Bruno Feunou - Latest
-
-
Generalized Autoregressive Gamma Processes
We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes in which each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. We show that using GARG processes reduces pricing errors by substantially more than using existing autoregressive gamma processes does. -
Secular Economic Changes and Bond Yields
We investigate the economic forces behind the secular decline in bond yields. Before the anchoring of inflation in the mid-1990s, nominal shocks drove inflation, output and bond yields. Afterward, the impacts of nominal shocks were much less significant. -
The Term Structures of Loss and Gain Uncertainty
We investigate the uncertainty around stock returns at different investment horizons. Since a return is either a loss or a gain, we categorize return uncertainty into two components—loss uncertainty and gain uncertainty. We then use these components to evaluate investment. -
Which Model to Forecast the Target Rate?
Specifications of the Federal Reserve target rate that have more realistic features mitigate in-sample over-fitting and are favored in the data. -
Variance Premium, Downside Risk and Expected Stock Returns
We decompose total variance into its bad and good components and measure the premia associated with their fluctuations using stock and option data from a large cross-section of firms. -
Risk-Neutral Moment-Based Estimation of Affine Option Pricing Models
This paper provides a novel methodology for estimating option pricing models based on risk-neutral moments. We synthesize the distribution extracted from a panel of option prices and exploit linear relationships between risk-neutral cumulants and latent factors within the continuous time affine stochastic volatility framework. -
Good Volatility, Bad Volatility and Option Pricing
Advances in variance analysis permit the splitting of the total quadratic variation of a jump diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions, and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions. -
Time-Varying Crash Risk: The Role of Stock Market Liquidity
We estimate a continuous-time model with stochastic volatility and dynamic crash probability for the S&P 500 index and find that market illiquidity dominates other factors in explaining the stock market crash risk. While the crash probability is time-varying, its dynamic depends only weakly on return variance once we include market illiquidity as an economic variable in the model. -
Tractable Term Structure Models
We introduce a new framework that facilitates term structure modeling with both positive interest rates and flexible time-series dynamics but that is also tractable, meaning amenable to quick and robust estimation.