- BOOK: Financial Economics: Classics and Contemporary
Forthcoming, MIT Press, approx. 1,000 pages
A graduate level book on Financial Economics
From the Introduction: This book originates from a set of notes I wrote in support of graduate and advanced undergraduate lectures in financial economics, macroeconomic dynamics, financial econometrics and financial engineering. These notes have circulated for about 20 years under the title Lectures on Financial Economics. The book aims to track historical intellectual developments in the field of Financial Economics and to illustrate the interactions of ideas and theories with actual markets behaviors, attempting at a synthesis of the state of knowledge accumulated during more than 65 years of contributions to the field.
- “Interest Rate Derivatives and Volatility” (with Yoshiki Obayashi)
Handbook of Fixed-Income Securities: Chapter 20, 767-838 (2016)
Handbook Series in Financial Engineering and Econometrics. John Wiley & Sons (Editor Pietro Veronesi)
Surveys interest rate derivatives and their use to hedge against fixed income volatility — a mix of applied and theory pieces
From the Introduction: Interest rate volatility (IRV) affects a wide base of individuals, investors, companies, and even governments. Individuals who have borrowed through adjustable-rate retail products, such as student loans and mortgages, are susceptible to greater uncertainty about the magnitude of their liabilities from one payment period to the next when short-term interest rates are volatile. For individual and institutional investors in fixed-income products like corporate bonds and mortgage-backed securities, IRV translates directly into undesirable, and sometimes devastating, portfolio volatility. Routine issuers of debt, such as financial institutions, corporations, and governmental agencies, are also forced to deal with the impact of IRV on their vital funding decisions. This chapter aims to equip the reader with a foundational understanding of the vast IRD market and the quantitative tools for measuring and managing IRV.
- BOOK: The Price of Fixed Income Market Volatility (with Yoshiki Obayashi)
Springer Verlag: Springer Finance Series, New York (2015), 250 pages
Develops unifying foundations on fixed income volatility pricing and variance swap design — theory piece with applications
From the Preface: The volatility of major asset classes is a key driver of portfolio performance affecting institutional and individual investors alike. Portfolio volatility may be managed by diversification through security selection decisions as well as by derivatives supplying direct exposure to volatility. Volatility derivatives and methodologies underlying their designs and pricing have been well-developed for equity markets, while fixed income markets have lagged in this respect despite their principal role in capital markets. This book aims to narrow this gap. While the book exposition is of a theoretical nature, its ultimate objective is to serve as a foundation upon which a market for standardized fixed income volatility trading may be built. In fact, some of the interest rate volatility index designs proposed in this book have already been brought to life in the US and in Japan—by far the two largest government bond markets by notional outstanding. The book presents a unified volatility evaluation framework and in-depth accounts of its application to four major fixed income asset sub-classes: interest rate swaps, government bonds, time deposits, and credit.
- “Uncertainty, Information Acquisition and Price Swings in Asset Markets” (with Francesco Sangiorgi)
Review of Economic Studies 82, 1533-1567 (2015)
In asset markets with uncertainty that cannot be quantified probabilistically (“Knightian uncertainty”), the value of information increases precisely as markets become more efficient. Overturns Grossman and Stiglitz (1980) — theory piece
Abstract: This article analyses costly information acquisition in asset markets with Knightian uncertainty about the asset fundamentals. In these markets, acquiring information not only reduces the expected variability of the fundamentals for a given distribution (i.e. risk). It also mitigates the uncertainty about the true distribution of the fundamentals. Agents who lack knowledge of this distribution cannot correctly interpret the information other investors impound into the price. We show that, due to uncertainty aversion, the incentives to reduce uncertainty by acquiring information increase as more investors acquire information. When uncertainty is high enough, information acquisition decisions become strategic complements and lead to multiple equilibria. Swift changes in information demand can drive large price swings even after small changes in Knightian uncertainty.
- “Rates Fears Gauges and the Dynamics of Fixed Income and Equity Volatilities” (with Yoshiki Obayashi and Catherine Shalen)
Journal of Banking and Finance 52, 256-265 (2015)
Uncovers salient empirical features of forward looking gauges of interest rate volatility against equity — applied piece
Abstract: While CBOE’s VIX index is widely acknowledged as a broad-based investor ‘‘fear gauge’’ for its strong inverse relationship with major equity indexes, one cannot necessarily expect it to translate to the level of future turbulence or investor risk-aversion in fixed-income markets. Indeed, expected volatilities in equity and interest rate markets as measured respectively by CBOE’s VIX and their newly launched swap rate volatility index, the SRVX, exhibit significantly distinct behavior. The two indexes react to different events and risk factors, thereby providing investors with complementary diversification, hedging, and risk-taking tools.
