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Author: John T. Scruggs Publisher: ISBN: Category : Languages : en Pages : 56
Book Description
We extend the variance decomposition model of Campbell (1991) to allow for time-varying stock market volatility. Specifically, we introduce a model in which the covariance matrix of the vector autoregression (VAR) follows a multivariate stochastic volatility (MSV) process. This VAR-MSV model permits the decomposition of unexpected real stock return variance into three time-varying components: variance of news about future dividends, variance of news about future returns, and a covariance term. We develop Bayesian Markov chain Monte Carlo (MCMC) econometric techniques for estimating the VAR-MSV model. These methods are well-suited for estimating models with latent stochastic volatilities, and are not subject to the small-sample biases and unit root problems that plague frequentist estimation of predictive regressions. We report strong evidence that real stock returns are predictable when the dividend-price ratio and a stochastically detrended short-term interest rate are employed as forecasting variables. The time-varying variance of news about future returns is the primary determinant of stock market volatility (both levels and changes). The variance of news about future dividends increased dramatically during the 1973-1974 recession and peaked during the 1980 recession before descending in the 1980s. However, its contribution to stock market volatility was offset by positive correlation between news about future dividends and news about future returns from 1974-1984.
Author: John T. Scruggs Publisher: ISBN: Category : Languages : en Pages : 56
Book Description
We extend the variance decomposition model of Campbell (1991) to allow for time-varying stock market volatility. Specifically, we introduce a model in which the covariance matrix of the vector autoregression (VAR) follows a multivariate stochastic volatility (MSV) process. This VAR-MSV model permits the decomposition of unexpected real stock return variance into three time-varying components: variance of news about future dividends, variance of news about future returns, and a covariance term. We develop Bayesian Markov chain Monte Carlo (MCMC) econometric techniques for estimating the VAR-MSV model. These methods are well-suited for estimating models with latent stochastic volatilities, and are not subject to the small-sample biases and unit root problems that plague frequentist estimation of predictive regressions. We report strong evidence that real stock returns are predictable when the dividend-price ratio and a stochastically detrended short-term interest rate are employed as forecasting variables. The time-varying variance of news about future returns is the primary determinant of stock market volatility (both levels and changes). The variance of news about future dividends increased dramatically during the 1973-1974 recession and peaked during the 1980 recession before descending in the 1980s. However, its contribution to stock market volatility was offset by positive correlation between news about future dividends and news about future returns from 1974-1984.
Author: Chengbo Fu Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This dissertation consists of three essays on stock market volatility. In the first essay, we show that investors will have the information in the idiosyncratic volatility spread when using two different models to estimate idiosyncratic volatility. In a theoretical framework, we show that idiosyncratic volatility spread is related to the change in beta and the new betas from the extra factors between two different factor models. Empirically, we find that idiosyncratic volatility spread predicts the cross section of stock returns. The negative spread-return relation is independent from the relation between idiosyncratic volatility and stock returns. The result is driven by the change in beta component and the new beta component of the spread. The spread-relation is also robust when investors estimate the spread using a conditional model or EGARCH method. In the second essay, the variance of stock returns is decomposed based on a conditional Fama-French three-factor model instead of its unconditional counterpart. Using time-varying alpha and betas in this model, it is evident that four additional risk terms must be considered. They include the variance of alpha, the variance of the interaction between the time-varying component of beta and factors, and two covariance terms. These additional risk terms are components that are included in the idiosyncratic risk estimate using an unconditional model. By investigating the relation between the risk terms and stock returns, we find that only the variance of the time-varying alpha is negatively associated with stock returns. Further tests show that stock returns are not affected by the variance of time-varying beta. These results are consistent with the findings in the literature identifying return predictability from time-varying alpha rather than betas. In the third essay, we employ a two-step estimation method to separate the upside and downside idiosyncratic volatility and examine its relation with future stock returns. We find that idiosyncratic volatility is negatively related to stock returns when the market is up and when it is down. The upside idiosyncratic volatility is not related to stock returns. Our results also suggest that the relation between downside idiosyncratic volatility and future stock returns is negative and significant. It is the downside idiosyncratic volatility that drives the inverse relation between total idiosyncratic volatility and stock returns. The results are consistent with the literature that investor overreact to bad news and underreact to good news.
Author: John Y. Campbell Publisher: ISBN: Category : Autoregression (Statistics) Languages : en Pages : 0
Book Description
This paper shows that unexpected stock returns must be associated with changes in expected future dividends or expected future returns A vector autoregressive method is used to break unexpected stock returns into these two components. In U.S. monthly data in 1927-88, one-third of the variance of unexpected returns is attributed to the variance of changing expected dividends, one-third to the variance of changing expected returns, and one-third to the covariance of the two components. Changing expected returns have a large effect on stock prices because they are persistent: a 1% innovation in the expected return is associated with a 4 or 5% capital loss. Changes in expected returns are negatively correlated with changes in expected dividends, increasing the stock market reaction to dividend news. In the period 1952-88, hanging expected. returns account for a larger fraction of stock return variation than they do in the period 1927-51.
