Extreme-quantile tracking for financial time series

Time series of financial asset values exhibit well-known statistical features such as heavy tails and volatility clustering. We propose a nonparametric extension of the classical Peaks-Over-Threshold method from extreme value theory to fit the time varying volatility in situations where the stationarity assumption may be violated by erratic changes of regime, say. As a result, we provide a method for estimating conditional risk measures applicable to both stationary and nonstationary series. A backtesting study for the UBS share price over the subprime crisis exemplifies our approach.     

Analysis of optimal dynamic withdrawal policies in withdrawal guarantee products

Guaranteed Minimum Withdrawal Benefits (GMWB) are popular riders in variable annuities with withdrawal guarantees. With withdrawals spread over the life of the annuities contract, the benefit promises to return the entire initial annuitization amount irrespective of the market performance of the underlying fund portfolio. Treating the dynamic withdrawal rate as the control variable, the earlier works on GMWB have considered the construction of a continuous singular stochastic control model and the numerical solution of the resulting pricing model. This paper presents a more detailed characterization of the pricing properties of the GMWB and performs a full mathematical analysis of the optimal dynamic withdrawal policies under the competing factors of time value of fund, optionality value provided by the guarantee and penalty charge on excessive withdrawal. When a proportional penalty charge is applied on any withdrawal amount, we can reduce the pricing formulation to an optimal stopping problem with lower and upper obstacles. We then derive the integral equations for the determination of a pair of optimal withdrawal boundaries. When a proportional penalty charge is applied on the amount that is above the contractual withdrawal rate, we manage to characterize the behavior of the optimal withdrawal boundaries that separate the domain of the pricing models into three regions: no withdrawal, continuous withdrawal at the contractual rate and an immediate withdrawal of a finite amount. Under certain limiting scenarios such as a high policy fund value, the time close to expiry, or a low value of guarantee account, we manage to obtain analytical approximate solution to the singular stochastic control model of dynamic withdrawals.     

Decisionmetrics: A decision-based approach to econometric modelling

An approach to developing a possibly misspecified econometric model that will be used as the beliefs of an expected utility maximiser is proposed. This approach builds on a novel objective function that measures the value of predictive distributions in decision-making and is used in model estimation, selection and evaluation. The methods proposed also provide an econometric approach for developing arbitrary parametric action rules such as technical trading rules. The approach is compared in detail with existing methods and is applied in the context of a CARA investor's decision problem where analytical and empirical results suggest it is very effective.     

Proposal for calculating regulatory capital requirements for reverse mortgages

In the current context in which many people worry about the sustainability of pension systems, reverse mortgages are gaining popularity because they are a way to supplement elderly people's incomes. However, it is necessary to provide banks with an adequate risk measurement and management procedure for reverse mortgages to increase the commercialization of these products, which will result in greater well-being for the retirement age population. In this paper, we propose a method to measure risk and estimate the regulatory capital requirements for a portfolio of reverse mortgages owned by a financial institution according to Basel II and III. The method considers house price risk, mortality risk and interest rate risk; consequently, regulatory capital requirements need to be computed using a Monte Carlo simulation procedure. The proposed method is general and can accommodate several scenarios for reverse mortgage specifications, including fixed or variable mortgage rates and different income stream schemes (with the lump sum as a particular case). The results for the U.K. show that reverse mortgage providers face higher risk when the lender initially advances a higher amount, with the lump-sum case indicating the highest risk, for relatively younger borrowers, the female population, higher interest rates and floating mortgage rates.     

The Effectiveness of Portfolio Risk Diversification: An Additive Approach by Project Replication

This article proposes a probabilistic approach to project operational risk and project portfolio risk diversification. The analysis rests on a fundamental distinction between a fractional and an additive approach for constructing portfolios. Since the additive approach excludes variance as a measure of risk, the project's operational risk is defined by its probability of loss. Paradoxically, the effectiveness of any firm's portfolio risk diversification process will be negatively related to the operational risk of its representative project. We also present the conditions under which risk management and efficiency management can contribute to the firm's strategic imperative of lowering its operational risk.     

Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities

This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment confirms that the procedure works well in practice. Implementing the procedure with actual S&P500 option-implied volatilities and high-frequency five-minute-based realized volatilities indicates significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns.     

Technically and cost-efficient centralized allocations in data envelopment analysis

In data envelopment analysis and with a variable returns to scale production-technology, we apply Banker's [2] approach to determine the relationship between technically and cost-efficient industry structures, featuring reallocation of outputs and a variable number of firms. The interpretation based on the most productive and optimal scale-size notions allows us to both establish an inequality relationship between the corresponding industry-efficiency measures and provide adequate information on optimal solutions. At the applicative level, we introduce an exact algorithm to solve related non-linear programming problems, thus providing the decision maker with an accurate method for computing and comparing the input and output mixes and the optimal number of units obtained in the two allocations. Empirical illustration, given with reference to the Italian local-public-transit sector and employing a multiple inputs and outputs technology, reveals striking differences with regard to the managerial and regulatory implications of the two centralized allocations.     

The Risk-Relevance of Accounting Data: Evidence from the Spanish Stock Market

This paper analyses the relevance of accounting fundamentals to inform about equity risk as measured by the cost of equity capital. Assuming the latter is a summary measure of how investors make decisions regarding the allocation of resources, the strength of the association between the cost of capital and the accounting‐based measures of risk indicates how important these measures are for market participants when making economic decisions. To infer the cost of equity capital, we use the O'Hanlon and Steele's method, which is based on the residual income valuation model. Moreover, we use the insights from this model to provide a theoretical underpinning for the choice of the accounting variables related to risk. The sample refers to the non‐financial firms listed in the Madrid Stock Exchange along the period 1987–2002. Our results support our initial expectations regarding the association between the cost of equity capital and the accounting‐based risk variables, thereby supporting the usefulness of fundamental analysis to determine the risk inherent in share's future payoffs. In particular, we highlight the role of investing risk, which has been ignored in previous research. Our results are also robust to measures of risk other than the cost of capital such as the variability in total returns and the firm's systematic risk (β).     

Risk aversion for variational and multiple-prior preferences

The objective of this paper is to identify variational preferences and multiple-prior (maxmin) expected utility functions that exhibit aversion to risk under some probability measure from among the priors. Risk aversion has profound implications on agents’ choices and on market prices and allocations. Our approach to risk aversion relies on the theory of mean-independent risk of Werner (2009). We identify necessary and sufficient conditions for risk aversion of convex variational preferences and concave multiple-prior expected utilities. The conditions are stability of the cost function and of the set of probability priors, respectively, with respect to a probability measure. The two stability properties are new concepts. We show that cost functions defined by the relative entropy distance or other divergence distances have that property. Set of priors defined as cores of convex distortions of probability measures or neighborhoods in divergence distances have that property, too.     

Venture capital firms’ lead orientation,network position,and selection of familiar syndicate partners

This study explores how a venture capital (VC) firm’s lead orientation (i.e., acting more as a lead investor rather than a follower investor in past syndication investments) influences its selection of familiar syndicate partners. By conducting empirical research that uses detailed information of 11,219 investment deals in China from January 1999 to June 2016, we find that a VC firm’s lead orientation has an inverted U-shaped relationship with its propensity to select familiar syndicate partners. In other words, as a VC firm’s lead orientation increases, its familiarity degree with the partners in the subsequent syndicated investment will first increase and then decrease. In addition, a VC firm’s network centrality and network constraint imposed by the VC network structure both weakens the inverted U-shaped relationship between its lead orientation and the selection of familiar partners. Robustness checks with different measures and samples, together with the instrumental variable approach and the Heckman selection model that address potential endogeneity problems, show the reliability of our findings.     

