Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics

Abstract

W e consider three sets of phenomena that feature prominently in the financial economics literature: (1) conditional mean dependence (or lack thereof) in asset returns, (2) dependence (and hence forecastability) in asset return signs, and (3) dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated and explore the relationships in detail. Among other things, we show that (1) volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (2) it is statistically possible to have sign dependence without conditional mean dependence; (3) sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests because of the special nonlinear nature of sign dependence, so that traditional market timing tests are best viewed as tests for sign dependence arising from variation in expected returns rather than from variation in volatility or higher moments; (4) sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; and (5) the link between volatility dependence and sign dependence remains intact in conditionally non-Gaussian environments, for example, with time-varying conditional skewness and/or kurtosis.

Spillovers from the United States to Latin American and G7 stock markets: A VAR quantile analysis

“…These results are consistent with the weak form of the efficient market hypothesis and support past evidence in the literature about the unpredictability of asset returns in the central fragment of the distribution, within a daily frequency framework (White, 2000;Christoffersen and Diebold, 2006). Note: *** significant at 99%, ** significant at 95%, * significant at 90%.…”

Heterogeneous trading strategies with adaptive fuzzy Actor–Critic reinforcement learning: A behavioral approach

“…Additionally, the SR measuring the profitability per unit of risk and the IP, are much higher compared with RNN and MSW in all indices and periods examined. The fact that B&H strategy outperforms the RNN model in some cases is not in accordance with previous results derived by Ferná ndez-Rodriguez et al (2000) as well as with the conclusions reached by Christoffersen and Diebold (2006). It is noticeable in this study that in some cases an ''active'' trading strategy employed by the nonlinear RNN and MSW models compared with the naive B&H, provides with worse even negative (loss) results, (e.g., in P 3 for both RNN and MSW in case of NASDAQ).…”

“…Christoffersen and Diebold (2006) show that volatility dependence produces return dependence, and therefore forecastability, as long as expected returns are nonzero. The intuition behind this relationship is that volatility changes will alter the probability of observing negative or positive returns.…”

Can Markov switching models predict excess foreign exchange returns?

“…The three Markov states switch independently. 4 Christoffersen and Diebold (2003) demonstrate that serial dependence in higher moments, such as the variance and kurtosis, affects the expected sign of returns in the presence of a non-zero unconditional mean return. This sign dependence creates predictability in the direction of returns.…”