Clemente
Keyword(s):  
At Risk ◽  
Financial Institutions ◽  
Value At Risk ◽  
Hybrid Approach ◽  
Capital Allocation ◽  
Capital Requirements ◽  
Internal Models ◽  
Capital Requirement ◽  
Brazilian Legislation ◽  
Historical Model

This paper examines capital requirement for financial institutions in order to cover market risk stemming from exposure to foreign currencies. The models examined belong to two groups according to the approach involved: standardized and internal models. In the first group, we study the Basel model and the model adopted by the Brazilian legislation. In the second group, we consider the models based on the concept of value at risk (VaR). We analyze the single and the double-window historical model, the exponential smoothing model (EWMA) and a hybrid approach that combines features of both models. The results suggest that the Basel model is inadequate to the Brazilian market, exhibiting a large number of exceptions. The model of the Brazilian legislation has no exceptions, though generating higher capital requirements than other internal models based on VaR. In general, VaR-based models perform better and result in less capital allocation than the standardized approach model applied in Brazil.

Comparison of market risk models with respect to suggested changes of Basel Accord

2014 ◽  
Vol 64 (Supplement-2) ◽  
pp. 257-274
Author(s):  
Eliška Stiborová ◽  
Barbora Sznapková ◽  
Tomáš Tichý
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Market Risk ◽  
Capital Requirements ◽  
Internal Models ◽  
Conditional Value At Risk ◽  
Risk Capital ◽  
Basel Accord ◽  
Capital Charge

The market risk capital charge of financial institutions has been mostly calculated by internal models based on integrated Value at Risk (VaR) approach, since the introduction of the Amendment to Basel Accord in 1996. The internal models should fulfil several quantitative and qualitative criteria. Besides others, it is the so called backtesting procedure, which was one of the main reasons why the alternative approach to market risk estimation — conditional Value at Risk or Expected Shortfall (ES) — were not applicable for the purpose of capital charge calculation. However, it is supposed that this approach will be incorporated into Basel III. In this paper we provide an extensive simulation study using various sets of market data to show potential impact of ES on capital requirements.

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.

Expected shortfall or median shortfall

2014 ◽  
Vol 01 (01) ◽  
pp. 1450007 ◽  
Author(s):  
Steven Kou ◽  
Xianhua Peng
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Measure ◽  
Model Misspecification ◽  
Capital Requirements ◽  
Expected Shortfall ◽  
Banking Supervision ◽  
Tail Risk ◽  
Basel Committee ◽  
Distributional Robustness

In a recent consultative document, the Basel Committee on Banking Supervision suggests replacing Value-at-Risk (VaR) by expected shortfall (ES) for setting capital requirements for banks' trading books because ES better captures tail risk than VaR. However, besides ES, another risk measure called median shortfall (MS) also captures tail risk by taking into account both the size and likelihood of losses. We argue that MS is a better alternative than ES as a risk measure for setting capital requirements because: (i) MS is elicitable but ES is not; (ii) MS has distributional robustness with respect to model misspecification but ES does not; (iii) MS is easy to implement but ES is not.

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