Krishna Ramaswamy

• Chua, C.T. Krishna Ramaswamy, Robert A. Stine (2009), Predicting Short-Term Eurodollar futures, Journal of Fixed Income, 18, 47–61.
• Choong Tze Chua, Dean P. Foster, Krishna Ramaswamy, Robert A. Stine (2007), A Dynamic Model for the Forward Curve, Review of Financial Studies, 21, 265-310. Abstract

  This article develops and estimates a dynamic arbitrage-free model of the current forward curve as the sum of (i) an unconditional component, (ii) a maturity-specific component and (iii) a date-specific component. The model combines features of the Preferred Habitat model, the Expectations Hypothesis (ET) and affine yield curve models; it permits a class of low-parameter, multiple state variable dynamic models for the forward curve. We show how to construct alternative parametric examples of the three components from a sum of exponential functions, verify that the resulting
  forward curves satisfy the Heath-Jarrow-Morton (HJM) conditions, and derive the risk-neutral dynamics for the purpose of pricing interest rate derivatives. We select a model from alternative affine examples that are fitted to the Fama-Bliss Treasury data over an initial training period and use it to generate out-of-sample forecasts for forward rates and yields. For forecast horizons of 6 months or longer, the forecasts of this model significantly outperform those from common benchmark models.

Past Courses

• FNCE1000 - Corporate Finance

  This course provides an introduction to the theory, the methods, and the concerns of corporate finance. The concepts developed in FNCE 1000 form the foundation for all elective finance courses. The main topics include: 1) the time value of money and capital budgeting techniques; 2) uncertainty and the trade-off between risk and return; 3) security market efficiency; 4) optimal capital structure, and 5) dividend policy decisions. ACCT 1010 + STAT 1010 may be taken concurrently.

• FNCE1008 - Corporate Finance (Honors)

  This course provides an introduction to the theory, the methods, and the concerns of corporate finance. The concepts developed in FNCE 1000 form the foundation for all elective finance courses. The main topics include: 1) the time value of money and capital budgeting techniques; 2) uncertainty and the trade-off between risk and return; 3) security market efficiency; 4) optimal capital structure, and 5) dividend policy decisions. ACCT 1010 + STAT 1010 may be taken concurrently. Honors sections require MATH 1400 or MATH 1070 as a prerequisite. Application process.

• FNCE2170 - Financial Derivatives

  This course covers one of the most exciting and fundamental areas in finance. Financial derivatives serve as building blocks to understand broad classes of financial problems, such as complex asset portfolios, strategic corporate decisions, and stages in venture capital investing. The main objective of this course is build intuition and skills on (1) pricing and hedging of derivative securities, and (2) using them for investment and risk management. In terms of methodologies, we apply the non-arbitrage principle and the law of one price to dynamic models through three different approaches: the binomial tree model, the Black-Scholes-Merton option pricing model, and the simulation-based risk neutral pricing approach. The course covers a wide range of applications, including the use of derivatives in asset management, the valuation of corporate securities such as stocks and corporate bonds with embedded options, interest rate and credit derivatives, as well as crude oil derivatives. We emphasize practical considerations of implementing strategies using derivatives as tools, especially when no-arbitrage conditions do not hold. STAT 1020 may be taken concurrently.

• FNCE7170 - Financial Derivatives

  This course covers one of the most exciting and fundamental areas in finance. Financial derivatives serve as building blocks to understand broad classes of financial problems, such as complex asset portfolios, strategic corporate decisions, and stages in venture capital investing. The main objective of this course is build intuition and skills on (1) pricing and hedging of derivative securities, and (2) using them for investment and risk management. In terms of methodologies, we apply the non-arbitrage principle and the law of one price to dynamic models through three different approaches: the binomial tree model, the Black-Scholes-Merton option pricing model, and the simulation-based risk neutral pricing approach. The course covers a wide range of applications, including the use of derivatives in asset management, the valuation of corporate securities such as stocks and corporate bonds with embedded options, interest rate and credit derivatives, as well as crude oil derivatives. We emphasize practical considerations of implementing strategies using derivatives as tools, especially when no-arbitrage conditions do not hold.

• FNCE8920 - Financial Engineering

  This class covers advanced pricing models for equity, fixed income and credit derivatives. It aims at: 1) Introducing the main models used in practical applications to price and hedge derivatives; 2) Understanding their comparative advantages and limitations, as well as how they are calibrated and applied. As part of team assignments, students will be asked to calibrate and implement the models introduced in the class using software of their choice.

• FNCE8990 - Independent Study

  Independent Study Projects require extensive independent work and a considerable amount of writing. ISP in Finance are intended to give students the opportunity to study a particular topic in Finance in greater depth than is covered in the curriculum. The application for ISP's should outline a plan of study that requires at least as much work as a typical course in the Finance Department that meets twice a week. Applications for FNCE 8990 ISP's will not be accepted after the THIRD WEEK OF THE SEMESTER. ISP's must be supervised by a Standing Faculty member of the Finance Department.

Knowledge @ Wharton

• College Football Playoffs with Bill Connelly, Knowledge @ Wharton - 01/08/2025
• The Real Estate Market Survived Until 2025. What’s Next?, Knowledge @ Wharton - 01/07/2025
• AI in 2025: What Challenges Lie Ahead?, Knowledge @ Wharton - 01/07/2025
• Jeremy Siegel: 2025 Outlook for Stocks and the Economy, Knowledge @ Wharton - 01/07/2025
• What’s Ahead for Real Estate in 2025?, Knowledge @ Wharton - 01/07/2025
• Confidence in Cryptocurrency | David Reibstein and John Zhang (Part 1), Knowledge @ Wharton - 01/07/2025
• What’s Ahead for the Economy in 2025, Knowledge @ Wharton - 01/06/2025
• How Did AI Grow in 2024 and What’s in Store for 2025?, Knowledge @ Wharton - 01/02/2025
• NFL Data Science with Tom Bliss, Knowledge @ Wharton - 12/25/2024
• Overwhelmed by Too Many Choices When Shopping? Wharton Retail Expert Weighs In, Knowledge @ Wharton - 12/19/2024