Term Structure of Interest Rates under Long-Run Risks
and Incomplete Information (with Ji Zhou)
In this paper we
derive the term structure of default-free zero rates under the Epstein-Zin utility function, non-i.i.d.
consumption growth, and incomplete information about fundamentals. We extend
the continuous-time long-run risks model of Eraker
(2008) to an incomplete information environment in which agents learn about
unobservable persistent component of the conditional mean of consumption
growth. In equilibrium, agents learn about the conditional mean of consumption
growth and price zero-coupon bonds simultaneously under a new measure, which is
generated by information observed by agents. We derive analytic formulas for a
zero coupon bond price and that for spot rates under the new measure.
Recursive Estimation
for Continuous Time Stochastic Volatility Models using the Milstein
Approximation (A. Thavaneswaran and T. Koulis) forthcoming in the Journal of Mathematical Finance
Optimal as well as recursive parameter estimation for semimartingales had been studied in Thavaneswaran
and Thompson [1, 2]. Recently, there has been a growing interest in modeling
volatility of the observed process by nonlinear stochastic processes (Taylor
[3]). In this paper, we study the recursive estimates for various classes of
discretely sampled continuous time stochastic volatility models using the
Milstein method. We provide closed form expressions for the recursive estimates
for recently proposed stochastic volatility models. We also give an example of
computation of the term structure of zero rates in an incomplete information
environment. In this case, learning about an unobserved state variable is done
jointly with the valuation procedure.
Intertemporal Asset Pricing with Information Quality Risk
(with Gady Jacoby, Yan Wang, and Gemma Lee)
This paper provides a novel theoretical
platform for the pricing of imprecise accounting information as a systematic
market risk. Our intertemporal asset pricing model
shows that systematic information-quality risk is priced through a distinct
market risk premium and three extra betas associated with an
imprecise-information risk. Our first information-quality beta is related to
the covariance between market-wide imprecise-information return error and
security precise return. Together with the separate market information-quality
risk premium, this beta provides the theoretical underpinning for a separate
market information-quality factor in the spirit of empirical multiple-factor
model prevalent in the literature. The second extra beta (linked to the
covariance between firm and market-wide imprecise-information return errors),
represents the commonality in information quality, which is priced by investors
seeking to curtail adverse effects of imprecise accounting information on their
portfolio value. Our third information-quality beta (related to the covariance
between stock imprecise-information return error and overall market return),
implies that – for hedging purposes – investors prefer to invest in stocks
issued by firms that tend to, erroneously or deliberately, release false
positive information about the firm when the market is bearish. Our model is
strongly supported by empirical evidence.
A
Generalized Earnings-Based Stock Valuation Model with Learning (with Gady
Jacoby and Yan Wang)
This paper extends a recent generalized
complete information stock valuation model with incomplete information
environment. In practice, mean earnings-per-share growth rate (MEGR) is random
and unobservable. Therefore, asset prices should reflect how investors learn
about the unobserved state variable. In our model investors learn about MEGR in
continuous time. Firm characteristics, such as stronger mean reversion and
lower volatility of MEGR, make learning faster and easier. As a result, the
magnitude of risk premium due to uncertainty about MEGR declines over learning
horizon and converges to a long-term steady level. Due to the stochastic nature
of the unobserved state variable, complete learning is impossible (except for
cases with perfect correlation between earnings and MEGR). As a result, the
risk premium is non-zero at all times reflecting a persistent uncertainty that
investors hold in an incomplete information environment.
Debt
Valuation with Endogenous Default and Chapter 11 Reorganization
Continuous-time structural model of the bargaining process in Chapter 11
as the debtor's ultimatum offers to the creditors. Calibration a la
Huang and Huang (2002) shows that although credit spreds
are higher than in Leland and Toft one needs sizeable
deviations from Absolute Priority Rules for the model to produce realistic
results.
New empirical implications of the model with regards to the
expected time in bankruptcy as a function of different firm characteristics.
Bayesian Estimation
of Asset Return Dynamics Using Information in Both Option and Asset Prices
(with Christopher Lamoureux)
In this paper we derive the (exact, discrete-time) joint transition density of returns and volatilities under Heston's (1993) stochastic volatility option pricing model to identify all model parameters and state variables. All time series and cross-sectional information is used, and since we use the joint transition density, no approximations are needed. We use Markov Chain Monte Carlo methods to construct predictive densities of implied volatilities for our panel of options. This allows us to confirm well-known properties in a formal statistical sense, and confirm that tensions between the model and the data transcend parameter and state variable uncertainty.