Intertemporal Relationship Research Articles

Expected Returns and Markov-Switching Illiquidity

Recent theoretical models imply that liquidity is fragile: financial markets are liquid in some equilibria and illiquid in others. This paper employs an intuitively appealing Markov-switching regime model to investigate the episodic nature of stock market illiquidity and the intertemporal relation between illiquidity risk and expected stock returns. We introduce a two-state Markov-switching regime model for stock market illiquidity, returns and volatility. We find evidence of a significant illiquidity risk premium; the expected stock return is positively related to the conditional probability of an illiquid regime. By combining the Markov-switching model with a log-linear model for stock returns, we derive a tractable expression for the illiquidity feedback effect. Modeling the illiquidity feedback effect is critical to understanding the relation between realized stock returns, expected returns and Markov-switching illiquidity. We develop a flexible Bayesian Markov chain Monte Carlo (MCMC) approach for estimating and comparing models.

Temporal aggregation and risk–return relation

The function form of a linear intertemporal relation between risk and return is suggested by Merton's [1973. Econometrica 41, 867–887] analytical work for instantaneous returns, whereas empirical studies have examined the nature of this relation using temporally aggregated data, i.e., daily, monthly, quarterly, or even yearly returns. Our paper carefully examines the temporal aggregation effect on the validity of the linear specification of the risk–return relation at discrete horizons, and on its implications on the reliability of the resulting inference about the risk–return relation based on different observation intervals. Surprisingly, we show that, based on the standard Heston's [1993. Review of Financial Studies 6, 327–343] dynamics, the linear relation between risk and return will not be distorted by the temporal aggregation at all. Neither will the sign of this relation be flipped by the temporal aggregation, even at the yearly horizon. This finding excludes the temporal aggregation issue as a potential source for the conflicting empirical evidence about the risk–return relation in the earlier studies.

Is There a Risk-Return Tradeoff? Evidence from High-Frequency Data

This paper examines the intertemporal relation between risk and return for the aggregate stock market using high-frequency data. We use daily realized, GARCH, implied, and range-based volatility estimators to determine the existence and significance of a risk-return tradeoff for several stock market indices. We find a positive and statistically significant relation between the conditional mean and conditional volatility of market returns at the daily level. This result is robust to alternative specifications of the volatility process, across different measures of market return and sample periods, and after controlling for macroeconomic variables associated with business cycle fluctuations. We also analyze the risk-return relationship over time using rolling regressions, and find that the strong positive relation persists throughout our sample period. The market risk measures adopted in the paper add power to the analysis by incorporating valuable information, either by taking advantage of high frequency intraday data (in the case of realized, GARCH, and range volatility) or by utilizing the market's expectation of future volatility (in the case of implied volatility index).

Is there a risk–return trade‐off? Evidence from high‐frequency data

AbstractThis paper examines the intertemporal relation between risk and return for the aggregate stock market using high‐frequency data. We use daily realized, GARCH, implied, and range‐based volatility estimators to determine the existence and significance of a risk–return trade‐off for several stock market indices. We find a positive and statistically significant relation between the conditional mean and conditional volatility of market returns at the daily level. This result is robust to alternative specifications of the volatility process, across different measures of market return and sample periods, and after controlling for macro‐economic variables associated with business cycle fluctuations. We also analyze the risk–return relationship over time using rolling regressions, and find that the strong positive relation persists throughout our sample period. The market risk measures adopted in the paper add power to the analysis by incorporating valuable information, either by taking advantage of high‐frequency intraday data (in the case of realized, GARCH, and range volatility) or by utilizing the market's expectation of future volatility (in the case of implied volatility index). Copyright © 2006 John Wiley & Sons, Ltd.

