Leonid Kogan
Nippon Telegraph and Telephone Professor of Management and Professor of Finance, MIT Sloan School of Management. Research Associate, National Bureau of Economic Research.
Bio
Leonid Kogan is the Nippon Telegraph and Telephone Professor of Management and a Professor of Finance at the MIT Sloan School of Management and a Research Associate at the National Bureau of Economic Research. His research covers theoretical and empirical topics in capital markets. Dr. Kogan has published extensively in leading academic journals, including the Journal of Finance, the Journal of Financial Economics, Review of Financial Studies, the Journal of Political Economy, and Operations Research. Dr. Kogan has won several professional awards, including the 1998 Lehman Brothers Fellowship for Research Excellence in Finance for his work on the asset pricing implications of investment irreversibility; the 2004 FAME Research Prize and the 2006 Smith Breeden Prize for his work on the price impact and survival of irrational traders; the 2007 Crowell Memorial Prize for his work on output durability and stock returns; the 2013 Crowell Memorial Prize for his work on technological innovation and growth; the 2014 Amundi Smith Breeden Prize for his work on the effect of technology shocks on stock prices; the 2020 NASDAQ award for the best paper on asset pricing at the WFA for his work on common fund flows; and the 2025 Crowell Memorial Prize (first prize) for his work on measuring creative destruction. He received his M.Sc. degree in mechanics and applied mathematics from the Moscow State University, a Ph.D. in mechanics from Cornell University, and a Ph.D. degree in finance from MIT.
Research
Working papers
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Kakhbod, A., L. Kogan, P. Li, and D. Papanikolaou (2026) — “Measuring Creative Destruction.”
Winner, 2025 Crowell Memorial Prize (first prize), PanAgora Asset Management. Constructs a firm-level measure of creative destruction from the similarity between a firm’s 10-K technology description and other firms’ patented innovations, and finds that higher displacement predicts declines in firm profits, revenue, employment, and capital, while industries with more creative destruction show greater dispersion in firm growth.
Abstract
We construct a firm-level measure of creative destruction from the similarity between a firm’s 10K technology description and other firms’ patented innovations. Motivated by a simple model, we identify two channels of displacement, one in the product market and one through the firm’s production technologies, and build a composite measure summarizing both. Higher composite displacement is associated with significant declines in firm profits, revenue, employment, and capital. We find no evidence that industry-level creative destruction raises subsequent growth, but we do see significant reallocation: industries with more creative destruction experience greater dispersion in firm growth rates.
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Green, B., L. Kogan, D. Papanikolaou, and L. Schmidt (2026) — “Winners and Losers: Competition, Creative Destruction, and Labor Income Risk.”
Uses U.S. administrative data to show that technology-driven creative destruction passes through to worker earnings asymmetrically: rival innovations cause larger earnings losses than own-firm innovations cause gains, and top earners are most exposed. Subsumes the earlier paper “Technological Innovation and Labor Income Risk” (also circulated as “Technological Innovation and the Distribution of Labor Income Growth”) by Kogan, Papanikolaou, Schmidt, and Song.
Abstract
Using U.S. administrative data, we find that technology-driven creative destruction in the product market passes through to worker earnings. The passthrough to incumbent worker earnings is both asymmetric and concentrated: profit drops from rival innovations lead to proportionally greater earning declines and changes in the likelihood of job destruction than profit gains from their own firm’s innovations, while top workers are significantly more exposed than the average worker. We develop an endogenous-growth model with monopsonistic labor markets and worker heterogeneity that replicates this asymmetry and the distribution of earnings risk. Creative destruction exposes high-income workers to concentrated downside risk while increasing upward mobility for lower-income workers, shaping the welfare consequences of innovation policy. (This paper subsumes the earlier paper “Technological Innovation and Labor Income Risk” (also circulated as “Technological Innovation and the Distribution of Labor Income Growth”), by Kogan, Papanikolaou, Schmidt, and Song.)
