A new approach to risk-return trade-off dynamics via decomposition

This paper revisits the puzzling time series relation between risk premium and conditional volatility by proposing a flexible risk-return trade-off that allows for a variety of possible shapes and incorporates potential nonlinearities inherent in excess return dynamics. We derive this flexible risk-return relation using the decomposition approach of Anatolyev and Gospodinov (2010), which splits excess returns into the product of absolute returns and signs. Using this decomposition strategy, we study four major international financial markets. The empirical results support a significant and positive risk-return trade-off that is driven by conditional volatility, market timing and the interdependence between the two components, which is generically related to return skewness.     

Causality effects in return volatility measures with random times

We provide a structural approach to identify instantaneous causality effects between durations and stock price volatility. So far, in the literature, instantaneous causality effects have either been excluded or cannot be identified separately from Granger type causality effects. By giving explicit moment conditions for observed returns over (random) duration intervals, we are able to identify an instantaneous causality effect. The documented causality effect has significant impact on inference for tick-by-tick data. We find that instantaneous volatility forecasts for, e.g., IBM stock returns must be decreased by as much as 40% when not having seen the next quote change before its (conditionally) median time. Also, instantaneous volatilities are found to be much higher than indicated by standard volatility assessment procedures using tick-by-tick data. For IBM, a naive assessment of spot volatility based on observed returns between quote changes would only account for 60% of the actual volatility. For less liquidly traded stocks at NYSE this effect is even stronger.     

A Bayesian approach to excess volatility,short-term underreaction and long-term overreaction during financial crises

In this paper, we introduce a new Bayesian approach to explain some market anomalies during financial crises and subsequent recovery. We assume that the earnings shock of an asset follows a random walk model with and without drift to incorporate the impact of financial crises. We further assume the earning shock follows an exponential family distribution to accommodate symmetric as well as asymmetric information. By using this model setting, we develop some properties on the expected earnings shock and its volatility, and establish properties of investor behavior on the stock price and its volatility during financial crises and the subsequent recovery. Thereafter, we develop properties to explain excess volatility, short-term underreaction, long-term overreaction, and their magnitude effects during financial crises and the subsequent recovery. We also explain why behavioral finance theory could be used to explain many of the asset pricing anomalies, but traditional asset pricing models cannot achieve this aim.     

Day trading and stock price volatility

When an investor buys and sells the same stock on the same day, he is said to have made a day trade. Using the trading records of Finnish traders, this paper examines whether day trading is related to volatility of stock prices. I find a strong positive time-series relation between the number of day trades by individual investors and intraday volatility among heavily day traded stocks. This effect is robust after controlling for a previously documented volume–volatility relation. The result suggests that the joint hypothesis of price pressure and volatility induced day trading dominates the liquidity effects of day trading.     

Exchange rate volatility and Spanish-American commodity trade flows

A number of recent studies have tested the impact of exchange rate volatility on trade flows, particularly for individual commodities, for various country pairs. These have found that risk can increase as well as decrease trade, but that oftentimes industries are not affected. This study examines trade between the United States and Spain over the period from 1962 to 2009, for 131 U.S. export industries and 88 import industries. We find that exchange rate volatility has short-run and long-run effects in only a fraction of the cases, but that exports respond more to increased uncertainty than imports do. In all, only 35 of the 74 U.S. export industries are affected (11 positive, 24 negative), whilst only three out of 37 import industries have positive coefficients and 11 have negative ones. We find no evidence that durable or nondurable goods are more likely to respond to volatility, whilst small industries or specialized goods might show more of a positive response.     

Dynamic volatility connectedness between industrial metal markets

Combined with the spillover framework of Diebold and Yilmaz (2009, 2012, 2014) and the TVP-VAR-SV model of Primiceri (2005), this paper studies the dynamic volatility connectedness between six major industrial metal (i.e., aluminum, copper, lead, nickel, tin and zinc) spot and futures markets. The results show that: (1) The total volatility connectedness between industrial metal spot or futures markets has three obvious cyclical change periods with a higher connectedness level; (2) The net connectedness of zinc and copper with other metals has been at a high positive level for a long time, which indicates the two metal markets dominate the industrial metal market; (3) Zinc exhibits the strongest volatility spillovers, while tin exhibits the weakest volatility spillovers, no matter in spot markets or futures markets; (4) The connectedness of realized skewness and kurtosis have similarity with volatility connectedness but the spillover effects of skewness and kurtosis are not as obvious as the volatility spillover effects.     

