Risk assessment for uncertain cash flows: model ambiguity, discounting ambiguity, and the role of bubbles

We study the risk assessment of uncertain cash flows in terms of dynamic convex risk measures for processes as introduced in Cheridito et al. (Electron. J. Probab. 11(3):57–106, 2006). These risk measures take into account not only the amounts but also the timing of a cash flow. We discuss their robust representation in terms of suitably penalised probability measures on the optional σ-field. This yields an explicit analysis both of model and discounting ambiguity. We focus on supermartingale criteria for time consistency. In particular, we show how “bubbles” may appear in the dynamic penalisation, and how they cause a breakdown of asymptotic safety of the risk assessment procedure.     

Testing macroprudential stress tests: The risk of regulatory risk weights

We compare the capital shortfall measured by regulatory stress tests, to that of a benchmark methodology — the “V-Lab stress test” — that employs only publicly available market data. We find that when capital shortfalls are measured relative to risk-weighted assets, the ranking of financial institutions is not well correlated to the ranking of the V-Lab stress test, whereas rank correlations increase when required capitalization is a function of total assets. We show that the risk measures used in risk-weighted assets are cross-sectionally uncorrelated with market measures of risk, as they do not account for the “risk that risk will change.” Furthermore, the banks that appeared to be best capitalized relative to risk-weighted assets were no better than the rest when the European economy deteriorated into the sovereign debt crisis in 2011.     

Measuring potential market risk

We argue herein that there is a fundamental and an important difference between the market risk and the potential market risk in financial markets. We also argue that the spectrum of smooth Lyapunov exponents can be used in (λ,σ²)-analysis, which is a method to measure and monitor these risks. The reason is that these exponents focus on the stability properties (λ) of the stochastic dynamic system generating asset returns, while more traditional risk measures such as value-at-risk are concerned with the distribution of asset returns (σ²).     

Interval Estimation of Actuarial Risk Measures

Abstract

This article investigates performance of interval estimators of various actuarial risk measures. We consider the following risk measures: proportional hazards transform (PHT), Wang transform (WT), value-at-risk (VaR), and conditional tail expectation (CTE). Confidence intervals for these measures are constructed by applying nonparametric approaches (empirical and bootstrap), the strict parametric approach (based on the maximum likelihood estimators), and robust parametric procedures (based on trimmed means).

Using Monte Carlo simulations, we compare the average lengths and proportions of coverage (of the true measure) of the intervals under two data-generating scenarios: “clean” data and “contaminated” data. In the “clean” case, data sets are generated by the following (similar shape) parametric families: exponential, Pareto, and lognormal. Parameters of these distributions are selected so that all three families are equally risky with respect to a fixed risk measure. In the “contaminated” case, the “clean” data sets from these distributions are mixed with a small fraction of unusual observations (outliers). It is found that approximate knowledge of the underlying distribution combined with a sufficiently robust estimator (designed for that distribution) yields intervals with satisfactory performance under both scenarios.     

Managerial incentives to take asset risk

We argue that incentives to take equity risk (”equity incentives”) only partially capture incentives to take asset risk (“asset incentives”). This is because leverage, while central to the theory of risk-shifting, is not explicitly considered by equity incentives. Employing measures of asset incentives that account for leverage, we find that asset risk-taking incentives can be large compared to incentives to increase firm value. Stock holdings can induce substantial risk-taking incentives, contrary to the assumption that only stock options drive risk-taking. Finally, asset incentives help explain asset risk-taking of U.S. financial institutions before the 2007/08 crisis.     

Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices,commodities and exchange rates: Inter-day versus intra-day data

In order to provide reliable Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, this paper attempts to investigate whether an inter-day or an intra-day model provides accurate predictions. We investigate the performance of inter-day and intra-day volatility models by estimating the AR(1)-GARCH(1,1)-skT and the AR(1)-HAR-RV-skT frameworks, respectively. This paper is based on the recommendations of the Basel Committee on Banking Supervision. Regarding the forecasting performances, the exploitation of intra-day information does not appear to improve the accuracy of the VaR and ES forecasts for the 10-steps-ahead and 20-steps-ahead for the 95%, 97.5% and 99% significance levels. On the contrary, the GARCH specification, based on the inter-day information set, is the superior model for forecasting the multiple-days-ahead VaR and ES measurements. The intra-day volatility model is not as appropriate as it was expected to be for each of the different asset classes; stock indices, commodities and exchange rates.The multi-period VaR and ES forecasts are estimated for a range of datasets (stock indices, commodities, foreign exchange rates) in order to provide risk managers and financial institutions with information relating the performance of the inter-day and intra-day volatility models across various markets. The inter-day specification predicts VaR and ES measures adequately at a 95% confidence level. Regarding the 97.5% confidence level that has been recently proposed in the revised 2013 version of Basel III, the GARCH-skT specification provides accurate forecasts of the risk measures for stock indices and exchange rates, but not for commodities (that is Silver and Gold). In the case of the 99% confidence level, we do not achieve sufficiently accurate VaR and ES forecasts for all the assets.     