Author: Zuliu Hu Publisher: International Monetary Fund ISBN: 1451852584 Category : Business & Economics Languages : en Pages : 26
Book Description
Despite concerns are often voiced on the so called “excess volatility” of the stock market, little is known about the implications of market volatility for the real economy. This paper examines whether the stock market volatility affects real fixed investment. The empirical evidence obtained from the US data shows that market volatility has independent effects on investment over and above that of stock returns. Volatility and its changes are negatively related to investment growth. To the extent volatility depresses fixed capital formation and hence future income growth, the results suggest the desirability of reducing stock market volatility.
Author: John Y. Campbell Publisher: OUP Oxford ISBN: 019160691X Category : Business & Economics Languages : en Pages : 272
Book Description
Academic finance has had a remarkable impact on many financial services. Yet long-term investors have received curiously little guidance from academic financial economists. Mean-variance analysis, developed almost fifty years ago, has provided a basic paradigm for portfolio choice. This approach usefully emphasizes the ability of diversification to reduce risk, but it ignores several critically important factors. Most notably, the analysis is static; it assumes that investors care only about risks to wealth one period ahead. However, many investors—-both individuals and institutions such as charitable foundations or universities—-seek to finance a stream of consumption over a long lifetime. In addition, mean-variance analysis treats financial wealth in isolation from income. Long-term investors typically receive a stream of income and use it, along with financial wealth, to support their consumption. At the theoretical level, it is well understood that the solution to a long-term portfolio choice problem can be very different from the solution to a short-term problem. Long-term investors care about intertemporal shocks to investment opportunities and labor income as well as shocks to wealth itself, and they may use financial assets to hedge their intertemporal risks. This should be important in practice because there is a great deal of empirical evidence that investment opportunities—-both interest rates and risk premia on bonds and stocks—-vary through time. Yet this insight has had little influence on investment practice because it is hard to solve for optimal portfolios in intertemporal models. This book seeks to develop the intertemporal approach into an empirical paradigm that can compete with the standard mean-variance analysis. The book shows that long-term inflation-indexed bonds are the riskless asset for long-term investors, it explains the conditions under which stocks are safer assets for long-term than for short-term investors, and it shows how labor income influences portfolio choice. These results shed new light on the rules of thumb used by financial planners. The book explains recent advances in both analytical and numerical methods, and shows how they can be used to understand the portfolio choice problems of long-term investors.
Author: Chibuzo Amaefula Publisher: LAP Lambert Academic Publishing ISBN: 9783659557446 Category : Languages : en Pages : 256
Book Description
The robustness of this research book is evident in its contribution to knowledge. It has shown that the variance equation can contain more than two exogenous variables without violating the non-negativity condition of the conditional variance under univariate GARCH specification and the use of univariate GARCH (p, q) model in examining volatility spillover effect. It has also studied time varying correlation using the diagonal BEKK model with OLS method to test the effect of the time trend on the correlation and the CCC-Model as a 'check model'. The research has empirically shown that the structure of correlation between stock returns and interest rate is time variant while relative to exchange rate and inflation is time invariant. The research empirical results have also shown that the correlation of stock returns volatility relative to the volatility of exchange rate and inflation rate vary over time and relative to interest rate volatility is time invariant. The dimensions of this book has made it be a reference book to many researchers and has also breached the gap between past research and future research.
Author: John Y. Campbell Publisher: ISBN: Category : Rate of return Languages : en Pages : 54
Book Description
This paper studies three different measures of monthly stock market volatility: the time-series volatility of daily market returns within the month; the cross-sectional volatility or 'dispersion' of daily returns on industry portfolios, relative to the market, within the month; and the dispersion of daily returns on individual firms, relative to their industries, within the month. Over the period 1962-97 there has been a noticeable increase in firm-level volatility relative to market volatility. All the volatility measures move together in a countercyclical fashion. While market volatility tends to lead the other volatility series, industry-level volatility is a particularly important leading indicator for the business cycle.
Author: Greg N. Gregoriou Publisher: CRC Press ISBN: 1420099558 Category : Business & Economics Languages : en Pages : 654
Book Description
Up-to-Date Research Sheds New Light on This Area Taking into account the ongoing worldwide financial crisis, Stock Market Volatility provides insight to better understand volatility in various stock markets. This timely volume is one of the first to draw on a range of international authorities who offer their expertise on market volatility in devel
Author: Huafeng (Jason) Chen Publisher: ISBN: Category : Languages : en Pages :
Book Description
We develop a daily measure of average stock variance and study whether it can predict market returns one day ahead. Using a time-invariant prediction model we find a robust predictive relation between these variables which cannot be used to profitably time the market. A closer look reveals that the strength and even the direction of the predictive relation vary significantly over short periods of time. Moreover, a simple timing strategy that exploits this variation over time significantly outperforms the market buy-and-hold strategy in terms of the mean-variance tradeoff. The evidence shows that predictability is stronger during business-cycle contractions and that our timing strategy is profitable because it avoids losses during bad times. Last, parameter breaks occur very frequently over short periods of time, and not only when the economy switches the phase of the business cycle. Our results suggest that idiosyncratic risk matters in asset pricing and that its effect is time varying.
Author: John T. Scruggs
Author: John T. Scruggs
Author: Chengbo Fu
Author: John Y. Campbell
Author: Zuliu Hu
Author: John Y. Campbell
Author: Chibuzo Amaefula
Author: John Y. Campbell
Author: Greg N. Gregoriou
Author: Missaka Warusawitharana
Author: Huafeng (Jason) Chen