Customer Trading in the Foreign Exchange Market: Empirical Evidence from an Internet Trading Platform

This paper analyzes the inter-temporal relationship between currency price changes and their expectations on intra-day frequencies. We examine how price expectations are transmitted into prices by means of order flow and how order flow is affected by past prices (feedback effects). Based on a trading activity dataset from a foreign exchange electronic trading platform, OANDA FXTrade, currency price change expectations are approximated by aggregating very detailed individual order information aiming to present an accurate picture of currency price expectations. We investigate on different intra-day sampling frequencies, whether price expectations and trading patterns of the OANDA FXTrade investors are helpful for predicting future currency prices. Moreover, considering the literature on market microstructure and behaviorial finance, we formulate several hypotheses about the relationship between price changes and order flow as well as the trading behavior and the preference structure of our investors, which we investigate with the help of forecasting studies and out-of-sample prediction criteria.

THE INTERTEMPORAL RELATION BETWEEN MONEY AND PRICES IN SOUTH AFRICA*

Several studies have emphasized the need to ‘filter’ the money growth and inflation data before a clear short-run intertemporal relation between them is revealed. When a simple filtering technique is employed to changes in money and prices, the correlation of the series tends to increase as the filter used shifts to lower frequency data. The technique also reveals a statistically significant relation between money and inflation, a pattern of Granger causality that has changed with changes in the monetary and exchange rate regime during 1965-2005, and much shorter leads/lags between money and inflation than those typically found in similar studies of the experience of developed economies.

The Oil Cycle, the Resource Curse, and the Tax-Spend Hypothesis: A VAR Analysis for Angola

The intertemporal relationship between oil revenues, real government spending and real output over the oil cycle is investigated for the case of Angola, the second largest oil producer in Sub-Saharan Africa. The results of a trivariate VAR, impulse response functions, and variance decomposition analysis provide empirical support for the tax-spend hypothesis. In addition, the Angolan economy is found victim of the resource curse, as real output does not respond to aggregate demand shocks such as changes in real government spending. The policy implications for Angola are twofold. First, to alleviate the constraints associated with the resource curse, Angola needs to find ways to diversify its economy. Second, given the direction of causality found between government expenditures and oil revenues, in order to avoid fiscal disequilibria, efficient management of oil revenues is as important as controlling public spending.

Estimating the Intertemporal Capital Asset Pricing Model with Cross-Sectional Consistency

The intertemporal capital asset pricing model of Merton (1973) states that the expected excess return on an asset is proportional to the expected covariance of the excess return on this asset with the excess return on the market portfolio. The proportionality coefficient measures the average relative risk aversion of investors. When the investment opportunity is stochastic, the expected excess return is also proportional to the covariance of the excess return with the state variables that govern the state of the investment opportunity. The proportionality coefficients on these covariance terms measure the investors' average aversion to unfavorable shifts in these state variables. In this paper, we use GARCH-type models to estimate the conditional covariance of a wide array of industry and Fama-French size/book-to-market portfolios with the market portfolio and with the Fama-French size (SMB) and book-to-market (HML) risk factors. We then estimate the system of simultaneous equations that links the excess returns on these portfolios to the corresponding conditional covariances with the market portfolio and the common risk factors. We obtain a positive and highly significant estimate for the relative risk aversion coefficient. The coefficient is about three for the long sample from July 1926 to December 2002, and is around six for the more recent period from July 1963 to December 2002. Furthermore, the expected excess returns are negatively related to their conditional covariance with the Fama-French size risk factor, suggesting that an increase in the size factor predicts an unfavorable shift in the investment opportunity. However, we do not find any consistent loading on the covariance with the book-to-market risk factor. Our findings are robust to different ways of forming portfolios and estimating conditional covariances. Most of the existing literature estimates the intertemporal risk-return relation using one single series of the market portfolio return. We show that the estimates from a single return series have low statistical significance and large sample variation. Our key contribution here is to direct the attention of the literature to the cross-sectional consistency of the intertemporal asset pricing relation and the universal proportionality underlying the risk-return relation. By exploiting this universal relation, we obtain positive and highly significant estimates on the relative risk aversion coefficient. We also gain a better understanding on how different risk factors predict future movements in investment opportunities.

