Lost in diversification

As financial instruments grow in complexity, more and more information is neglected by risk optimization practices. This brings down a curtain of opacity on the origination of risk, which has been one of the main culprits in the 2007–2008 global financial crisis. We discuss how the loss of transparency may be quantified in bits, using information theoretic concepts. We find i) that financial transformations imply large information losses, ii) that portfolios are more information sensitive than individual stocks only if fundamental analysis is sufficiently informative on the co-movement of assets, iii) that securitisation, in the relevant range of parameters, yields assets that are less information sensitive than the original stocks, and iv) that, when diversification (or securitisation) is at its best (i.e. when assets are uncorrelated), information losses are maximal. We also address the issue of whether pricing schemes can be introduced to deal with information losses. This is relevant for the transmission of incentives to gather information on the risk origination side. Within a simple mean variance scheme, we find that market incentives are not generally sufficient to make information harvesting sustainable.     

On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems

We point out the need for Behavioral Operational Research (BOR) in advancing the practice of OR. So far, in OR behavioral phenomena have been acknowledged only in behavioral decision theory but behavioral issues are always present when supporting human problem solving by modeling. Behavioral effects can relate to the group interaction and communication when facilitating with OR models as well as to the possibility of procedural mistakes and cognitive biases. As an illustrative example we use well known system dynamics studies related to the understanding of accumulation. We show that one gets completely opposite results depending on the way the phenomenon is described and how the questions are phrased and graphs used. The results suggest that OR processes are highly sensitive to various behavioral effects. As a result, we need to pay attention to the way we communicate about models as they are being increasingly used in addressing important problems like climate change.     

Synthesis and biological evaluation of a lipopeptide-based methamphetamine vaccine

We describe an application of carrier protein-free strategy in constructing a fully synthetic methamphetamine (METH) vaccine that contains three components: Toll-like receptor 2 ligand, Th2 epitope, and METH hapten. The immunological evaluation in mice revealed high titers of METH-specific antibodies induced by the construct and the activation of humoral immunity that would be beneficial for neutralization and clearance of the METH molecule. Behavioral experiments indicated that the synthetic vaccine attenuated the acquisition of METH-induced conditioned place preference and inhibited the initiation and expression of METH-induced locomotor sensitization. These results demonstrate that the lipopeptide-based vaccine has invoked an immune response and showed the potential of preventing the rewarding and psychoactive effects of METH.     

Comparison of solutions of stochastic equations and applications

Pathwise comparison of solutions to a class of stochastic systems of differential equations is proved which extends the existing result of Geiβ and Manthey. When the diffusion coefficients are defferent, the Gal’?huk-Davis method is extended to establish the comparision results. We illustrate our results with several examples some of which arise in stochastic finance theory     

Two-stage financial risk tolerance assessment using data envelopment analysis

Typical questionnaires administered by financial advisors to assess financial risk tolerance mostly contain stereotypes of people, have seemingly unscientific scoring approaches and often treat risk as a one-dimensional concept. In this work, a mathematical tool was developed to assess relative risk tolerance using Data Envelopment Analysis (DEA). At its core, it is a novel questionnaire that characterizes risk by its four distinct elements: propensity, attitude, capacity, and knowledge. Over 180 individuals were surveyed and their responses were analyzed using the Slacks-based measure type of DEA efficiency model. Results show that the multidimensionality of risk must be considered for complete assessment of risk tolerance. This approach also provides insight into the relationship between risk, its elements and other variables. Specifically, the perception of risk varies by gender as men are generally less risk averse than women. In fact, risk attitude and knowledge scores are consistently lower for women, while there is no statistical difference in their risk capacity and propensity compared to men. The tool can also serve as a “risk calculator” for an appropriate and defensible method to meet legal compliance requirements, known as the “Know Your Client” rule, that exist for Canadian financial institutions and their advisors.     

