nep-rmg New Economics Papers
on Risk Management
Issue of 2016‒09‒04
eight papers chosen by

  1. Treatment of Market Risks under Solvency II and its Market Implications By Benjamin Lorent
  2. Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach By Arreola Hernandez, Jose; Hammoudeh, Shawkat; Nguyen, Duc Khuong; Al Janabi, Mazin A. M.; Reboredo, Juan Carlos
  3. Risk Reallocation in OTC Derivatives Networks By Emil Siriwardane; Bernard Herskovic; Andrea Eisfeldt
  4. A Composite Indicator of Systemic Stress (CISS) for Colombia. By Wilmar Cabrera; Jorge Hurtado; Miguel Morales; Juan Sebastián Rojas
  5. Benchmarking Operational Risk Models By Curti, Filippo; Ergen, Ibrahim; Le, Minh; Migueis, Marco; Stewart, Rob T.
  6. Score-Driven Systemic Risk Signaling for European Sovereign Bond Yields and CDS Spreads By Rutger-Jan Lange; Andre Lucas; Arjen H. Siegmann
  7. Investor-Stock Decoupling in Mutual Funds By Ferreira, Miguel; Massa, Massimo; Matos, Pedro Pinto
  8. Expected skewness and momentum By Regele, Tobias; Weber, Martin

  1. By: Benjamin Lorent
    Abstract: The three chapters all address solvency regulation issues, with a focus on market risks under the Solvency II framework. Chapter 1 deals with “high-level” aspects of Solvency II as main principles and the general structure. Chapters 2 and 3 will be devoted to quantitative issues. Chapter 1 describes the main evolutions that led to the development of Solvency II. The insurance sector has dramatically evolved during the last two decades. Among others developments, we stress the new risks faced by the sector as natural catastrophes, changing demographics or market risks. Insurers become international companies, investing almost 10 trillion € of assets in Europe at the end of 2014 and being increasingly intertwined with banks and other financial sectors. Financial innovation and the refinement of risk management techniques and models developed by companies have gained momentum among the major European insurance companies. Have these evolutions changed the needs for the supervisory of insurance companies? The economic foundation for regulation is based on the presence of market failures, including severe asymmetric information problems and principal-agent conflicts. Insurance consumers, particularly individuals and households, face significant challenges in judging the financial risk of insurers. But the importance of the insurance sector for financial stability has been increasing. A sound regulatory and supervisory system is necessary to maintain efficient, safe, fair and stable financial markets and promote growth and competition in the insurance sector. The difficult conditions experienced by the industry and the shortcomings of the previous regulatory and supervisory framework have forced regulators to take action to change the way in which they regulate insurance companies’ solvency. Recognizing the shortcomings of Solvency I, EU policy-makers undertook the Solvency II project. Solvency I was not consistently applied throughout EU as the directive allowed countries to implement insurance regulation in different ways. Moreover Solvency I did not consider risks fully or in detail. In life business, the major criticism was the lack of consideration of asset risks. Allowances for latest developments in risk management were also inadequate and companies could not use an internal model to calculate the solvency capital. Finally, the increasing presence of conglomerates and groups forced the insurance regulator to align some requirements with the banking regulation, Basel II/III. Due to the differences in their core business activities, banks and insurers regulators’ goal does not imply comparability of the overall capital charges. However, considering the asset side of the balance sheets, the investment portfolios of banks and insurers contain the same asset classes. In order to avoid regulatory arbitrage, the capital charges for the same amount and type of asset risk should be similar. Chapter 2 compares the main regulatory frameworks in Europe: Solvency II and the Swiss Solvency Test, SST, in Switzerland, with a focus on potential market implications. Both systems are quite advanced but some key differences need to be highlighted, including the treatment of assets, in particular sovereign bonds, the consideration of diversification or the risk measure applied. Solvency II uses a Value at Risk at 99.5% whereas the SST is based on a Tail Value at Risk at 99%. Our approach is both qualitative and quantitative. In particular, based on a numerical example, we aim at quantifying the level of regulatory capital prescribed by the standard models. The numerical analysis reveals large differences between capital charges assigned to the same asset class under Solvency II and the SST. Solvency II penalizes investment in stocks, mainly due to a lower diversification benefit under the standard formula. On the other hand the SST model requires a higher capital for bonds, primary due to a stringent risk measure and confidence level. The treatment of EU sovereign bonds under Solvency II is another area of concern as it does not require any capital for spread risk. The question arises to what extent an internal model leads to different capital requirements as compared to the SST and Solvency II models. Therefore we apply an internal approach based on Monte Carlo simulation to derive the necessary capital based on the Value at Risk at 99.5% (in line with the Solvency II standard model) and on the Tail Value at Risk at 99% (in line with the SST standard model). Internal models calculate capital requirements that more closely matches risks of insurers and promote a culture of risk management. To develop internal models, companies need incentives to properly manage their risks, i.e. decreasing capital requirements. One potential benefit of the standard model is that insurers who use it can be compared to one another, whereas internal models are by definition specific to individual insurers. One argument