Firm Size and Capital Structure

Firm size has been empirically found to be strongly positively related to capital structure. A number of intuitive explanations can be put forward to account for this stylized fact, but none have been considered theoretically. This paper starts bridging this gap by investigating whether a dynamic capital structure model can explain the cross-sectional size-leverage relationship. The driving force that we consider is the presence of fixed costs of external financing that lead to infrequent restructuring and create a wedge between small and large firms. We find four firm size effects on leverage. Small firms choose higher leverage at the moment of refinancing to compensate for less frequent rebalancings. But longer waiting times between refinancings lead on average to lower levels of leverage. Within one refinancing cycle the intertemporal relationship between leverage and firm size is negative. Finally, there is a mass of firms opting for no leverage. The analysis of dynamic economy demonstrates that in cross-section the relationship between leverage and size is positive and thus fixed costs of financing contribute to the explanation of the stylized size-leverage relationship. However, the relationship changes the sign when we control for the presence of unlevered firms.

There is a risk-return trade-off after all

This paper studies the intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns, the mixed data sampling (or MIDAS) approach. Using MIDAS, we find a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the intertemporal capital asset pricing model based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.

Equity Issues and Temporal Variation in Information Asymmetry

We investigate the intertemporal relation between information asymmetry and equity issues, and particularly focus on which firms drive this relation. We find that when information asymmetry for a particular firm is low compared to the recent past, the firm is more likely to issue equity as opposed to debt. Importantly, this intertemporal association is driven by firms with high levels of information asymmetry. These firms are more prone to adverse selection costs and thus have more to gain by issuing equity after a narrowing of the information gap between managers and investors. Our findings are robust to various firm-specific proxies for information asymmetry.

Managerial Behavior and Cost/Profit Efficiency in the Banking Sectors of Central and Eastern European Countries

This paper analyzes cost and profit efficiency level and the managerial behavior of banks in nine Central and Eastern European countries (the Czech Republic, Estonia, Hungary, Latvia, Lithuania Poland, Romania, Slovakia and Slovenia), providing cross-country and time series evidence on the period 1995-2002. A stochastic frontier analysis based on a Fourier flexible form indicates a generally low level of cost efficiency and an even lower level of profit efficiency. However, we also find significant differences among countries and some evidence of an increasing tendency over time in profit efficiency and, to an even stronger extent, in cost efficiency. Cost and profit efficiency scores are negatively correlated both on a country wide as well as on a bank by bank basis. Furthermore, instead of just looking at the determinants of cost and profit efficiency (e.g. asset quality, problem loans and risk), we test several hypotheses of managerial behavior using the Granger causality approach based on the intertemporal relation between bank efficiency, capitalization and problem loans, as proposed by Berger and DeYoung (1997). Even though a static analysis shows a negative correlation between problem loan and efficiency, we find no evidence of bad management hypothesis. Results provide evidence for the bad luck hypothesis suggesting the exogeneity of bad loans triggering inefficiency.

American youth consumption of licit and illicit substances

Using a representative sample of the 2001 National School-based Youth Risk Behavior Survey, this study investigated the contemporaneous and intertemporal relationship between the use of licit and illicit substances by American youth between the ages 12 and 18. The results indicated a strong contemporaneous relationship between smoking cigarettes, drinking alcohol, smoking marijuana, and using cocaine. The results supported the gateway hypothesis indicating that prior smoking of cigarettes is significantly associated with the probability of current marijuana and cocaine use, as prior drinking of alcohol is significantly associated with the probability of current marijuana and cocaine use. The results also showed that males had a higher probability of using cocaine than females, whereas black students had a higher probability of smoking marijuana and using cocaine than white students. Age and race were associated with marijuana and cocaine use, but cocaine use was further linked with the factors of gender, region, and metropolitan area. These findings could have implications for public policies regarding prevention.

There is a Risk-Return Tradeoff After All

This paper studies the ICAPM intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns -- the Mixed Data Sampling (or MIDAS) approach. Using MIDAS, we find that there is a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process, and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the ICAPM based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

A Survey of the International Empirical Evidence on the Tax-Spend Debate

The concern over budget deficits in the mid-1980s generated a strand of literature known as the tax-spend debate, sometimes labeled the revenue-expenditure nexus. This literature has focused on the intertemporal relationship between revenues and expenditures in the generation of budget deficits. In light of the numerous empirical time-series studies on the subject, the intent of this survey article is to provide an overview of the work to date as well as provide suggestions for future research in this area.