Consider the non-adopter: Developing a prediction model for the adoption of household-level broadband access

We explore the decision process for the household-level adoption of broadband Internet access. Our aim, of determining the barriers to household-level broadband adoption and how to best overcome those barriers, guides our analysis in an effort to better inform broadband policy development and implementation. We introduce and rely on data collected from 3101 New Jersey households under the National Telecommunications and Information Administration's nationwide Broadband Technology Opportunity Program. Following MATH, the Model of Technology Adoption in Households, extended with a moderating control variable, we model the conditions under which a household is more or less likely to adopt household-level broadband Internet access. We specify an original two-step model that first estimates from demographic determinants a linear latent variable of the responding household's propensity to not adopt high-speed broadband Internet access, and then regresses that propensity variable on behavioral and attitudinal measures about broadband and computer use and familiarity. Analyzing those outcomes generates three empirical findings that help inform the efficient implementation of policies to establish universal broadband access: (1) demographically, household-level broadband adoption in New Jersey is colorblind: race and ethnicity, in and of themselves, do not predict household-level broadband adoption; (2) behaviorally, the strongest facilitator for household-level broadband adoption is computer use by the household decision-maker; and (3) structurally, the strongest barrier to such adoption is lack of resources. Decomposing and better understanding the phenomenon of non-adoption will help to inform planning efforts to maximize household-level broadband adoption.     

Income diversification and bank risk in Asia Pacific

This paper investigates the effect of income diversification on bank risk for a large sample of commercial banks in 14 Asia Pacific economies over the period 2011–2016. Using a dynamic panel data model with a system generalized methodof moments estimator, we find that banks with a higher level of income diversification are less risky in general. We further consider both developed and emerging economies according to the International Monetary Fund's classification of the level of economic development. Specifically, for emerging economies, the results indicate that banks with a higher level of income diversification face less risk. However, the diversification of commercial bank income has no significant impact on bank risk in developed Asia Pacific economies.     

Accountability and Role Effects in Balanced Scorecard Performance Evaluations When Strategy Timeline Is Specified

Abstract

This study experimentally examines if fixation on lagging financial measures (relative to leading non-financial measures) as reported in prior balanced scorecard literature is mitigated when evaluators are provided with a strategy implementation timeline (a non-manipulated variable). The experiment manipulates whether or not evaluators are subject to process accountability as well as the role to which evaluators are assigned (i.e. supervisor or subordinate). We predict and find that, in general, the provision of an implementation timeline results in evaluators placing more weight on strategically linked, leading non-financial measures within a subordinate's time span of control compared to strategically linked, lagged financial measures beyond the subordinate's controllable time horizon. However, we also find that evaluators in the role of a supervisor differentiate less between strategically linked non-financial measures that fall within the subordinate's control and strategically linked financial measures beyond the subordinate's control when held accountable compared to supervisors not held accountable. On the other hand, participants in the role of a subordinate were able to differentiate appropriately between these measures when held accountable. Our results extend prior research by considering how linking a timeline to strategy implementation may assist evaluators when assessing performance in the presence of both leading and lagging strategic measures. Further, reference to an implementation timeline may influence role and accountability effects. Implications for future research in multidimensional strategic performance evaluation are discussed.     

Do institutional investors monitor management? Evidence from the relationship between institutional ownership and capital structure

We examine the dynamic relations between institutional ownership and a firm's capital structure. We find that a firm's leverage decreases when institutional ownership increases. This result implies that a firm reduces its debt level as institutional investors substitute for the monitoring role of debt. More importantly, we find that a firm's suboptimal leverage decreases when the institutional ownership increases, and institutional ownership decreases when a firm's suboptimal leverage increases. This finding shows that institutions not only effectively monitor a firm's capital structure but they also passively sell their shares when dissatisfied with it. In addition, we find that the monitoring evidence on a firm's leverage and suboptimal leverage are more pronounced when the institutional investors are less likely to have business relationships with a firm or the information asymmetry is high in the market.     

Do Analysts Strategically Employ Cash Flow Forecast Revisions to Offset Negative Earnings Forecast Revisions?

