While many retirement planning products such as annuities have been around for centuries, the needs of retirement financial security are drastically different in the 21st century. Just to name a few, the dynamics of family structure, employee-employer relationship, end-of-life care, rising medical costs, and financial technology, all made retirement planning much more complicated than just a decade ago. The idea of a one-size-fits-all retirement is an outdated concept. People face difficult choices on how to plan savings and enjoy their lifetime incomes. There is a critical need for a better understanding of the changing retirement needs in the society and how smart planning can achieve individualized retirement goals.
This project is the first phase of a multi-semester program to develop “smart technology” for retirement planning. The current phase involves content analysis of retirement planning products currently available in the market. No background on retirement planning is needed, as technical training will be provided. Preference will be given to candidates with strong communication and analytical skills.
Students: Ruizheng Bai, Xiaomeng Yu, Ivan Wu, Wilson Jonathan Phurwo, Shuyu Guo
Supervisor: Runhuan Feng
Do blue skies drive away pollution?
We would like to understand pollution microclimates. We would like to statistically understand if urban regions with better access to the sky allow for decreased pollution. We will be combining data from
Estimates of pollution from Chicago's array of things
Congestion estimates from the Chicago data portal
Estimates of sky occasion from Google Street view
Using some statistical tests, we would like to quantify some interactions. This project is done in collaboration with the IGL.
Students: Philip Dohm
Supervisor: Richard Sowers
Risk margin under Solvency II
Solvency II is concerned about the amount of capital European insurance companies need to hold to reduce the risk of insolvency. An important part of the Solvency II ratio is the risk margin. The first part of the research project will investigate what the risk margin represents, why it has to be calculated and what the relationship is between the risk margin and the capital requirements of a company. The second part of the project will deal with the cost of capital (CoC) approach for calculating the risk margin. Currently, companies take a CoC rate of 6%. We will investigate if the 6% is appropriate. We will also investigate if the CoC method can be improved by taking a time-varying CoC rate.
Students: Ying-Chia Meng, Alice Gut, Talia Sutio
Supervisor: Klara Buysse
Data driven study of the Herd Behavior Index
In this research project, we focus on dependencies between stock prices. The Herd Behavior Index (HIX) is a measure for the degree of co-movement between stock prices composing a stock market index, such as the Dow Jones or the S&P 500. Monitoring the HIX from day to day may help to detect a market where stock prices are likely to tumble all together.
The HIX can be calculated for a stock market index as long as option on the index and its constituents are available. We can determine a HIX for major stock indices such as the Dow Jones and the S&P 500. Since the stocks in such indices represent the market as a whole, the corresponding HIX values can be interpreted as a measure for interconnectedness of the financial market. However, we can as well calculate HIX values for sector ETF’s, which provides information about the degree of systemic risk in a sector.
The goals of the research project are as follows:
Determine daily values of the HIX for the period 1998-2017
Investigate the behavior of the HIX during several extreme market conditions (9/11, Dotcom, Credit crisis, European debt crisis, Trump election, etc.)
Investigate the difference between volatility and herd behavior.
Students: Owen Adhikaputra, Chenna Bhumi Reddy, Sovin Birla, Da Xu
Supervisor: Daniël Linders
Statistics for monitoring the healthiness of a portfolio
When a company offers a new insurance product, there are a set of assumptions to be made in order to start the pricing process. For example, the size or frequency of the claims could be unknown, as could be the variability around their expected value. There are several ways in which actuaries estimate these parameters, but the risk of being too far off the true distribution should be constantly monitored. This project aims to study three categories of this risk/uncertainty:
Assumed Expectation: deviation of the realized losses from their assumed expected value.
Volatility: even if the expected value of the incoming claims looks adequate a higher-than-expected variability could be exposing the company to unwanted risk.
Extreme events: a possible accumulation of high losses or catastrophic events should also be taken into account when monitoring risk.
The goal of the project is to explore the technical risk of an insurance portfolio into the three categories described above and use statistical techniques to derive possible metrics to measure the adequacy of the assumptions.
Students: Linshan Jiang
Supervisor: Daniël Linders
Graduate Supervisor: Gabriel Casabianca
Paratus LLC - Third party liability claims management (Student - Consulting)
Paratus Partners LLC is a provider of third-party liability claims management for healthcare service providers (mainly hospital systems). Hospitals incur losses because they fail to recover all their costs related to a covered claim. Especially claims covered through a third party liability insurer are sometimes too cumbersome to handle by a hospital. Most hospitals do not have sufficient resources or expertise to coordinate the benefit payments across the multiple carriers found in third party liability cases.
Paratus and the IRisk Lab are working closely together to address research questions with regards to claims management. In a series of projects, we aim in creating a model that will help Paratus to (a) provide better predictability to their clients and (b) enhance the efficiency and effectiveness of their workflow processes.
Students: Nargiz Alekberova, Biwen Ling, Jingying Luo, Sara Lagvankar
Supervisor: Daniël Linders
Northwestern Mutual Fixed Income Project - cont'd (Student-Consulting)
Evaluating the performance of an active manager in institutional fixed income portfolios is often challenging due to the necessary customization of issuance-based benchmarks to meet specific investment objectives. These constraints can be related to risk limits including factors such as aggregate credit quality, issuer concentration, or asset type. Other constraints can be more liability-based such as duration, convexity, or minimum yield. Simply assessing a manager’s total returns relative to a broad-based index or peer group in isolation does not provide a complete representation of the quality of management.
We are seeking to produce a better representation of the investment opportunity set a manager has available based upon various portfolio management constraints and the investment process employed. This modeling will enable us to analyze the following:
Realized portfolio total returns in the context of the available market opportunity set, given unique constraints.
Evaluation of trade-offs between different constraints placed on managers and the potential risk profile and return potential in various market environments.
Assessment of new investment managers and strategies to determine potential impacts to our overall fixed income portfolio’s risk profile and return potential.
Students: Tristan Boyles, Prathamesh Padhye, Titan Wibowo, Samuel Woessner
Supervisor: Daniel Stier, Klara Buysse
Forward and backward preferences - cont'd
Classical backward preferences of an investor are simply defined by a family of her value functions across states and times. Due to the backward nature, a terminal preference must be specified a priori. However, pre-specifying the future preference is actually unjustifiable in practice. To rectify this modeling drawback, a novel concept called forward preferences has been introduced in Musiela and Zariphopoulou (2008).
In this project, we study both the classical backward preferences and the recently developed forward preferences. As the first stage of this project, we aim to investigate and compare the two preferences via the closed-form representations of the preferences under the binomial market model.