Government Spending, Taxation, and Oil Revenues in Mexico

The paper analyzes the intertemporal relationship between oil duties, taxes, government spending, and GDP in Mexico during the 1981–98 period. The results from estimating a VAR model, impulse response functions, and variance decompositions on the quarterly series of taxes, government spending, oil duties, and GDP suggest that there seems to be a substitution effect between oil duties and tax revenues, and that tax revenues are not able to absorb temporary decreases in oil duties. Also, increases in tax revenue might lead to increasing government spending, but short–run increases in government spending are not likely to lead to political pressure to reduce the expected budget deficit via increased taxation and/or oil revenues. Lastly, GDP is not stimulated in the short–run by temporary increases in government spending and, thus, stabilization measures adopted in recent years to reduce the size of the government are not likely to significantly undermine GDP growth.

Contemporaneous and Intertemporal Relationship Between the Consumption of Licit and Illicit Substances by Youth

Using a representative sample of the 1999 National School-based Youth Risk Behavior Survey, this study investigated the risky behavior of cigarette, alcohol, and marijuana consumption by underage students. The research analyzes the contemporaneous and intertemporal relationship between the use of these licit and illicit substances. Evidence of a gateway from cigarettes to marijuana was found; however, prior use of alcohol does not significantly increase the likelihood of currently using marijuana.

The stochastic volatility in mean model: empirical evidence from international stock markets

AbstractIn this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Volatility in Mean (SVM) model based on Monte Carlo simulation methods. The SVM model incorporates the unobserved volatility as an explanatory variable in the mean equation. The same extension is developed elsewhere for Autoregressive Conditional Heteroscedastic (ARCH) models, known as the ARCH in Mean (ARCH‐M) model. The estimation of ARCH models is relatively easy compared with that of the Stochastic Volatility (SV) model. However, efficient Monte Carlo simulation methods for SV models have been developed to overcome some of these problems. The details of modifications required for estimating the volatility‐in‐mean effect are presented in this paper together with a Monte Carlo study to investigate the finite sample properties of the SVM estimators. Taking these developments of estimation methods into account, we regard SV and SVM models as practical alternatives to their ARCH counterparts and therefore it is of interest to study and compare the two classes of volatility models. We present an empirical study of the intertemporal relationship between stock index returns and their volatility for the United Kingdom, the United States and Japan. This phenomenon has been discussed in the financial economic literature but has proved hard to find empirically. We provide evidence of a negative but weak relationship between returns and contemporaneous volatility which is indirect evidence of a positive relation between the expected components of the return and the volatility process. Copyright © 2002 John Wiley & Sons, Ltd.

Firm-level return dispersion and the future volatility of aggregate stock market returns

We find a sizeable positive relation between firm return dispersion and future market-level volatility in U.S. monthly equity returns from 1927 to 1995. This intertemporal relation remains strong when controlling for return shocks in the aggregate stock market, widely used factor-mimicking portfolios, and government bonds. In contrast, the well-known positive relation between market-return shocks and future market-level volatility largely disappears when controlling for firm return dispersion. We also document how firm return dispersion moves with the contemporaneous market return and with economic conditions. Collectively, our evidence suggests that the time variation in firm return dispersion has important market-wide implications.

The intertemporal relationship between market return and variance: an Australian perspective

In this paper we investigate the intertemporal relationship between the market risk premium and its conditional variance in an Australian setting. Using a bivariate EGARCH‐M model combined with the dynamic conditional correlation (DCC) framework as proposed by Engle (2000), we find evidence of a positive relationship between the market risk premium and its variance and evidence of two distinct interest rate effects. Furthermore, while the bond market’s own variance is not priced by investors, we find that the covariance between equity and bond markets is a significant risk factor that is priced in the market.