Robust Utility Maximization in a Stochastic Factor Model

We give an explicit PDE characterization for the solution of a robust utility maximization problem in an incomplete market model, whose volatility, interest rate process, and long-term trend are driven by an external stochastic factor process. The robust utility functional is defined in terms of a HARA utility function with negative risk aversion and a dynamically consistent coherent risk measure, which allows for model uncertainty in the distributions of both the asset price dynamics and the factor process. Our method combines two recent advances in the theory of optimal investments: the general duality theory for robust utility maximization and the stochastic control approach to the dual problem of determining optimal martingale measures.optimal investment, model uncertainty, incomplete markets, stochastic volatility, coherent risk measures, optimal control, convex duality

Robust Maximization of Consumption with Logarithmic Utility

We analyze the stochastic control approach to the dynamic maximization of the robust utility of consumption and investment. The robust utility functionals are defined in terms of logarithmic utility and a dynamically consistent convex risk measure. The underlying market is modeled by a diffusion process whose coefficients are driven by an external stochastic factor process. Our main results give conditions on the minimal penalty function of the robust utility functional under which the value function of our problem can be identified with the unique classical solution of a quasilinear PDE within a class of functions satisfying certain growth conditions.Robust utility maximization, optimal consumption, stochastic factor model, stochastic control, convex risk measure, dynamic consistency, Hamilton-Jacobi-Bellman equation

A Control Approach to Robust Utility Maximization with Logarithmic Utility and Time-Consistent Penalties

We propose a stochastic control approach to the dynamic maximization of robust utility functionals that are defined in terms of logarithmic utility and a dynamically consistent convex risk measure. The underlying market is modeled by a diffusion process whose coefficients are driven by an external stochastic factor process. In particular, the market model is incomplete. Our main results give conditions on the minimal penalty function of the robust utility functional under which the value function of our problem can be identified with the unique classical solution of a quasilinear PDE within a class of functions satisfying certain growth conditions. The fact that we obtain classical solutions rather than viscosity solutions is important for the use of numerical algorithms, whose applicability is demonstrated in examples.Optimal investment, model uncertainty, incomplete markets, stochastic volatility, coherent risk measure, convex risk measure, optimal control, convex duality

Optimal investment under multiple defaults risk: A BSDE-decomposition approach

We study an optimal investment problem under contagion risk in a financial model subject to multiple jumps and defaults. The global market information is formulated as a progressive enlargement of a default-free Brownian filtration, and the dependence of default times is modeled by a conditional density hypothesis. In this Ito-jump process model, we give a decomposition of the corresponding stochastic control problem into stochastic control problems in the default-free filtration, which are determined in a backward induction. The dynamic programming method leads to a backward recursive system of quadratic backward stochastic differential equations (BSDEs) in Brownian filtration, and our main result proves, under fairly general conditions, the existence and uniqueness of a solution to this system, which characterizes explicitly the value function and optimal strategies to the optimal investment problem. We illustrate our solutions approach with some numerical tests emphasizing the impact of default intensities, loss or gain at defaults and correlation between assets. Beyond the financial problem, our decomposition approach provides a new perspective for solving quadratic BSDEs with a finite number of jumps.Comment: Published in at http://dx.doi.org/10.1214/11-AAP829 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

An information security risk-driven investment model for analysing human factors

Modern organisational structure and risk management model are characterised by a wide range of forces including the role of human factors which combine to create an unprecedented level of uncertainty and exposure to information security risk, investment and decision making process. Developing a risk-driven investment model for information security systems with consideration of subjective nature of critical human factors, is a challenging task. The overall success of an information security system depends on analysis of the risks and threats so that appropriate protection mechanism can be in place to protect them. However, lack of appropriate analysis of such dependencies and understanding potentially results in information security systems to fail or to fully achieve their that depend on them. Existing literature does not provide adequate guidelines for a systematic process or an appropriate modelling language to support such analysis. This paper fills this gap by introducing a process that allows information security managers to capture possible riskinvestment relationships and to reason about them. The process is supported by a modelling language based on a set of concepts relating to trust and control and secure tropos and requirements engineering. In order to demonstrate the applicability and usefulness of the approach a descriptive example from an UK organisation is used. Keywords: Information Security (IS), Information Security Risk-Driven Investment Model (RIDIM), Risk, Social Engineering Attacks (SEAs), Security Investment (SI), Return On Investment in Information Security (ROISI)

Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis

Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called Volatility Target (VolTarget) strategy. In particular, we focus our attention on estimating volatility levels of a risky asset to perform a VolTarget simulation over two different time horizons. We first consider a 20 year period, from January 2000 to January 2020, then we analyse the last 12 months to emphasize the effects related to the COVID-19 virus\u2019s diffusion. We propose a hybrid algorithm based on the composition of a GARCH model with a Neural Network (NN) approach. Let us underline that, as an alternative to standard allocation methods based on realized and backward oriented volatilities, we exploited an innovative forward-looking estimation process exploiting a Machine Learning (ML) solution. Our solution provides a more accurate volatility estimation, allowing us to derive an effective investor risk-return profile during market crisis periods. Moreover, we show that, via a forward-looking VolTarget strategy while using an ML-based prediction as the input, the average outcome for an investment in a drawdown plan is more sustainable while representing an efficient risk-control solution for long time period investments

How is the innovation process developed in traditional companies by combining the plan-oriented and flexible process models, and in which situations is it utilized?

This paper analyzes how the innovation process is developed in traditional companies by combining the plan-oriented and flexible process models, and in which situations it is utilized. To answer the research question, a multiple case study was selected. This research compared three traditional manufacturing companies within the same industry with in-depth interviews, follow-up interviews, observations, and secondary research. Traditional manufacturing companies today have begun to focus more on innovations, even so, how they organize for innovation differ amongst the companies. Five out of the eight boundary conditions presented by Paluch et al., (2019), proved highly important for the traditional companies researched, but the findings revealed investment and time influence, strategic fit and willingness to change and mindset to be of equal importance for selecting the general Hybrid innovation process. Nevertheless, three separate situations were revealed to affect the development of the combined process in traditional manufacturing companies. These situations were then based on four out of nine conditions initially found through both expected and observed pattern matching during the analysis; investment, consumer preferences, managerial control, and approach to risk. The different combination of the conditions resulted in three situation-based approaches to the Hybrid process model: short-term incremental-, short-term radical- and long-term radical innovations. From a theoretical perspective, this study emphasizes a need for a combined process. When considering large traditional manufacturing companies’ approach to innovation and how the degree of leaning towards the Agile or Stage-Gate method, whilst still being a Hybrid process, highly varies based on the innovational situation related to time and radicality. From a practical perspective, the three combined processes developed through research can serve as a guideline for innovation managers and help simplify the practice for mutual understanding of how to organize the innovation process based on three separate situations

The New Basel Capital Accord: Structure, Possible Changes and Micro- and Macroeconomic Effects. CEPS Reports in Finance and Banking No. 30, 1 September 2002

During the last 12 years, the 1988 Basel Capital Accord dealing with minimum capital requirements for internationally active financial institutions has grown more pervasive, being integrated into national regulations in most advanced countries. Meanwhile, the limitations and drawbacks of the simple rules on which it is based have become increasingly apparent. In other words, the existence of a gap between supervisory requirements and risk-based measures of economic capital has led to forms of regulatory arbitrage (whereby loopholes in the regulation have been exploited to increase the real leverage of a bank without reducing its capital ratios). Paradoxically, the inability of the 1988 protocol to discriminate between investment grade and junk borrowers might also have made some financial institutions more risk-seeking, instead of helping them control their risks. To address such challenges, the Basel Committee on Banking Supervision has been engaged for several years in a revision process that will finally lead to a New Basel Capital Accord (NBCA). Remarkably, the new Accord is not being engineered inside a secluded laboratory by a handful of regulators and financial rocket-scientists, but its contents have been thoroughly discussed by national supervisors, banks and academics. Thus, the NBCA drafting has become a meeting point for many different perspectives: legal experts, accountants, bank managers, central bankers and finance scholars (to name only a few) have been working together, merging their professional backgrounds to make the NBCA more robust in its structure and parameters. This report tries to provide a complete, up-to-date, critical picture of the new Basel approach to bank capital, by summarising its structure and possible changes, and by focusing on some limitations and pitfalls that might deserve further investigation

Optimal Deterministic Investment Strategies for Insurers

We consider an insurance company whose risk reserve is given by a Brownian motion with drift and which is able to invest the money into a Black–Scholes financial market. As optimization criteria, we treat mean-variance problems, problems with other risk measures, exponential utility and the probability of ruin. Following recent research, we assume that investment strategies have to be deterministic. This leads to deterministic control problems, which are quite easy to solve. Moreover, it turns out that there are some interesting links between the optimal investment strategies of these problems. Finally, we also show that this approach works in the Lévy process framework