Areas of Expertise (6)
Aparna Gupta is a professor of quantitative finance and director of the Center for Financial Studies in the Lally School of Management at Rensselaer Polytechnic Institute. She has been the founding director of the MS program in Quantitative Finance and Risk Analytics at RPI, and holds a joint appointment in industrial and systems engineering in the School of Engineering at RPI.
Dr. Gupta has also been a visiting researcher at US SEC in Washington DC for three years.
Her research interest is in financial decision support, risk management, and financial engineering. She applies mathematical modeling, machine learning and financial engineering techniques for risk management both in technology-enabled network services, such as energy and renewable energy systems, communication systems, and technology-enabled service contracts, as well as risk management in the inter-connected financial institutions and financial markets.
She has worked on several US National Science Foundation, US Department of Energy funded research projects in financial innovations for risk management. Currently, she is working to establish a multi-university multi-disciplinary NSF-funded center for research toward advancing financial technologies (CRAFT).
Dr. Gupta's research has been published in top quantitative finance and operations research journals, and has been awarded various recognitions, including 2018 best paper award of the Financial Management Association and 2019 best conference paper award at the 17th FRAP Conference. She is the author of the book, Risk Management and Simulation. Dr. Gupta is a member of WFA, FMA, INFORMS, GARP and IAQF, and serves on the editorial board of several quantitative finance and analytics journals. She earned her doctorate from Stanford University and her B.Sc. and M.Sc. degrees in Mathematics from the Indian Institute of Technology, Kanpur.
Stanford University: Ph.D. 2000
Stanford University: M.S. 1997
Indian Institute of Technology (Kanpur, India): M.Sc. (Integrated) 1994
Media Appearances (1)
Rensselaer Receives Over 2.6M to Study Renewable Energy
ABC News- News10 Albany tv
Rensselaer Polytechnic Institute received millions to help integrate renewable energy into New York State’s power grid. Over $2.6 million in federal funding came from the Department of Energy’s Advanced Research Projects Agency-Energy through their new Performance-based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) program.
Addressing systemic risk using contingent convertible debt – A network analysisEuropean Journal of Operational Research
Gupta, A., Lu, Y., and Wang, R.
We construct a balance sheet network model to study the interconnectedness of a banking system. A simulation analysis of the buffer effect of contingent convertible (CoCo) debt in controlling contagion in a theoretical banking network model is followed by calibrating the model using 13F filings. We find that CoCo debt conversion significantly mitigates systemic risk, with a dual-trigger CoCo debt design being more effective in protecting the surviving banks. A two-tranche CoCo debt design combines the benefits of single and dual-trigger CoCo debt. The trade-offs in different designs of CoCo triggers can be evaluated in a network simulation model, as developed in this work.
Risk management of renewable power producers from co-dependencies in cash flowsEuropean Journal of Operational Research
Bhattacharya, S., Gupta, A., Kar K., and Owusu, A.
Increasing adoption of renewable energy, which is inherently intermittent, poses several business risks for renewable energy producers. We identify the core co-dependencies of electricity demand, temperature and radiation risk exposures of a solar energy producer at different times of the year, which offer a valuable risk mitigation opportunity. By capturing the co-dependencies in a vector autoregressive, multivariate GARCH model, we investigate the extent of natural hedge embedded in the solar energy producer’s cash flows. We further develop the framework to use explicit optimal cross hedging strategies for risk mitigation using temperature-based weather derivatives. We find that there is significant benefit of natural hedge in certain months of the year, while in others, explicit hedges can effectively modify risk exposure.
Filtering for Risk Assessment of Interbank NetworkEuropean Journal of Operational Research
Gupta, A., Kar, K., and Simaan, M.
Our paper contributes to the recent macroprudential policy addressing the resilience of financial systems in terms of their interconnectedness. We argue that beneath an interbank market, there is a fundamental latent network that affects the liquidity distributions among banks. To investigate the interbank market, we propose a framework that identifies such latent network using a statistical learning procedure. The framework reverse engineers overnight signals observed as banks conduct their reserve management on a daily basis. Our simulation-based results show that possible disruptions in funds supply are highly affected by the interconnectedness of the latent network. Hence, the proposed framework serves as an early warning system for regulators to monitor the overnight market and to detect ex-ante possible disruptions based on the inherent network characteristics.
Learning risk culture of banks using news analyticsEuropean Journal of Operational Research
Agarwal, A., Gupta, A., Kumar, A.K., Tamilselvam, S.
Risk culture is arguably a leading contributor to risk outcomes of a firm. We define risk culture indicators based on unstructured news data to develop a qualitative assessment of risk culture of banks. For US banks participating in an annual stress test program, we conduct a supervised learning ridge regression analysis to identify the most significant features to evaluate banks’ risk culture characteristics. These features are used for unsupervised clustering to determine the high to low quality of risk culture. The distinct groups obtained from clustering define and allow monitoring changes in the quality of risk culture in banks.
A network approach to unravel asset price comovement using minimal dependence structureJournal of Banking & Finance
de Carvalho, P.J.C., and Gupta, A
We develop a network representation-based methodology to aid an exploratory analysis of temporally evolving comovement in asset prices. This parsimonious order-n representation of the most significant comovement in asset prices, filtered by common factors, allows tackling a large number of assets and unraveling their complex comovement structure. Flexibility in choosing explanatory factors to suit the specific objectives of a study makes this methodology useful for portfolio analysis, risk parity approaches, and risk management decisions. We illustrate the features of the methodology for a set of major industry equity indices and to blue chip stocks, where we analyze the dynamic relevance of Fama–French factors. Investigating the network for more than 20 years, including the dot-com bust, global financial crisis, and European debt crisis, helps draw many insights. For instance, unexpected industries are seen to connect idiosyncratically through the dot-com bust. We demonstrate that a network factor model based portfolio allocation performs better than a regular factor model based allocation.
Risk assessment based on the analysis of the impact of contagion flowJournal of Banking & Finance
Edirisinghe, N.C., Gupta, A., and Roth, W.
This paper presents a new framework to model and calibrate the process of firm value evolution when an unanticipated exogenous event impacting one firm can contagiously affect other firms. The nature of propagation of such contagion is determined by the underlying connections between firms, which can adversely affect the tail risks of firm value, hence the securities issued by the firm. This paper combines the insights gained from the existing firm-value models and historical events into a structural model for flow of contagion among firms using a network-based approach. Rather than using stylized networks, we develop a data-driven approach for network construction where we define and calibrate several contagion variables to model the spread of contagion. This framework is applied for assessing firm-level risk under downside risk measures. Using actual data, our model illustrates how connections between firms can lead to heavy-tailed default distributions and default clustering observed in practice.