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Huang, Q., L. Kogan, and D. Papanikolaou (2026) — “Inflation and Innovation.”
Uses state-level R&D tax credits as an instrument to show that innovation raises local inflation, especially for non-tradables, and explains this with a multi-region model in which displacive innovation concentrates gains among innovators while non-innovative households bear a higher cost of living. (Previously circulated as “Productivity Shocks and Inflation in Incomplete Markets.”)
Abstract
Innovation raises productivity, yet it can also raise inflation when markets are incomplete. Exploiting state-level R&D tax credits as an instrument, we find that innovation raises local inflation, primarily in non-tradables. We rationalize this in a multi-region model where displacive innovation shocks reallocate output among agents; the concentrated gains from innovation raise local prices even as productivity rises. Non-innovative households bear the costs of innovation in the form of higher local prices. The calibrated model implies that an innovation shock raising output by 1 percent reduces welfare by approximately 7 percent. The model generates a realistic equity and value premium with modest risk aversion. Since local growth stocks hedge local inflation, households are willing to tilt their portfolios toward local growth stocks despite their low average returns. (Previously circulated as “Productivity Shocks and Inflation in Incomplete Markets.”)
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Cen, X., W. Dou, L. Kogan, and W. Wu (2026) — “Fund Flows and Income Risk of Fund Managers.”
Links U.S. active equity mutual fund managers to Census LEHD earnings records and finds that pay is anchored by fund scale, that return performance works mainly through bonuses, and that large fund outflows sharply raise the probability of turnover with a major pay decline.
Abstract
We construct the first large-scale dataset linking the compensation and career outcomes of U.S. active equity mutual fund managers to administrative earnings records from the U.S. Census Bureau’s LEHD data. Compensation is anchored primarily by fund scale: the elasticity of pay with respect to both AUM and fee revenue is about 0.18. Controlling for the mechanical effect through AUM, return performance affects pay mainly through bonuses rather than base pay. This distinction is important because the median bonus-to-pay ratio is 34%, which helps explain why return performance has limited additional explanatory power for total pay. Bonus shares are higher for managers who are more senior, more established, oversee larger funds, or have higher Morningstar ratings. Fund flows also matter beyond their effect through AUM: they affect compensation and strongly predict career outcomes. Large outflows raise the probability of turnover with a major compensation decline by about 4 percentage points. At the fund-family level, return performance and fund flows raise individual managers’ base pay, but not bonuses.
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Kogan, L., J. Li, H. Zhang, and Y. Zhu (2026) — “Operating Leverage and Risk Premium.”
Recipient of the 2023 CFRI & CIRF–Pacific-Basin Finance Journal Research Excellence Award (Finance Research) at the CFRI & CIRF Joint Conference. Introduces a machine-learning-based measure of firm-level operating leverage and finds that its relation to the risk premium is nonmonotonic rather than robustly positive, a pattern rationalized by a production-based model with variable and fixed costs. (Previously circulated as “Operating Leverage and Asset Pricing Anomalies.”)
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Cao, D., B. Falk, L. Kogan, and G. Tsoukalas (2025) — “A Structural Model of Automated Market Making.”
Develops and estimates a structural model of an automated market maker, shows that fixed fees are inefficient, and characterizes the optimal volatility-sensitive fee schedule, which raises fee revenue and liquidity supply in ETH-USDC data.
Abstract
Automated market makers (AMMs) process billions in annual transactions, yet most rely on fixed fee schedules that stand in contrast to microstructure theory, which prescribes volatility-sensitive spreads. To assess whether this theory extends to AMMs, we develop and estimate a structural model of an AMM. We show that fixed fees are inefficient and characterize the optimal volatility-sensitive fee schedule. Testing on ETH-USDC data shows that, even with noisy volatility forecasts, adaptive fees outperform fixed fees, increasing the annual fee revenue by 9–44%, and AMM liquidity supply by 2–10%.