Forecasting oil price realized volatility using information channels from other asset classes

Motivated from Ross (1989) who maintains that asset volatilities are synonymous to the information flow, we claim that cross-market volatility transmission effects are synonymous to cross-market information flows or “information channels” from one market to another. Based on this assertion we assess whether cross-market volatility flows contain important information that can improve the accuracy of oil price realized volatility forecasting. We concentrate on realized volatilities derived from the intra-day prices of the Brent crude oil and four different asset classes (Stocks, Forex, Commodities and Macro), which represent the different “information channels” by which oil price volatility is impacted from. We employ a HAR framework and estimate forecasts for 1-day to 66-days ahead. Our findings provide strong evidence that the use of the different “information channels” enhances the predictive accuracy of oil price realized volatility at all forecasting horizons. Numerous forecasting evaluation tests and alternative model specifications confirm the robustness of our results.     

The Effects of Ambiguous Information on Initial and Subsequent IPO Returns

Newly public companies must disclose significant risk factors in the offering prospectus. These disclosures are examples of “soft” or ambiguous information. Ambiguity models predict that investors will alter their portfolio weights and react to subsequent signals about such information. We test for these effects in a sample of 1,398 initial public offerings (IPOs) using word count ratios between soft and hard information as measures of ambiguity. We find a significant relationship between the soft information on risk and both initial and ex post measures of returns. These results support the view that soft information embeds ambiguity and that it influences investors’ portfolio choices.     

An analysis of the literature on systemic financial risk: A survey

This article presents an analysis of the literature on systemic financial risk. To that end, we analyze and classify 266 articles that were published no later than September 2016 in the databases Scopus and Web of Knowledge; these articles were identified using the keywords “systemic risk”, “financial stability”, “financial”, “measure”, “indicator”, and “index”. They were evaluated based on 10 categories, namely, type of study, type of approach, object of study, method, spatial scope, temporal scope, context, focus, type of data used, and results. The analysis and classification of this literature made it possible to identify the remaining gaps in the literature on systemic risk; this contributes to a future research agenda on the topic. Moreover, the most influential articles in this field of research and the articles that compose the mainstream research on systemic financial risk were identified.     

Comment on “A note on the returns from minimum variance investing”

In Scherer (2011) the author, analyzing minimum variance investing, shows that “the minimum variance tends to hold low beta and low residual risk stocks.”The conclusion that low residual risk assets and assets with a low β (below one) create a positive portfolio weight in the minimum variance portfolio does not prove the paper's “conjecture that the minimum variance portfolio is likely to pick up low beta and low residual risk stocks.”     

Identity Theft and Consumer Payment Choice: Does Security Really Matter?

In their paper “Spectral Risk Measures: Properties and Limitations”, Dowd et al. (J Financ Serv Res 341:61–75, 2008) introduce exponential and power spectral risk measures as subclasses of spectral risk measures (SRMs) to the literature, and claim that they are subject to three serious limitations: First, for these subclasses, the spectral risk may be counterintuitively decreasing when the user’s risk aversion is increasing. Second, these subclasses, and power SRMs in particular, become completely insensitive to market volatility when the respective parameters of risk aversion tend to their lower and upper boundaries. Third, exponential SRMs exhibit constant absolute risk aversion, while constant relative risk aversion better meets the empirical evidence. Consequently, “users of spectral risk measures must be careful to select utility functions that fit the features of the particular problems they are dealing with, and should be especially careful when using power SRMs.” (p. 61). In this comment, we show that the findings of Dowd et al. (J Financ Serv Res 341:61–75, 2008) suffer from misinterpretations and wrong conclusions.     

Accounting information,disclosure, and expected utility: Do investors really abhor uncertainty?

Investors are said to “abhor uncertainty,” but if there were no uncertainty they could earn only the risk‐free rate. A fundamental result in the analytical accounting literature shows that investors buying into a CARA‐normal CAPM market pay lower asset prices, gain higher ex‐ante expected returns, and obtain higher expected utility, when the market payoff has higher variance. New investors obtain similar “welfare” gains from risk under a log/power utility CAPM. These results do not imply that investors “abhor information.” To realize investors' ex‐ante expectations, the subjective probability distributions representing market expectations must be accurate. Greater payoff risk can add to investors' expected utility, but higher ex‐post(realized) utility comes from better information and more accurate ex‐ante expectations. An important implication for accounting is that greater disclosure can have the simultaneous effects of (i) exposing more fully or perceptibly firms' payoff uncertainty, thereby increasing new investors' expected utility, and (ii) improving market estimates of firms' payoff parameters (means, variances, covariances), thereby giving investors a better chance of realizing their expectations. Paradoxically, better information can be valuable to new investors by exposing more fully and more accurately the risk in firms' business operations and results–new investors maximizing expected utility want both more risk and better information.     

