Conditional Serial Correlation Research Articles

Option Returns and Conditional Serial Correlation in Currency Markets

Weekly option writing returns in currency markets are (volatility-) regime dependent: they are low when implied volatility is low, and high when implied volatility is high. However, a time-varying volatility risk premium (ex-post) might only be part of the explanation. Daily exchange rate returns show (volatility-)regime dependent departures from normality and (volatility-)regime dependent serial correlation. This leads to weekly absolute movements that differ from a reasonable expectation under normality and iid. In particular, I document positive daily autocorrelation in low implied volatility quantiles, which might arise from actions of either speculators or option market makers who trade urgently in a relatively less resilient/liquid market.

Review of Econometric Models Applicable to Hedge Fund Returns Capturing Serial Correlation and Illiquidity

Hedge Fund returns are often highly serially correlated mainly due to illiquidity exposures given that investments in such securities tend to be inactively traded and associated market prices are not always readily available. Following that, observed returns of such alternative investments tend to be smoother than “true” unobserved returns, which, in fact, turn out to underestimate risk measures such as volatility (i.e. standard deviation). In order to encompass for such serial correlation and illiquidity, we propose three econometric models. The first model referred to as the log-normally distributed random walk model with time varying parameters is largely used in the risk industry for Value-at-Risk4 purposes. Its main goal, in our context, is to derive specific characteristics of Hedge Fund returns by challenging and invalidating its assumptions (i.e. lognormality assumption and presence of autocorrelation between returns as well as their squares). The next two, referred to as the Blundell-Ward and the Getmansky, Lo and Markarovmodel respectively, both encompass an unsmoothing process and incorporate a predictive model for volatility. However, their mathematical background lies on a diametrically different perspective. Last but not the least, we propose for both an adequate model extension (i.e. a Markov-switching model for Blundell-Ward and conditional serial correlation for Getmansky, Lo and Markarov) that provide a superior volatility forecasting given the limitations arising within their actual standard mathematical formalism.

Conditional Return Smoothing in the Hedge Fund Industry

AbstractWe show that if true returns are independently distributed and a manager fully reports gains but delays reporting losses, then reported returns will feature conditional serial correlation. We use conditional serial correlation as a measure of conditional return smoothing. We estimate conditional serial correlation in a large sample of hedge funds. We find that the probability of observing conditional serial correlation is related to the volatility and magnitude of investor cash flows, consistent with conditional return smoothing in response to the risk of capital flight. We also present evidence that conditional serial correlation is a leading indicator of fraud.

A Screen for Fraudulent Return Smoothing in the Hedge Fund Industry

This paper constructs a statistical screen for fraudulent return smoothing in the hedge fund industry. We show that if true returns are independently distributed, and a manager fully reports gains but delays reporting losses, then reported hedge fund returns will feature conditional serial correlation. Simulation evidence indicates that the power of the screen is restricted by the limited histories of some funds, but may still be sufficient to deter fraudulent return smoothing. Empirical evidence shows that the probability of observing conditional serial correlation is related to the volatility and magnitude of investor cash flows, consistent with managerial smoothing in response to the risk of capital flight.