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Huang, Q., L. Kogan, and D. Papanikolaou (2025) — “Tech Dollars: Technological Innovation and Exchange Rates.”
Winner, 2025 WRDS Outstanding Paper Award in Financial Institutions, MFA. Winner, 2025 China International Conference in Finance XiYue Best Paper Award. Documents a positive link between U.S. innovation, dollar appreciation, and foreign capital inflows, and explains it with a general equilibrium model in which foreign investors buy U.S. technology stocks to share in the gains from U.S. innovation.
Abstract
We document a positive link between U.S. innovation, dollar appreciation, and foreign capital inflows. To explain these patterns, we develop a general equilibrium model in which innovation-driven productivity gains accrue disproportionately to entrepreneurs. The calibrated version of the model replicates the joint dynamics of the dollar, equity returns, inequality, consumption and output growth, highlighting a new channel between innovation, exchange rates, and global capital flows. In our model, foreign investors invest in U.S. technology stocks to share the gains of U.S. innovation; the dollar appreciates not because it is a safe asset but as a claim on U.S. innovation.
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Kogan, L., D. Papanikolaou, L. Schmidt, and B. Seegmiller (2024) — “Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data.”
Develops text-based measures of exposure to labor-saving and labor-augmenting technologies and shows, using US administrative data, that labor-saving technologies reduce the earnings of exposed workers, while labor-augmenting technologies raise employment but lower the earnings of white-collar, older, and higher-paid incumbents.
Abstract
We develop measures of labor-saving and labor-augmenting technology exposure using textual analysis of patents and job tasks. Using US administrative data, we show that exposure to labor-saving technologies negatively affects the earnings of exposed workers. This negative effect is pervasive across both blue- and white-collar workers and across workers of different ages or earnings relative to their peers. In contrast, labor-augmenting technologies have a heterogeneous impact on exposed workers. While the wage bill paid to affected groups rises, this increase is driven primarily by an increase in employment, while earnings rise for new entrants but decline for incumbent workers. This decline is primarily present among white-collar, older, and higher-paid workers, highlighting the importance of vintage-specific human capital. Last, we find positive spillovers of both types of innovation at the industry level, benefiting other workers in the same industry who are not directly exposed to these innovations.
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Kogan, L., J. Li, and X. Qiao (2023) — “Asset Growth Effect and Q Theory of Investment.”
Extends q theory into a two-capital setup and shows it explains the stronger return predictive power of total asset growth than current and long-term asset growths.
Abstract
The recent linear factor models (e.g., Fama and French (2015) and Hou, Xue, and Zhang (2015)) use total asset growth as the measure of investment, largely due to its stronger return predictive power than its components such as the long-term and current asset growths. We offer an explanation of the latter finding by extending the standard q theory of investment into a two-capital setup in which firms use both long-term and current asset as production inputs. We uncover a novel asset imbalance channel which creates negative comovement between current and long-term asset growths that are unrelated to discount rate. This comovement is muted in the total asset growth, giving rise to its stronger return prediction. Once controlling for this comovement, the return predictive power of current and long-term asset growths substantially improves. Furthermore, we document strong evidences for the model’s prediction that the asset growth effects are more prominent among firms with low asset imbalance. Our results support the q theory based explanation for the asset growth effect.
Permanent working papers
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Fanti, G., L. Kogan, and P. Viswanath (2022) — “Economics of Proof-of-Stake Payment Systems.”
Develops a valuation framework for Proof-of-Stake payment systems and shows that high token valuation relative to transaction flow is central to network security, while valuation bubbles undermine it.
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Kogan, L., and M. Tian (2015) — “Firm Characteristics and Empirical Factor Models: A Model-Mining Experiment.”
Constructs three-factor linear pricing models that match return spreads for many firm characteristics and evaluates the sensitivity of factor model performance to sample and methodology.
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Haugh, M., L. Kogan, and Z. Wu (2006) — “Portfolio Optimization with Position Constraints: An Approximate Dynamic Programming Approach.”