We investigate whether analysts use cash flow forecasts to reduce the impact of earnings forecast revisions (EFRs) on market participants. In particular, we focus on conflict between an analyst's concurrent cash flow and earnings forecast revisions. We hypothesize and find that analysts are more likely to issue a positive cash flow forecast revision when they issue a negative earnings forecast revision concurrently, but not the opposite, particularly for Fortune 500 firms. Furthermore, our supplementary analyses suggest that (1) some analysts optimistically bias cash flow forecasts when they issue negative earnings forecast revisions; (2) the market pays less attention to the historical accuracy of analyst cash flow forecasts, so analysts have some latitude to present their cash flow forecasts in an optimistic way; and (3) the market reacts mainly to the direction, not the magnitude, of cash flow forecast revisions. Overall, these findings suggest that analysts may strategically use cash flow forecasts in conjunction with earnings forecasts to maintain good management relationships.     

Corporate Risk Disclosure and Audit Fee: A Text Mining Approach

The aim of this study is to introduce an innovative text mining approach to assess firms' risks using unstructured textual disclosure from annual reports. Specifically, we use Natural Language Processing techniques to extract firms' self-identified risks including financial, strategic, operational, and hazard risks based on an enterprise risk management framework. We examine the association between these four risk measures derived from the risk factor section in 10-K filings and audit fees. The results show that audit fees are significantly and positively related to firm-specific financial, strategic, and operational risks, indicating the informativeness of corporate textual risk disclosures. This study provides direct support for the recent US reporting regulatory requirement of adding a new section on risk factors in corporate annual reports.     

Integrated risk management and product innovation in China: The moderating role of board of directors

This study explored the role of the board of directors in the relationship between integrated risk management and product innovation. We focused on a board's direct involvement in risk oversight and its use of external audit in risk oversight, and examined their moderating effects on the relationship between integrated risk management and product innovation. Panel data from a survey of 1178 Chinese firms was analyzed to test the hypotheses. A board's direct involvement in risk oversight was found to negatively moderate the positive relationship between integrated risk management and product innovation success. The use of external audit in risk oversight similarly weakens the relationship. These results show how an effective board contributes to the innovation benefits associated with risk management in product innovation. They also have important implications for emerging economy firms pursuing an integrated approach to risk management in product innovation.     

Estimation of Markov regime-switching regression models with endogenous switching

Following Hamilton [1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357–384], estimation of Markov regime-switching regressions typically relies on the assumption that the latent state variable controlling regime change is exogenous. We relax this assumption and develop a parsimonious model of endogenous Markov regime-switching. Inference via maximum likelihood estimation is possible with relatively minor modifications to existing recursive filters. The model nests the exogenous switching model, yielding straightforward tests for endogeneity. In Monte Carlo experiments, maximum likelihood estimates of the endogenous switching model parameters were quite accurate, even in the presence of certain model misspecifications. As an application, we extend the volatility feedback model of equity returns given in Turner et al. [1989. A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics 25, 3–22] to allow for endogenous switching.     

Variance trading and market price of variance risk

This paper develops a new approach for variance trading. We show that the discretely-sampled realized variance can be robustly replicated under very general conditions, including when the price can jump. The replication strategy specifies the exact timing for rebalancing in the underlying. The deviations from the optimal schedule can lead to surprisingly large hedging errors. In the empirical application, we synthesize the prices of the variance contract on S&P 500 index over the period from 01/1990 to 12/2009. We find that the market variance risk is priced, its risk premium is negative and economically very large. The variance risk premium cannot be explained by the known risk factors and option returns.     

Developing an emission risk control model in coal-fired power plants for investigating CO2 reduction strategies for sustainable business development

The climatic risk consists of financial and environmental risks, predominantly evolutes from carbon dioxide emissions from coal-fired power plants. We develop a climatic risk control model to investigate the coexistence of renewable energy and the post-combustion carbon capture technology as CO₂ reduction strategies. Additionally, our proposed framework explicitly considers the acceptance of such strategies based on a balance of electricity supply according to demand. This paper adopts an additive fuzzy mixed-integer optimization approach to model uncertain parameters and determines the optimal solution focusing on business and the environment. Furthermore, we investigate the feasibility of such emission control strategies with scenario analysis that help to execute the country's emission reduction policies. The usefulness of our methodology is demonstrated using data from the coal-based power sector in the Eastern part of India. Overall, with the proposed model, we can achieve 30% reduction in emission release, which provides strategies to the decision-maker for investment towards sustainable development.