- “Interest Rate Variance Swaps and the Pricing of Fixed Income Volatility” (with Yoshiki Obayashi)
GARP Risk Professional: Quant Perspectives, March 1-8 (2014)
Develops unifying methodology to price fixed income volatility in a model-free fashion in 8 pages — theory piece
From the Introduction: One of the pillars supporting the recent movement toward standardized measurement and trading of interest rate volatility is a novel theory of options-based model-free fixed income volatility pricing. The meaning of this mouthful is best understood by working backwards: “fixed income volatility pricing” refers to the valuation of a contract with payoffs tied to a specific measure of realized variance of an underlying fixed income instrument (generically, a “variance swap”); “model-free” signifies the absence of reliance on modeling assumptions beyond specification of standard price dynamics and absence of arbitrage; and “options-based” relates to the valuation technique of spanning variance swap payoffs with those of options on the same underlying. This article provides an overview of how volatility pricing and indexing methodologies work for options on four major fixed income contracts.
- “Financial Volatility and Economic Activity” (with Fabio Fornari)
Journal of Financial Management, Markets and Institutions 1, 155-198 (2013)
Stock market volatility forecasts economic activity — applied piece (∼ written in 2005)
Abstract: Does uncertainty in capital markets affect the business cycle? We find that financial volatility predicts 30% of post-war economic activity in the United States, and that during the Great Moderation, aggregate stock market volatility explains, alone, up to 55% of real growth. In out-of-sample tests, stock price volatility helps predict turning points over and above traditional financial variables such as credit or term spreads, and other leading indicators. Combining stock volatility and the term spread leads to a proxy for (i) aggregate risk, (ii) risk-premiums and (iii) a gauge of monetary policy conduct, which tracks and anticipates the business cycle. At the heart of our analysis is a notion of volatility based on a slowly changing measure of return variability. This volatility is designed to capture long-run uncertainty in capital markets, and is particularly successful at explaining trends in the economic activity at horizons of six months and one year.
- “Macroeconomic Determinants of Stock Volatility and Volatility Premiums” (with Valentina Corradi and Walter Distaso)
Journal of Monetary Economics 60, 203-220 (2013)
How aggregate stock market volatility and volatility premiums link to business cycles in a no-arbitrage framework — applied piece
Abstract: How does stock market volatility relate to the business cycle? We develop, and estimate, a no-arbitrage model, and find that (i) the level and fluctuations of stock volatility are largely explained by business cycle factors and (ii) some unobserved factor contributes to nearly 20% to the overall variation in volatility, although not to its ups and downs. Instead, this ‘‘volatility of volatility’’ relates to the business cycle. Finally, volatility risk-premiums are strongly countercyclical, even more than stock volatility, and partially explain the large swings of the VIX index during the 2007–2009 subprime crisis, which our model captures in out-of-sample experiments.
- “Adding and Subtracting Black-Scholes: A New Approach to Approximating Derivative Prices in Continuous-Time Models” (with Dennis Kristensen)
Journal of Financial Economics 102, 390-415 (2011)
A new method to calculate derivative prices in models without a closed-form solution — theory piece
Abstract: We develop a new approach to approximating asset prices in the context of continuous- time models. For any pricing model that lacks a closed-form solution, we provide a closed-form approximate solution, which relies on the expansion of the intractable model around an ‘‘auxiliary’’ one. We derive an expression for the difference between the true (but unknown) price and the auxiliary one, which we approximate in closed-form, and use to create increasingly improved refinements to the initial mispricing induced by the auxiliary model. The approach is intuitive, simple to implement, and leads to fast and extremely accurate approximations. We illustrate this method in a variety of contexts including option pricing with stochastic volatility, computation of Greeks, and the term structure of interest rates.
- “Information Linkages and Correlated Trading” (with Paolo Colla)
Review of Financial Studies 23, 203-246 (2010)
Asset markets in the presence of information networks amongst agents — theory piece
Abstract: In a market with informationally connected traders, the dynamics of volume, price informativeness, price volatility, and liquidity are severely affected by the information linkages every trader experiences with his peers. We show that in the presence of information linkages among traders, volume and price informativeness increase. Moreover, we find that information linkages improve or damage market depth, and lower or boost the traders’ profits, according to whether these linkages convey positively or negatively correlated signals. Finally, our model predicts patterns of trade correlation consistent with those identified in the empirical literature: trades generated by “neighbor” traders are positively correlated and trades generated by “distant” traders are negatively correlated.
- “Simulated Nonparametric Estimation of Dynamic Models” (with Filippo Altissimo)
Review of Economic Studies 76, 413-450 (2009)
A new estimator that achieves the same asymptotic efficiency as maximum likelihood — theory piece with finance in view
Abstract: This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel, which considerably simplifies bandwidth selection for the purpose of implementing the estimator. Furthermore, the SNE displays the same asymptotic efficiency properties as the maximum-likelihood estimator as soon as the model is Markov in the observable variables. The methods introduced in this paper are fairly simple to implement, and possess finite sample properties that are well approximated by the asymptotic theory. We illustrate these features within typical estimation problems that arise in financial economics.