Analyzes dynamic portfolio choice using an ADP algorithm with borrowing constraints and a duality-based simulation procedure.
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Kogan, L., and T. Wang (2003) — “A Simple Theory of Asset Pricing under Model Uncertainty.”
Characterizes implications of model uncertainty for the cross-section of returns in a single-period mean-variance framework.
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Kogan, L., and R. Uppal (2001) — “Risk Aversion and Optimal Portfolio Policies in Partial and General Equilibrium Economies.”
Analyzes equilibrium policies and prices with stochastic investment opportunity set and incomplete markets using perturbation around log utility.
Publications
Refereed journal articles and selected other publications.
- Chen, Z., L. Kogan, A. W. Lo, Q. Xu, and R. Zhang (2026) “DORADO: Dynamic Optimization of R&D Options.” Management Science, forthcoming. Best Paper Award, 2026 Frontiers in Finance Conference.
Develops a dynamic strategy for managing a portfolio of R&D projects with correlated binary outcomes that uses project correlation as a knowledge discovery mechanism, and shows that portfolio value is maximized at either very low or very high correlations. (Previously circulated as “Optimizing a Portfolio of R&D Projects: A Real Options Approach.”)
- Kogan, L., and I. Mitra (2025) “Near-Rational Equilibria in Heterogeneous-Agent Models: A Verification Method.” Review of Financial Studies, 38, 2227–2274.
Proposes a simulation-based verification method to bound welfare losses under approximate policies in heterogeneous-agent equilibrium models.
- Chen, H., W. Dou, and L. Kogan (2024) “Measuring the ‘Dark Matter’ in Asset Pricing Models.” Journal of Finance, 79, 843–902. Best Paper Award, 2013 Red Rock Finance Conference.
Formalizes a measure of “dark matter” in asset pricing models that captures model fragility, refutability, and out-of-sample fit.
- John, K., L. Kogan, and S. Fahad (2023) “Smart Contracts and Decentralized Finance.” Annual Review of Financial Economics, 15, 523–542.
Reviews smart contract mechanics, their benefits and limitations, and major DeFi applications including decentralized exchanges and lending protocols.
- Kogan, L., J. Li, and H. Zhang (2023) “Operating Hedge and Gross Profitability Premium.” Journal of Finance, 78, 3387–3422.
Shows that variable costs create an operating hedge that reduces firms’ cash flow risk and that the gross profitability premium arises because this effect is weaker for more profitable firms.
- Dou, W., L. Kogan, and W. Wu (2023) “Common Fund Flows: Flow Hedging and Factor Pricing.” Journal of Finance, forthcoming. 2020 NASDAQ Award for the Best Paper on Asset Pricing, WFA.
Shows that common fund flows earn a risk premium and that funds hedge by tilting portfolios toward low-flow-beta stocks.
- Chen, W., M. Khan, L. Kogan, and G. Serafeim (2021) “Cross-Firm Return Predictability and Accounting Quality.” Journal of Business Finance & Accounting, 48, 70–101.
Shows that returns of high accounting-quality firms predict returns of matched low-quality firms, consistent with delayed information processing.
- Kogan, L., D. Papanikolaou, and N. Stoffman (2020) “Left Behind: Creative Destruction, Inequality, and the Stock Market.” Journal of Political Economy, 128, 855–906.
Shows that innovation gains are distributed asymmetrically because innovators cannot sell claims on future ideas, and this explains asset pricing patterns that representative-agent models miss.
- Kogan, L., and D. Papanikolaou (2019) “Technological Innovation, Intangible Capital, and Asset Prices.” Annual Review of Financial Economics, 11, 221–242.
Reviews the link between technological innovation, intangible capital, and asset prices.
