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Fall 2018

Option-Implied indicators for market stress


TStock and index options are traded on the financial market and their prices  are determined by supply and demand. These prices are publicly available and are forward-looking: they contain information about the aggregate view of the market about the future dynamics of the financial market. The Volatility Index (VIX) is the market barometer for volatility and is calculated continuously during the trading day by the CBOE. The Herd Behavior Index (HIX) is a recently developed measure for the degree of co-movement between stocks. high levels for the HIX correspond wiht a market where stocks are moving strongly together. Currently, up-to-date levels for the HIX are not available. In this project we will develop an online tool which calculates the HIX in real time during the trading day. This requires building a webscraping tool for option data after which the data is used as input in the HIX calculations.


Students: Padalia Bansi, Tamara Beecroft, Biwen Ling, Yuxin Wei, Haocheng Wu, Yun Xia 

Supervisor: Daniël Linders


Forward and backward preferences


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.

Students: Gongyi Chen, Bhanu Segal

Supervisor: Alfred Chong

Visualization of sample recycling methods for nested stochastic modeling


As more regulatory reporting requirements in the regulatory regimes around the world move towards dependence on stochastic approaches, insurance companies are experiencing increasing difficulty with detailed forecasting and more accurate valuation and risk assessment based on Monte Carlo simulations.

Stochastic modeling is commonly used by financial reporting actuaries whenever reporting procedures, such as reserving and capital requirement calculation, are performed under various economic scenarios, which are stochastically determined. Nested stochastic modeling is required whenever modeling components under each economic scenario are determined by stochastic scenarios in the further future.

Many existing techniques to speed up nested simulations are based on the reduction of inner loop calcuations by curve fitting techniques. The essence of these techniques is to develop a functional relationship between risk factors (equity values, interest rates, etc) and target features (insurance liability or their greeks) of inner loop calculations. Such functional relationship can be approximated by multivariate interpolation or smoothing techniques such as least squares Monte Carlo. Nonetheless, these techniques often require a large size of economic scenarios to develop accurate enough functional relationships, which could also be very costly to begin with. The new Sample Recycling technique is based on an entirely different strategy, which is to avoid "approximate" functional relationship but instead to save time by recycling a limited set of economic scenarios.

This project is intended to provide a visualization of sample recycling methods and to make the new technology accessible to practitioners. The research team is expected to make a Youtube video to illustrate the technology.

Students: Zhangyao Chen, Hao Gai, Wilson Jonathan Phurwo, Jeremy Soriano, Samuel Woessner

Supervisor: Runhuan Feng

Youtube Video: Click here

Northwestern Mutual Fixed Income Project


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. 


Actuarial Science: Prathamesh Padhye, Hanmiao Shen, Titan Wibowo

Finance: Hongyu Li, Chensheng Zhang

Supervisor: Daniel Stier, Klara Buysse 

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