- “Asymmetric Stock Market Volatility and the Cyclical Behavior of Expected Returns”
Journal of Financial Economics 86, 446-478 (2007)
Why is stock market volatility countercyclical? — theory piece
Abstract: Recent explanations of aggregate stock market fluctuations suggest that countercyclical stock market volatility is consistent with rational asset evaluations. In this paper, I develop a framework to study the causes of countercyclical stock market volatility. I find that countercyclical risk premia do not imply countercyclical return volatility. Instead, countercyclical stock volatility occurs if risk premia increase more in bad times than they decrease in good times, thereby inducing price–dividend ratios to fluctuate more in bad times than in good. The business cycle asymmetry in the investors’ attitude toward discounting future cash flows plays a novel and critical role in many rational explanations of asset price fluctuations.
- “Approximating Volatility Diffusions with CEV-ARCH Models” (with Fabio Fornari)
Journal of Economic Dynamics and Control 30, 931-966 (2006)
A simple method to estimate/calibrate models with stochastic volatility — applied piece
Abstract: This article develops a new model which allows volatility to react nonlinearly to past shocks as a function of the past volatility level. We show that this model approximates any CEV-diffusion model for stochastic volatility, and we judge its empirical performance as a diffusion approximation to models of the short-term rate with stochastic volatility and as a filter of the unobserved volatility. The estimation of the continuous time scheme to which the discrete time model converges can be safely based on simple moment conditions linking the discrete time to the continuous time parameters. Monte-Carlo studies reveal that the filtering performances of the model are robust to the presence of serious misspecification.
- “Fundamental Properties of Bond Prices in Models of the Short-Term Rate”
Review of Financial Studies 16, 679-716 (2003)
Volatility and the yield curve — theory piece
Abstract: This article develops restrictions that arbitrage-constrained bond prices impose on the short-term rate process in order to be consistent with given dynamic properties of the term structure of interest rates. The central focus is the relationship between bond prices and the short-term rate volatility. In both scalar and multidimensional diffusion settings, typical relationships between bond prices and volatility are generated by joint restrictions on the risk-neutralized drift functions of the state variables and convexity of bond prices with respect to the short-term rate. The theory is illustrated by several examples and is partially extended to accommodate the occurrence of jumps and default.
- “Recovering the Probability Density Function of Asset Prices using GARCH as Diffusion Approximations” (with Fabio Fornari)
Journal of Empirical Finance 8, 83-110 (2001)
Derives market risk-aversion from the price of derivatives in the presence of stochastic volatility — applied piece
- “Volatility Smiles and the Information Content of News” (with Fabio Fornari)
Applied Financial Economics 11, 179-186 (2001)
Event studies regarding government bond implied vols — applied piece
- BOOK: Stochastic Volatility in Financial Markets—Crossing the Bridge to Continuous Time (with Fabio Fornari)
Springer Verlag (original ed.: Kluwer Academic Publishers), New York (2000), 145 pages
A survey of work on stochastic volatility in equity and fixed income markets — a mix of applied and theory pieces
- BOOK: Dynamiques non linéaires, volatilité et équilibre (in French)
Editions Economica, Paris (1998), 212 pages
Essays in continuous time finance, chaos theory and financial econometrics — drawn from my 1995 PhD dissertation
- “Sign and Volatility Switching ARCH Models” (with Fabio Fornari)
Journal of Applied Econometrics 12, 49-65 (1997)
Volatility reacts asymmetrically to both past shocks and past unexpected volatility — applied piece
- “Asymmetries and Non-Linearities in the Economic Activity” (with Fabio Fornari)
Applied Financial Economics 7, 203-206 (1997)
Business cycle volatility reacts asymmetrically to past macroeconomic shocks — applied piece
- “Weak Convergence and Distributional Assumptions for a General Class of Non Linear ARCH Models” (with Fabio Fornari)
Econometric Reviews 16, 205-227 (1997)
- “Modeling the Changing Asymmetry of Conditional Variances” (with Fabio Fornari)
Economics Letters 50, 197-203 (1996)
- “Continuous Time Conditionally Heteroskedastic Models: Theory with Applications to the Term Structure of Interest Rates” (with Fabio Fornari)
Economic Notes 24, 327-352 (1995)
- “A Stochastic Variance Model For Absolute Returns” (with Fabio Fornari)
Economics Letters 46, 211-214 (1994)
- “Stochastic Behavior of Deterministic Utility Functions”
Rivista internazionale di scienze economiche e commerciali 41, 1013-1031 (1994)
- “A Two Factor Arbitrage Model with Optimal Filtering Behavior” (with Fabio Fornari)
Statistica 54, 293-312 (1994)