Will Solvency II Market Risk Requirements Bite? The Impact of Solvency II on Insurers’ Asset Allocation

2012 ◽  
Vol 38 (2) ◽  
pp. 250-273 ◽  
Author(s):  
Dirk Höring
Keyword(s):  
Asset Allocation ◽  
Market Risk ◽  
Solvency Ii ◽  
The Impact

scholarly journals Measuring Market Risk of Commercial Banks Implementing VaR with Historical Simulation Approach

2021 ◽  
pp. 79-99
Author(s):  
Minhaz-Ul Haq
Keyword(s):  
Stock Prices ◽  
Commercial Banks ◽  
Value At Risk ◽  
First Generation ◽  
Market Risk ◽  
Jel Classification ◽  
Simulation Approach ◽  
Share Prices ◽  
Historical Simulation ◽  
The Impact

This paper attempts to picture the impact of the market risk of ten commercial banks located in Bangladesh with the help of a non-parametric model known as the Historical Simulation Approach over the course of eight years. These banks' daily stock prices were used as inputs and analyzed in Microsoft Excel by means of Percentile and LN function. The study revealed market risk exposure as third, second-and first-generation banks from the least to the highest. It also pointed out the ups and downs of these banks' share prices in the selected period. Further analysis showed the portfolio VaR estimation for different time intervals. JEL classification numbers: G32. Keywords: Value-at-risk, Historical Simulation, Market Risk, Confidence Interval.

Herding and Delegated Portfolio Management: The Impact of Relative Performance Evaluation on Asset Allocation

2011 ◽  
Vol 01 (02) ◽  
pp. 265-292 ◽  
Author(s):  
Ernst Maug ◽  
Narayan Naik
Keyword(s):  
Performance Evaluation ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Relative Performance ◽  
Optimal Contracts ◽  
Fund Managers ◽  
Portfolio Managers ◽  
Delegated Portfolio Management ◽  
The Impact ◽  
Allocation Decisions

This paper investigates the effect of fund managers' performance evaluation on their asset allocation decisions. We derive optimal contracts for delegated portfolio management and show that they always contain relative performance elements. We then show that this biases fund managers to deviate from return-maximizing portfolio allocations and follow those of their benchmark (herding). In many cases, the trustees of the fund who employ the fund manager prefer such a policy. We also show that fund managers in some situations ignore their own superior information and "go with the flow" in order to reduce deviations from their benchmark. We conclude that incentive provisions for portfolio managers are an important factor in their asset allocation decisions.

Impact of volatility and equity market uncertainty on herd behaviour: evidence from UK REITs

2018 ◽  
Vol 11 (2) ◽  
pp. 169-186 ◽  
Author(s):  
Omokolade Akinsomi ◽  
Yener Coskun ◽  
Rangan Gupta ◽  
Chi Keung Marco Lau
Keyword(s):  
Asset Allocation ◽  
Regime Switching ◽  
Stock Exchange ◽  
Equity Market ◽  
Market Uncertainty ◽  
Content Type ◽  
Regime Switching Model ◽  
Switching Model ◽  
Market Sentiment ◽  
The Impact

PurposeThis paper aims to examine herding behaviour among investors and traders in UK-listed Real Estate Investment Trusts (REITs) within three market regimes (low, high and extreme volatility periods) from the period June 2004 to April 2016.Design/methodology/approachObservations of investors in 36 REITs that trade on the London Stock Exchange as at April 2016 were used to analyse herding behaviour among investors and traders of shares of UK REITs, using a Markov regime-switching model.FindingsAlthough a static herding model rejects the existence of herding in REITs markets, estimates from the regime-switching model reveal substantial evidence of herding behaviour within the low volatility regime. Most interestingly, the authors observed a shift from anti-herding behaviour within the high volatility regime to herding behaviour within the low volatility regime, with this having been caused by the FTSE 100 Volatility Index (UK VIX).Originality/valueThe results have various implications for decisions regarding asset allocation, diversification and value management within UK REITs. Market participants and analysts may consider that collective movements and market sentiment/psychology are determinative factors of risk-return in UK REITs. In addition, general uncertainty in the equity market, proxied by the impact of the UK VIX, may also provide a signal for increasing herding-related risks among UK REITs.

scholarly journals Mortality data reliability in an internal model

2020 ◽  
Vol 14 (2) ◽  
pp. 420-444
Author(s):  
Fabrice Balland ◽  
Alexandre Boumezoued ◽  
Laurent Devineau ◽  
Marine Habart ◽  
Tom Popa
Keyword(s):  
Data Quality ◽  
Internal Model ◽  
Mortality Rates ◽  
Solvency Ii ◽  
West Germany ◽  
Mortality Data ◽  
Longevity Risk ◽  
Capital Requirement ◽  
Stochastic Mortality ◽  
The Impact

AbstractIn this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database show particularly low and high mortality rates, respectively. To provide corrected tables for the three countries of interest here (France, Italy and West Germany), we use the approach developed by Boumezoued for countries for which the method applies (France and Italy) and provide an extension of the method for West Germany as monthly fertility histories are not sufficient to cover the generations of interest. These mortality tables are crucial inputs to stochastic mortality models forecasting future scenarios, from which the extreme 0.5% longevity improvement can be extracted, allowing for the calculation of the solvency capital requirement. More precisely, to assess the impact of such anomalies in the Solvency II framework, we use a simplified internal model based on three usual stochastic models to project mortality rates in the future combined with a closure table methodology for older ages. Correcting this bias obviously improves the data quality of the mortality inputs, which is of paramount importance today, and slightly decreases the capital requirement. Overall, the longevity risk assessment remains stable, as well as the selection of the stochastic mortality model. As a collateral gain of this data quality improvement, the more regular estimated parameters allow for new insights and a refined assessment regarding longevity risk.

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