against the standard model is the possibility of some systemic risk. An unusual event in the capital or insurance market could encourage all insurers to take the exact same response, thereby causing a run in the market. The analysis shows that standard and internal models still display large discrepancies in their results, suggesting a long way ahead to achieve a harmonized view between the regulators and the insurance sector. The choice of a statistical model or the refinement of parameters are key concepts when setting up an internal model and appear to be critical in the Solvency Capital Requirement calculation. By calculating and comparing the market risk capital charges for a representative insurer under the Solvency II and the SST standard approach as well as an internal model, we are able to provide evidence that the regulatory framework might have an impact on asset portfolios. The main impacts would be a shift from long-term to shorter-term debt, an increase in the attractiveness of higher-rated corporate debt and government bonds, in particular EU sovereign bonds as the consequence of the special treatment under Solvency II, as well as low level of equity holdings. But it is unlikely that large-scale reallocations will happen in the short term, as transitional arrangements are likely to phase in the implementation of Solvency II over several years. The likely impact on assets portfolios could have also already been anticipating by insurers. Chapter 3 studies the effectiveness of the Solvency II reform to prevent the default probability faced by a life insurance company. The default risk leads to a consequence that policyholders might not get back their initial investment upon default of the insurance company. Therefore, policyholders are concerned with the issues like what probability the insurance company will become bankrupt and which amount they can expect to obtain after taking account of the default risk of the insurer. Starting from a theoretical life insurance company which sells a participation insurance policy containing only a savings component and a single premium inflow, we simulate a life insurance company on an eight-year time horizon. We focus only on market risks as there is no mortality risk attached to the insurance contract. Finally several policies and investment strategies will be analysed. The purpose of the chapter is to evaluate how Solvency II can prevent the company to collapse. The papers discussing Solvency II effectiveness are qualitative in nature. In particular there is little research on the accuracy of the standard formula with regard to the proclaimed ruin probability of 0.5% per year. To do so we compare the probability of default at maturity of the life insurance policy, i.e. if the company has to enough assets to pay what was promised to the policyholders, with the early probability of default forced by Solvency II based on standard and internal models. We have first to calculate the Solvency Capital Requirement as laid down in the directive. One crucial point is the evaluation of liabilities. To do so we use an approach recently applied by the insurance sector called Least-squares Monte Carlo (LSMC). The aim of Solvency II is to monitor insurers on an annual basis. The SCR level can then be interpreted as a regulatory barrier, consistent with a model developed by Grosen and Jørgensen (2002). Key drivers of the ruin probability at maturity include interest rate parameters, portfolio riskiness and investment strategies in bonds. The continuously decrease of interest rates creates a challenge for insurers, especially life insurers that suffer a double impact on their balance sheet: a valuation effect and a decreasing reinvestment returns of premiums and maturing bonds. The latter explain also the riskiness of rolling-bond strategies compared to duration matching strategies. By setting the confidence level to 99.5% per year, the regulator wants to ensure that the annual ruin probability equals to 0.5%. Since the SCR from our internal model equals the 0.5% quantile of the distribution, it exactly matches the targeted ruin probability. Our analysis reveals that the set-up and calibration of the Solvency II standard model are inadequate as the solvency capital derived by the standard formula overestimates the results of the internal model. This is mainly the consequence of an overestimated equity capital and a lower diversification benefit. The 0.5% proclaimed goal under Solvency II is not reached, being too conservative. One declared goal of the directive is to decrease the duration gap between assets and liabilities. Solvency II penalizes then rolling-bond strategies. The long-term feature of our policy should impact the level of regulatory capital. As Solvency II is based on a quantile measurement, we define the solvency capital using the default probability objective for different horizons. SCR is not systematically a decreasing function of the time horizon even if a decreasing form appears on long-term. This shows undoubtedly that a horizon effect exists in terms of measurement of solvency. As the standard model overestimated the internal model capital we expect a forced default probability higher than 0.5% under the Solvency II framework. The SCR barrier stops the company more often than it should be. This can be interpreted as one cost of regulation, i.e. closing down financially sound at maturity companies. The analysis of the evolution of default probabilities as a function of time horizon reveals that ruin probabilities at maturity lie always below the Solvency II objective. Furthermore the gap between the observed default at maturity and the Solvency II objective is increasing over time; the situation is even worse for longer-term insurance products. Finally stakeholders are more interested in their expected return than in the default probability. A cost of regulation defined as the difference between stakeholder’s returns with and without regulatory framework exists, particularly for shareholders.