- Fanti, G., L. Kogan, S. Oh, K. Ruan, P. Viswanath, and G. Wang (2019) “Compounding of Wealth in Proof-of-Stake Cryptocurrencies.” In I. Goldberg and T. Moore (eds.), Financial Cryptography and Data Security (FC 2019), Lecture Notes in Computer Science, 11598. Springer.
Analyzes wealth compounding and security in Proof-of-Stake cryptocurrency protocols.
- Kogan, L., and D. Papanikolaou (2018) “Equilibrium Analysis of Asset Prices: Lessons from CIR and APT.” Journal of Portfolio Management, 44, 59–69.
Draws lessons from CIR and APT for equilibrium asset pricing and factor models.
- Kogan, L., D. Papanikolaou, A. Seru, and N. Stoffman (2017) “Technological Innovation, Resource Allocation, and Growth.” Quarterly Journal of Economics, 132, 665–712. 2013 Crowell Memorial Prize (second prize).
Constructs a measure of innovation value from patent data and stock market responses and shows it accounts for significant growth, reallocation, and firm-level growth predictability.
- Kogan, L., S. Ross, J. Wang, and M. Westerfield (2017) “Market Selection.” Journal of Economic Theory, 168, 209–236.
Establishes conditions for trader survival and price impact and shows that survival and price impact are distinct—traders with poor forecasts can affect prices even with negligible wealth.
- Huynh, V. A., L. Kogan, and E. Frazzoli (2014) “A Martingale Approach and Time-Consistent Sampling-Based Algorithms for Risk Management in Stochastic Optimal Control.” Proceedings of the 53rd IEEE Conference on Decision and Control (CDC), 1858–1865.
Develops martingale-based, time-consistent algorithms for risk management in stochastic control.
- Kogan, L., and D. Papanikolaou (2014) “Growth Opportunities, Technology Shocks, and Asset Prices.” Journal of Finance, 69, 675–718. 2014 Amundi Smith Breeden Prize (first prize).
Shows that investment-specific technology shocks affect assets in place and growth opportunities differently, generating a value factor and cross-sectional return predictability.
- Kogan, L., and D. Papanikolaou (2013) “Firm Characteristics and Stock Returns: The Role of Investment-Specific Shocks.” Review of Financial Studies, 26, 2718–2759.
Shows that firm characteristics and average return differences are driven by exposure to investment-specific technology shocks in a calibrated general equilibrium model.
- Kogan, L., and D. Papanikolaou (2012) “Economic Activity of Firms and Asset Prices.” Annual Review of Financial Economics, 4, 361–384.
Reviews how firm-level economic activity and real investment relate to asset prices.
- Khan, M., L. Kogan, and G. Serafeim (2012) “Mutual Fund Trading Pressure: Firm-Level Stock Price Impact and Timing of SEOs.” Journal of Finance, 67, 1371–1395. Runner-up, 2013 Whitebox Prize for Best Financial Research.
Studies how mutual fund flows affect firm-level stock prices and the timing of seasoned equity offerings.
- Garleanu, N., L. Kogan, and S. Panageas (2012) “Displacement Risk and Asset Returns.” Journal of Financial Economics, 105, 491–510. Best Paper Award, 2011 Utah Winter Finance Conference.
Shows that innovation creates displacement risk because of limited intergenerational risk sharing and that this risk explains the value premium and high equity premium.
- Kogan, L., and D. Papanikolaou (2010) “Growth Opportunities and Technology Shocks.” American Economic Review: Papers & Proceedings, 100, 532–536.
Summarizes the link between growth opportunities, technology shocks, and asset prices.
- Gomes, J., L. Kogan, and M. Yogo (2009) “Durability of Output and the Cross-Section of Stock Returns.” Journal of Political Economy, 117, 941–986. 2007 Crowell Memorial Prize (first prize).
Shows that durable-good producers face higher systematic risk because demand for durables is more cyclical, explaining cross-sectional return patterns.
- Kogan, L., D. Livdan, and A. Yaron (2009) “Oil Futures Prices in a Production Economy with Investment Constraints.” Journal of Finance, 64, 1345–1375.
