We present a dynamic model of venture capital financing, described as a sequential investment problem with uncertain outcome. Each venture has a critical, but unknown threshold beyond which it cannot progress. If the threshold is reached before the completion of the project, then the project...

After analyzing retail investors' stock trades for potential learning behavior, we present evidence that individual investors learn from their trading experience. Initially, we question whether investors' previous forecasting ability (inferred from prior purchases' subsequent risk-adjusted...

This paper investigates whether individual investors adjust their stock trading according to their stock selection abilities, which can be inferred from their trading history. Fixed-effect panel regressions provide strong evidence that the ability to forecast future stock returns significantly...

In this paper, a Chover-type law of the iterated logarithm is established for the weighted sums of independent and identically distributed random variables with a distribution in the domain of attraction of a stable law.

Parametric models for tail copulas are being used for modeling tail dependence and maximum likelihood estimation is employed to estimate unknown parameters. However, two important questions seem unanswered in the literature: (1) What is the asymptotic distribution of the MLE and (2) how does one...

In general, the risk of joint extreme outcomes in financial markets can be expressed as a function of the tail dependence function of a high-dimensional vector after standardizing marginals. Hence, it is of importance to model and estimate tail dependence functions. Even for moderate dimension,...

We analyze sequential investment decisions in an innovative project that depend on the investor's information about the project failure risk and its potential final value. We consider the feedback effects between learning about the project parameters and the continuous adjustment of the...

This paper uses GARCH models and a panel VAR model to analyze possible time variation of the volatility of single-family home value appreciation and the interactions between the volatility and the economy, using a large quarterly data set that covers 277 MSAs in the U.S. from 1990:1 to 2002:2....

Hall & Yao (2003) showed that, for ARCH/GARCH, i.e. autoregressive conditional heteroscedastic/generalised autoregressive conditional heteroscedastic, models with heavy-tailed errors, the conventional maximum quasilikelihood estimator suffers from complex limit distributions and slow convergence...