    Keywords: insurance; regulation; solvency II; market risks
    Date: 2016–06–21
  2. By: Arreola Hernandez, Jose; Hammoudeh, Shawkat; Nguyen, Duc Khuong; Al Janabi, Mazin A. M.; Reboredo, Juan Carlos
    Abstract: We use regular vine (r-vine), canonical vine (c-vine) and drawable vine (d-vine) copulas to examine the dependence risk characteristics of three 20-stock portfolios from the retail, manufacturing and gold-mining equity sectors of the Australian market in periods before, during and after the 2008-2009 global financial crisis (GFC). Our results indicate that the retail portfolio is less risky than the manufacturing counterpart in the crisis period, while the gold-mining portfolio is less risky than both the retail and manufacturing sector portfolios. Both the retail and gold stocks display a higher propensity to yield positively skewed returns in the crisis periods, contrary to the manufacturing stocks. The r-vine is found to best capture the multivariate dependence structure of the stocks in the retail and gold-mining portfolios, while the d-vine does it for the manufacturing stock portfolio. These findings could be used to develop dependence risk and investment risk-adjusted strategies for investment, rebalancing and hedging which more adequately account for the downside risk in various market conditions.
    Keywords: vine copulas, risk analysis, dependence structure, retail and manufacturing stocks
    JEL: C32 C51 G11
    Date: 2014–12
  3. By: Emil Siriwardane (Harvard Business School); Bernard Herskovic (UCLA Anderson School of Management); Andrea Eisfeldt (UCLA Anderson School of Management)
    Abstract: Over-the-counter (OTC) derivatives markets are the key venue for quickly reallocating exposures to key risk factors such as interest rates, exchange rates, and credit amongst market participants. These markets are very large, and are characterized by a complex trading network with disperse prices. In this paper, we ask how the structure of the OTC derivatives trading network, the preferences and technologies of the participants, and the distribution of endowed exposures to the underlying risk factor, jointly determine the observed patterns of trade, post-trade exposures, and prices. We use detailed data from the DTCC to estimate the key parameters of our model. Finally, we use the model at estimated parameters to study comparative statics related to risk management and regulation.
    Date: 2016
  4. By: Wilmar Cabrera; Jorge Hurtado; Miguel Morales; Juan Sebastián Rojas
    Abstract: The most recent global financial crisis (2008-2009) highlighted the importance of systemic risk and promoted academic interest to develop a wide set of warning indicators, which are mechanisms to identify systemically important institutions and global systemic risk indexes. Using the methodology proposed by Holló et al. (2012), along with some considerations from Hakkio & Keeton (2009), this document comprises a Composite Indicator of Systemic Stress (CISS) for Colombia. The index takes into account several dimensions related to financial markets (credit institutions, housing market, external sector, money market and local bond market) and is constructed using portfolio theory, considering the contagion among dimensions. Results suggest the peak of the global financial crisis (September 2008) as the most important episode of systemic risk in Colombia between 2000-2014. Additionally, real activity seems to be adversely affected by an unexpected increase of the systemic risk index.