Hysteresis effects under CIR interest rates

Most decision making research in real options focuses on revenue uncertainty assuming discount rates remain constant. However, for many decisions revenue or cost streams are relatively static and investment is driven by interest rate uncertainty, for example the decision to invest in durable machinery and equipment. Using interest rate models from Cox et al. (1985b), we generalize the work of Ingersoll and Ross (1992) in two ways. Firstly, we include real options on perpetuities (in addition to zero coupon cash flows). Secondly, we incorporate abandonment or disinvestment as well as investment options, and thus model interest rate hysteresis (parallel to revenue uncertainty in Dixit (1989a)). Under stochastic interest rates, economic hysteresis is found to be significant, even for small sunk costs.     

A mixed integer linear programming model for optimal sovereign debt issuance

Governments borrow funds to finance the excess of cash payments or interest payments over receipts, usually by issuing fixed income debt and index-linked debt. The goal of this work is to propose a stochastic optimization-based approach to determine the composition of the portfolio issued over a series of government auctions for the fixed income debt, to minimize the cost of servicing debt while controlling risk and maintaining market liquidity. We show that this debt issuance problem can be modeled as a mixed integer linear programming problem with a receding horizon. The stochastic model for the interest rates is calibrated using a Kalman filter and the future interest rates are represented using a recombining trinomial lattice for the purpose of scenario-based optimization. The use of a latent factor interest rate model and a recombining lattice provides us with a realistic, yet very tractable scenario generator and allows us to do a multi-stage stochastic optimization involving integer variables on an ordinary desktop in a matter of seconds. This, in turn, facilitates frequent re-calibration of the interest rate model and re-optimization of the issuance throughout the budgetary year allows us to respond to the changes in the interest rate environment. We successfully demonstrate the utility of our approach by out-of-sample back-testing on the UK debt issuance data.     

Sudden changes in variance and time varying hedge ratios

This paper analyzes the influence of sudden changes in the unconditional volatility on the estimation and forecast of volatility and its impact on futures hedging strategies. We employ several multivariate GARCH models to estimate the optimal hedge ratios for the Spanish stock market including in each one some well-known patterns that may affect volatility forecasts (asymmetry and sudden changes). The main empirical results show that more complex models including sudden changes in volatility outperform the simpler models in hedging effectiveness both with in-sample and out-of-sample analysis. However, the evidence is stronger when the loss distribution tail is used as a measure for the effectiveness (Value at Risk (VaR) and Expected Shortfall (ES)) suggesting that traditional measures based on the variance of the hedged portfolio should be used with caution.     

On the predictive ability of narrative disclosures in annual reports

We investigate whether narrative disclosures in 10-K and 10K-405 filings contain value-relevant information for predicting market performance. We apply text classification techniques from computer science to machine code text disclosures in a sample of 4280 filings by 1236 firms over five years. Our methodology develops a model using documents and actual performance for a training sample. This model, when applied to documents from a test set, leads to performance prediction. We find that a portfolio based on model predictions earns significantly positive size-adjusted returns, indicating that narrative disclosures contain value-relevant information. Supplementary analyses show that the text classification model captures information not contained in document-level features of clarity, tone and risk sentiment considered in prior research. However, we find that the narrative score is not providing information incremental to traditional predictors such as size, market-to-book and momentum, but rather affects investors’ use of price momentum as a factor that predicts excess returns.     

Pricing caps with HJM models: The benefits of humped volatility

In this paper we compare different multifactor HJM models with humped volatility structures, to each other and to models with strictly decreasing volatility. All the models are estimated on Euribor and swap rates panel data maximizing the quasi-likelihood function obtained from the Kalman filter. We develop the analysis in two steps: first we study the in-sample properties of the estimated models, then we test the pricing performance on caps. We find the humped volatility specification to greatly improve the model estimation and to provide sufficiently accurate cap prices, although the models has been calibrated on interest rates data and not on cap prices. Moreover, we find the two-factor humped volatility model to outperform the three-factor models in pricing caps.