Sellers of variance swaps earn time-varying risk premia for their exposure to realized variance, the level of variance swap rates, and the slope of the variance swap curve. To measure risk premia, we estimate a dynamic term structure model that decomposes variance swap rates into expected...

The standard generalized method of moments (GMM) estimation of Euler equations in heterogeneous-agent consumption-based asset pricing models is inconsistent under fat tails because the GMM criterion is asymptotically random. To illustrate this, we generate asset returns and consumption data from...

We study implications of unpriced "granular measurement errors" -- idiosyncratic shocks to large firms that aren't well-diversified in market indices -- for asset pricing tests and propose alternative tests insensitive to them. We find stronger evidence of an intertemporal relation between the...

Testing for constant expected returns and forecasting future returns necessitate the information beyond a single predictor. We consider the predictive regression model with multiple predictors which are potentially strongly persistent and cointegrated. Instrumental variables based tests for...

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995-2014...

This paper explores the volatility forecasting implications of a model in which the friction in high-frequency prices is related to the true underlying volatility. The contribution of this paper is to propose a framework under which the realized variance may improve volatility forecasting if the...

We introduce a new fractionally integrated model for covariance matrix dynamics based on the long-memory behavior of daily realized covariance matrix kernels and daily return observations. We account for fat tails in both types of data by appropriate distributional assumptions. The covariance...

In this paper, we examine the Nigerian stock market sector returns and estimate the bull and bear betas using the Logistic Smooth Threshold Market (LSTM) model. The LSTM model specification follows from the linear Constant Risk Market (CRM) model. We estimate the LSTM model for the overall...

In this paper, we analyze new possibilities in predicting daily ranges, i.e. differences between daily high and low prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges and explore the use of these more...