Management earnings forecasts and other forward-looking statements

We identify forward-looking statements (FLS) in firms’ disclosures to distinguish between “forecast-like” (quantitative statements about earnings) and “other”, or non-forecast-like, FLS.  We show that, like earnings forecasts, other FLS generate significant investor and analyst responses. Unlike earnings forecasts, other FLS are issued more frequently when uncertainty is higher. We then show that earnings-related FLS are more sensitive to uncertainty than quantitative statements, suggesting that managers are more likely to alter the content than the form of FLS when uncertainty is higher.  Our study indicates that incorporating other FLS into empirical measures provides a more comprehensive proxy for firms’ voluntary disclosures.     

Net value created: measuring a non-life insurer’s performance

The measure Net Value Created (NVC) captures periodic deviations of planned for and realized net present values at a specific reporting date. Therefore, NVC provides control-related signals about the company’s performance additional to economic value added/residual income measures. In this paper we adopt the NVC to the Flow to Equity approach, commonly used in non-life insurance company valuation. In a multi-period context the NVC allows for an update of information over time regarding planned for and realized values and, it further allows for a separation of value contributions of the main insurer’s business units and value contributions due to a change in cost of capital. Thus, NVC is useful for value-based performance measurement in the retrospect and for strategic (investment-) decision support in the prospect for non-life insurance companies.     

Close form pricing formulas for Coupon Cancellable CoCos

Contingent Convertibles (“CoCos”) are contingent capital instruments which convert into shares, or have a principal write down, if a trigger event takes place. CoCos exhibit the undesirable so-called death-spiral effect: by actively hedging the equity risk, investors can (unintentionally) force the conversion by making the share price deteriorate and eventually trigger the conversion.In this paper we introduce and analyse Coupon Cancellable CoCos (“CoCa CoCos”), a new type of CoCo where coupons can be cancelled during the lifetime of the note. We provide closed-form pricing formulas for CoCa CoCos, we study the impact of coupon cancellations in the price of the bond and we show that death-spiral effect is reduced.     

Lessons from the Financial Crisis on Risk and Capital Management: The Case of Insurance Companies

This article proposes that risk management be viewed as an integral part of the corporate value‐creation process— one in which the concept of economic capital can provide companies with the financial cushion and confidence to carry out their strategic plans. Using the case of insurance and reinsurance companies, the authors discuss three main ways that the integration of risk and capital management creates value:

• 1 strengthening solvency (by limiting the probability of financial distress);
• 2 increasing prospects for profitable growth (by preserving access to capital during post‐loss periods); and
• 3 improving transparency (by increasing the “information content” or “signaling power” of reported earnings).

Insurers can manage solvency risk by using Enterprise Risk Management (ERM) models to limit the probability of financial distress to levels consistent with the firm's specified risk tolerance. While ERM models are effective in managing “known” risks, we discuss three practices widely used in the insurance industry to manage “unknown” and “unknowable” risks using the logic of real options—slack, mutualization, and incomplete contracts. Second, risk management can create value by securing sources of capital that, like contingent capital, can be used to fund profitable growth opportunities that tend to arise in periods following large losses. Finally, the authors argue that risk management can raise the confidence of investors in their estimates of future growth by removing the “noise” in earnings that comes from bearing non‐core risks, thereby making current earnings a more reliable guide to future earnings. In support of this possibility, the authors provide evidence showing that, for a given level of reported return on equity (ROE), (re)insurers with more stable ROEs have higher price‐to‐book ratios, suggesting investors' willingness to pay a premium for the stability provided by risk management.     

The flight from maturity

Why did the failure of Lehman Brothers make the financial crisis dramatically worse? Our answer is that the financial crisis was a process of a build-up of risk during the crisis prior to the Lehman failure. During the crisis market participants tried to preserve an option to withdraw by shortening maturities — the “flight from maturity”. We show that the flight from maturity was manifested in a steepening of the term structures of spreads in money markets. With increasingly short maturities, lenders created the possibility of fast exit. The failure of Lehman Brothers was the tipping point of this build-up of systemic fragility. A crisis is a dynamic process in which “tail risk” is endogenous.     

An Empirical Investigation of the Influence of Qualitative Risk Factors on Canadian Auditors’ Determination of Performance Materiality*

This paper presents the results of a field experiment that tested the effects of various qualitative risk factors suggested by auditing standards and prior literature on practicing Canadian auditors’ estimates of performance materiality, a concept introduced by Canadian Auditing Standard (CAS) 320, in the audit of specific accounts in a financial statement audit. Ninety‐four practicing auditors responded to four scenarios and, based on “good” and “bad” versions of six qualitative risk factors, revised or not, as they deemed appropriate, initially established performance materiality for the audit of four different transaction streams/account balances. For all four scenarios, on average, the auditors revised, to a statistically significant degree, performance materiality, downward on the basis of “bad” information and upward on the basis of “good” information. Different combinations of transaction streams/accounts and risk factors were associated with different magnitudes of revision. However, at the level of individual participants, responses were quite varied. Some participants did not revise performance materiality and some even stated that performance materiality should not be revised based on risk‐related information. It may be that the concept of performance materiality as promulgated in CAS 320 and the relationship between overall materiality, performance materiality, and risk requires clarification to provide appropriate guidance for auditors to make performance materiality judgments.