How useful are historical data for forecasting the long-run equity return distribution?

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The paper illustrates the importance of uncertainty about structural breaks and the value of modeling higher-order moments of excess returns when forecasting the return distribution and its moments. The shape of the long-run distribution and the dynamics of the higher-order moments are quite different from those generated by forecasts which cannot capture structural breaks. The empirical results strongly reject ignoring structural change in favor of our forecasts which weight historical data to accommodate uncertainty about structural breaks. We also strongly reject the common practice of using a fixed-length moving window. These differences in long-run forecasts have implications for many financial decisions, particularly for risk management and long-run investment decisions.density forecasts, structural change, model risk, parameter uncertainty, Bayesian learning, market returns

Nonlinear Features of Realized FX Volatility

This paper investigates nonlinear features of FX volatility dynamics using estimates of daily volatility based on the sum of intraday squared returns. Measurement errors associated with using realized volatility to measure ex post latent volatility imply that standard time series models of the conditional variance become variants of an ARMAX model. We explore nonlinear departures from these linear specifications using a doubly stochastic process under duration-dependent mixing. This process can capture large abrupt changes in the level of volatility, time varying persistence, and time-varying variance of volatility. The results have implications for forecast precision, hedging, and pricing of derivatives. Dans cet article, nous étudions les caractéristiques nonlinéaires de la dynamique de la volatilité des taux de change à l'aide d'estimations de la volatilité quotidienne basées sur la somme du carré des rendements intraquotidiens. Les erreurs de mesure commises en utilisant la volatilité réalisée pour mesurer la volatilité latente ex post font en sorte que les modèles standards de séries chronologiques de la variance conditionnelle deviennent des variantes d'un modèle ARMAX. Nous explorons des alternatives nonlinéaires à ces spécifications linéaires en utilisant un processus doublement stochastique, avec mixage dépendant de la durée. Ce processus peut capter des changements importants et abrupts dans le niveau de la volatilité, de même qu'une persistence et une variance de la volatilité variant dans le temps. Nos résultats influent sur la précision des prévisions, la couverture et l'évaluation des produits dérivés.High-frequency data, realized volatility, semi-Marko, Données à haute fréquence, volatilité réalisée, demi-Markov

Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions?

Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV ) dynamics; the timing of information availability; and the assumed distributions of both return and log(RV) innovations. We find that a joint model of returns and volatility that features two components for log(RV) provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returnsRealized Volatility, multiperiod out-of-sample prediction, term structure of density forecasts, Stochastic Volatility

Do high-frequency measures of volatility improve forecasts of return distributions?

Many finance questions require a full characterization of the distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV) dynamics; the timing of information availability; and the assumed distributions of both return and log(RV) innovations. We find that a joint model of returns and volatility that features two components for log(RV) provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returns.RV, multiperiod, out-of-sample, term structure of density forecasts, observable SV

Endocrine and Psychophysiological Correlates of Jealousy and Social Anxiety in Healthy Adults: Elevated Responses to Inter-Male Competition

Male mammals compete for reproductive access to females. Gaining and maintaining this access can be stressful and anxiety-provoking. In humans, anxiety and associated protective behaviors can manifest as jealousy. Physiological stress is likely to increase in relation to jealousy as it does with anxiety. Hypothetically, higher levels of anxiety and cortisol may indicate, and may even promote, strong territorial or jealous behavior. Chronically elevated cortisol has been shown to be deleterious to prefrontal and hippocampal neurons and result in emotional and stress-response dysregulation. In very anxious and jealous individuals, chronic stress activation could further promote these tendencies via emotional disinhibition. Cortisol production also related to vasopressin (AVP) levels and AVP has been shown to increase mate preference and territoriality. Furthermore, physiological measures may be more valid than self-report of less socially desirable behaviors such as jealousy and anxiety. As a preliminary study, we measured salivary cortisol, heart-rate, and blood pressure in relation to self-reported anxiety and jealousy in healthy men and women in response to threatening male faces paired with smiling female faces. Elevated anxiety positively predicted jealousy in men but not women. Anxiety and jealousy also predicted elevated heart rate and blood pressure. Cortisol levels in response to the threat task and in relation to jealousy approached statistical significance (ps \u3c 0.07) and suggest the need for a larger sample size

Endocrine and Psychophysiological Correlates of Jealousy and Social Anxiety in Healthy Adults: Elevated Responses to Inter-Male Competition

Male mammals compete for reproductive access to females. Gaining and maintaining this access can be stressful and anxiety-provoking. In humans, anxiety and associated protective behaviors can manifest as jealousy. Physiological stress is likely to increase in relation to jealousy as it does with anxiety. Hypothetically, higher levels of anxiety and cortisol may indicate, and may even promote, strong territorial or jealous behavior. Chronically elevated cortisol has been shown to be deleterious to prefrontal and hippocampal neurons and result in emotional and stress-response dysregulation. In very anxious and jealous individuals, chronic stress activation could further promote these tendencies via emotional disinhibition. Cortisol production also related to vasopressin (AVP) levels and AVP has been shown to increase mate preference and territoriality. Furthermore, physiological measures may be more valid than self-report of less socially desirable behaviors such as jealousy and anxiety. As a preliminary study, we measured salivary cortisol, heart-rate, and blood pressure in relation to self-reported anxiety and jealousy in healthy men and women in response to threatening male faces paired with smiling female faces. Elevated anxiety positively predicted jealousy in men but not women. Anxiety and jealousy also predicted elevated heart rate and blood pressure. Cortisol levels in response to the threat task and in relation to jealousy approached statistical significance (ps \u3c 0.07) and suggest the need for a larger sample size

Extracting bull and bear markets from stock returns

Traditional methods used to partition the market index into bull and bear regimes often sort returns ex post based on a deterministic rule. We model the entire return distribution; two states govern the bull regime and two govern the bear regime, allowing for rich and heterogeneous intra-regime dynamics. Our model can capture bear market rallies and bull market corrections. A Bayesian estimation approach accounts for parameter and regime uncertainty and provides probability statements regarding future regimes and returns. Applied to 123 years of data our model provides superior identification of trends in stock prices.Markov switching, bear market rallies, bull market corrections, Gibbs sampling

Testing the Unbiasedness Hypothesis in the Forward Foreign Exchange Market: A Specification Analysis

This paper evaluates two popular regression methods of testing the unbiasedness hypothesis in the forward foreign exchange market. For the 30-day Canada/United States forward foreign exchange market, the evidence overwhelmingly indicates that it is inappropriate to treat the structure of the systematic and stochastic components of the test relations as constant over time. Hence, conclusions inferred from parameter significance testing based upon full-sample estimation can be very misleading. Accordingly, we argue for a specification analysis of the test relations, and more explicit modelling of market fundamentals.The financial support of the Social Sciences and Humanities Research Council of Canada and the Advisory Research Committee of Queen's University is acknowledged