    Keywords: Systemic Risk, Risk Indicators, Financial Stability, Early-Warning-Indicators, Multivariate GARCH. Classification JEL:G12,G29,C51.
  5. By: Curti, Filippo; Ergen, Ibrahim; Le, Minh; Migueis, Marco; Stewart, Rob T.
    Abstract: The 2004 Basel II accord requires internationally active banks to hold regulatory capital for operational risk, and the Federal Reserve's Comprehensive Capital Analysis and Review (CCAR) requires banks to project operational risk losses under stressed scenarios. As a result, banks subject to these rules have measured and managed operational risk more rigorously. But some types of operational risk - particularly legal risk - are challenging to model because such exposures tend to be fat-tailed. Tail operational risk losses have significantly impacted banks' balance sheets and income statements, even post crisis. So, operational risk practitioners, bank analysts, and regulators must develop reasonable methods to assess the efficacy of operational risk models and associated equity financing. We believe benchmarks should be used extensively to justify model outputs, improve model stability, and maintain capital reasonableness. Since any individual benchmark can be misleading, we outline a set of principles for using benchmarks effectively and describe how these principles can be applied to operational risk models. Also, we provide some examples of the benchmarks that have been used by US regulators in assessing Advanced Measurement Approach (AMA) capital reasonableness and that can be used in CCAR to assess the reasonableness of operational risk loss projections. We believe no single model's output and no single benchmark offers a comprehensive view, but that practitioners, analysts, and regulators must use models combined with rigorous benchmarks to determine operational risk capital and assess its adequacy.
    Keywords: Banking Regulation; Benchmarking; Operational Risk; Risk Management
    JEL: G21 G28
    Date: 2016–03–02
  6. By: Rutger-Jan Lange (VU University Amsterdam, Erasmus University Rotterdam, the Netherlands); Andre Lucas (VU University Amsterdam, the Netherlands); Arjen H. Siegmann (VU University Amsterdam, the Netherlands)
    Abstract: We compute joint sovereign default probabilities as coincident systemic risk indicators. Instead of commonly used CDS spreads, we use government bond yield data which provide a longer data history. We show that for the more recent sample period 2008--2015, joint default probabilities based on CDS and bond yield data yield similar results. For the period 1987-2008, only the bond yield data can be used to shed light on European sovereign systemic stress. We also show that simple averages of rolling pairwise correlations do not always yield intuitive systemic risk indicators.
    Keywords: systemic risk; conditional default; credit default swaps; bond yields
    JEL: G01 G17 C32
    Date: 2016–08–29
  7. By: Ferreira, Miguel; Massa, Massimo; Matos, Pedro Pinto
    Abstract: We investigate whether mutual funds whose investors and stocks are decoupled (i.e., investor location does not coincide with that of the stock holdings) benefit from a natural hedge as they have fewer outflows during market downturns and fewer inflows during upturns. Using a sample of equity mutual funds from 26 countries, we find that funds with higher investor-stock decoupling exhibit higher performance and this is more pronounced during the 2007-2008 financial crisis. We also find that decoupling allows fund managers to take less risk, be more active, and tilt their portfolios toward smaller and less liquid stocks.
    Keywords: Fund flows; Limits to Arbitrage; Mutual funds; Performance; Risk Taking
    JEL: G20 G23
    Date: 2016–08
  8. By: Regele, Tobias; Weber, Martin
    Abstract: Motivated by the time-series insights of Daniel and Moskowitz (2016), we investigate the link between expected skewness and momentum in the cross-section. The alpha of skewness-enhanced (-weakened) momentum is about twice (half) as large as the traditional alpha. These findings are driven by the short leg. Portfolio sorts, Fama-MacBeth regressions, and the market reaction to earnings announcements suggest that expected skewness is an important determinant of momentum. Due to the simplicity of the approach, its economic magnitude, its existence among large stocks, and the success of risk management, the results are difficult to reconcile with the efficient market hypothesis.
    Keywords: behavioral finance; Market Efficiency; Momentum; return predictability; Skewness
    JEL: G12 G14
    Date: 2016–08

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