Models oil futures prices in a production economy with investment constraints and commodity storage.
- Kogan, L. (2008) “A Dynamic Default Correlation Model.” Quantitative Credit Research Quarterly, Q2/3, Lehman Brothers.
Develops a dynamic model of default correlation for credit portfolios.
- Kogan, L., and V. Konstantinovsky (2008) “Commodity Futures and Inflation.” Global Relative Value, Lehman Brothers.
Analyzes the relationship between commodity futures and inflation.
- Kogan, L., and M. Haugh (2007) “Duality Theory and Approximate Dynamic Programming for Pricing American Options and Portfolio Optimization.” In J. Birge and V. Linetsky (eds.), Handbooks in Operations Research and Management Science: Financial Engineering, 15, Ch. 23. Elsevier.
Presents duality-based and approximate dynamic programming methods for American option pricing and portfolio choice.
- Kogan, L., I. Makarov, and R. Uppal (2007) “The Equity Risk Premium and the Riskfree Rate in an Economy with Borrowing Constraints.” Mathematics and Financial Economics, 1, 1–19. Lead article, inaugural issue.
Characterizes the equity premium and risk-free rate in economies with borrowing constraints.
- Kogan, L., S. Ross, J. Wang, and M. Westerfield (2006) “The Price Impact and Survival of Irrational Traders.” Journal of Finance, 61, 195–229. 2006 Smith Breeden Prize (first prize). 2004 FAME Research Prize.
Analyzes the price impact and long-run survival of irrational traders in competitive asset markets.
- Haugh, M., L. Kogan, and J. Wang (2006) “Evaluating Portfolio Policies: A Duality Approach.” Operations Research, 54, 405–418.
Uses duality to evaluate portfolio policies and bound their performance.
- Kogan, L. (2004) “Asset Prices and Real Investment.” Journal of Financial Economics, 73, 411–432. Lead article.
Shows how irreversibility and adjustment costs in real investment determine the dynamics of stock returns and the relation between market-to-book and returns.
- Haugh, M., and L. Kogan (2004) “Pricing American Options: A Duality Approach.” Operations Research, 52, 258–270.
Develops a duality approach for pricing American options and computing exercise boundaries.
- Gomes, J., L. Kogan, and L. Zhang (2003) “Equilibrium Cross-Section of Returns.” Journal of Political Economy, 111, 693–732. Lead article.
Shows that in a dynamic general equilibrium production economy, the conditional CAPM characterizes returns and size and book-to-market are correlated with conditional market beta.
- Chan, Y., and L. Kogan (2002) “Catching Up with the Joneses: Heterogeneous Preferences and the Dynamics of Asset Prices.” Journal of Political Economy, 110, 1255–1285.
Studies asset prices in an economy with heterogeneous agents who have catching-up-with-the-Joneses preferences and shows how wealth distribution affects prices and trading.
- Kogan, L. (2001) “An Equilibrium Model of Irreversible Investment.” Journal of Financial Economics, 62, 201–245. Lead article.
Builds an equilibrium model of irreversible investment and links it to the cross-section of returns.
- Bertsimas, D., L. Kogan, and A. Lo (2001) “Hedging Derivative Securities and Incomplete Markets: An Epsilon-Arbitrage Approach.” Operations Research, 49, 372–397.
Develops an epsilon-arbitrage approach to hedging derivatives in incomplete markets.
- Bertsimas, D., L. Kogan, and A. Lo (2000) “When Is Time Continuous?” Journal of Financial Economics, 55, 173–204.
Characterizes when discrete-time models converge to continuous-time limits in asset pricing.
Teaching
Teaching objectives
My teaching aims to connect rigorous theory with practical applications in finance. I emphasize quantitative methods, dynamic models, and empirical inference so that students can both understand and implement state-of-the-art tools used in asset pricing, risk management, and financial engineering.
Mentoring
I enjoy working with Ph.D. students on theoretical and empirical research in asset pricing, innovation, and macro-finance. I aim to support students in developing independent research agendas and in placing at leading academic and industry institutions.
Recently taught courses
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Foundations of Modern Finance, 15.415
Core theory of capital markets and corporate finance. Topics include consumption-investment decisions, valuation theory, risk analysis, portfolio theory, pricing models of risky assets, and investment, financing and risk management decisions of firms. Restricted to Master of Finance Program.
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Advanced Financial Economics I, 15.472J
Focuses on solving, estimating, and empirically evaluating theoretical models of asset prices and financial markets, as well as their microeconomic foundations and macroeconomic implications. Discusses theory and econometric methods, the state of the literature, and recent developments and empirical evidence. Covers topics such as cross-sectional and time-series models, consumption-based and intermediary-based models, financial institutions, household finance, housing, behavioral finance, financial crises, and continuous-time tools and applications. Students complete a short term paper and a presentation. Primarily for doctoral students in finance, economics, and accounting.
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Advanced Financial Economics I, 15.474J
Faculty present their current research in a wide variety of topics in finance. Provides a rapid overview of the literature, an in-depth presentation of selected contributions, and a list of potential research ideas for each topic. Faculty rotate every year to cover new topics. Primarily for doctoral students in accounting, economics, and finance.
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Financial Engineering, 15.456
Exposes students to the cutting edge of financial engineering. Includes a deep immersion into 'how things work,' where students develop and test sophisticated computational models and solve highly complex financial problems. Covers stochastic modeling, dynamic optimization, stochastic calculus and Monte Carlo simulation through topics such as dynamic asset pricing and investment management, market equilibrium and portfolio choice with frictions and constraints, and risk management. Assumes solid undergraduate-level background in calculus, probability, statistics, and programming and includes a substantial coding component. Classroom examples presented using Python and R.
Ph.D. Student Dissertation Committees
- Lu Zhang (2002; University of Rochester)
- Dmitry Livdan (2003; University of Houston)
- Mark Westerfield (2004; USC)
- Igor Makarov (2006; Chair; London Business School)
- Dimitris Papanikolaou (2007; Chair; Northwestern University)
- Oleg Rytchkov (2007; Temple University)
- Shawn Staker (2009; Chair; Deutsche Bank)
- Weiyang Qiu (2010; Citigroup)
- Kan Huang (2011; Two Sigma Investments)
- Fernando Duarte (2011; Chair; New York Federal Reserve Bank)
- Mary Tian (2011; Chair; Federal Reserve Board of Governors in Washington DC)
- Sahar Parsa (2011; Tufts University)
- Ngoc-Khanh Tran (2012; Chair; Washington University in St. Louis)
- Eung Jun Brandon Lee (2013; Chair; Goldman Sachs)
- Yichuan Li (2013; Rutgers University)
- Vu Anh Huynh (2014; Goldman Sachs)
- Zhe Zhu (2014; Munich Re)
- Indrajit Mitra (2015; Chair; University of Michigan)
- Yu Xu (2015; University of Hong Kong)
- Dejanir Silva (2016; University of Illinois at Urbana-Champaign)
- Alex Remorov (2016; BlackRock)
- Wei (Winston) Dou (2017; Chair; University of Pennsylvania, Wharton School)
- Victor Duarte (2018; Chair; Dallas Fed)
- Seung Kwak (2018; Chair; The Federal Reserve Board)
- Yixing Chen (2018; Co-chair; University of Rochester)
- Shomesh Chaudhuri (2018; Post-Doc, MIT)
- Anton Petukhov (2019; Co-chair; Citadel)
- Thomas Ernst (2020; University of Maryland)
- Ali Kakhbod (2021; Rice University)
- Nihal Koduri (2021)
- Qingyang Xu (2022; LinkedIn)
- Yury Olshanskiy (2024; Chair; Citadel)