On the Link between Volatility and Growth: A Spatial Econometrics Approach

This paper examines the link between macro volatility and economic growth in the lens of spatial econometrics. We present an unconstrained spatial Durbin Ramey-Ramey model. We test the extended model in a panel of 78 countries to investigate all the possible dimensions along which spatial interactions can affect the link between macro volatility and growth. In contrast to previous literature, we split the effects of volatility on growth into direct and indirect effects using partial derivative impacts approach. We found that both the direct and indirect effects of volatility on growth are negative; the latter effect suggesting the transmission of volatility shocks to neighbouring countries. Growth rates observed in neighbouring countries has a positive effect on growth rate of a particular country.     

Bond risk, bond return volatility, and the term structure of interest rates

This paper explores the time variation in the bond risk, as measured by the covariation of bond returns with stock returns and consumption growth, and in the volatility of bond returns. A robust stylized fact in empirical finance is that the spread between the yields on long- and short-term bonds forecasts future excess returns on bonds at varying horizons positively; in addition, the short-term nominal interest rate forecasts both the stock return volatility and the exchange rate volatility positively. This paper presents evidence that movements in both the short-term nominal interest rate and the yield spread are positively related to changes in the subsequent realized bond risk and bond return volatility. The yield spread appears to proxy for business conditions, while the short rate appears to proxy for inflation and economic uncertainty. A decomposition of bond betas into a real cash flow risk component and a discount rate risk component shows that yield spreads have offsetting effects in each component. A widening yield spread is correlated with a reduced cash-flow (or inflationary) risk for bonds, but it is also correlated with a larger discount rate risk for bonds. The short rate only forecasts the discount rate component of the bond beta.     

Cross-sectional implications of dynamic asset pricing with stochastic volatility and ambiguity aversion

Building on recent research that highlights the importance of macroeconomic volatility and ambiguity aversion in explaining the dynamics of stock returns, in this paper we propose a dynamic asset pricing model that simultaneously accounts for stochastic macroeconomic volatility and ambiguity, assuming that investors deal with uncertainty about the mechanics of macroeconomic fluctuations using first-release consumption and revisions to aggregate consumption on vintage data. Our results show that the proposed model captures a large fraction of the cross-sectional variation of excess returns for a wide range of market anomaly portfolios. Furthermore, while the price of risk for ambiguity is positive and significant for the vast majority of assets under study, macroeconomic volatility yields ambiguous outcomes, although it significantly increases the explanatory power of the model for specific assets. Our results suggest that macroeconomic volatility and ambiguity complement each other in explaining the cross-sectional behavior of stock returns.     

Ex-post rational price approximations and the empirical reliability of the present-value relation

Excess volatility and regression tests have resulted in apparent rejections of the present-value relation when ex-post price approximations are employed. These approximations are based upon a sample terminal condition for prices, are not ergodic time-series, and do not result in statistics with readily calculable standard errors. Kleidon (1986a) has demonstrated that ex-post price approximations can subtly affect the reliability of certain volatility tests. We use a bootstrapped cointegration model to demonstrate some of these same effects in Mankiw, Romer and Shapiro's (1985) volatility statistics. The volatility statistics rarely have positive expected value in finite samples and still do not reject the presentvalue relation. Approximations based upon a ‘rolling’ terminal condition result in volatility statistics which have calculable large-sample errors, but even these standard errors greatly overstate the accuracy of volatility statisics in small samples. Regression tests of the present value relation are also affected by the price approximations.     

Volatility, trade size, and order imbalance in China and Japan exchange traded funds

In recent years there have been reports of excessive price volatility and value differences between prices of exchange traded funds (ETFs) and their underlying asset values. The objective of our study is to shed light on these facts by taking a closer look at the relationship between return volatility, trade size, and order imbalance for international ETFs. Even after controlling for order imbalance, volume as represented by number of trades in one of five trade size categories continues to have a significant and predominantly positive impact on volatility. Both trade size and order imbalance are important determinants of the Japan and China ETFs’ price volatility.     

Stock market volatility,the news,and monetary policy

The dynamic relationship linking the volatility of equity prices with “the news” and the expected path for monetary policy is investigated. Previous results that link the impact of the news about real activity to changes in current and future interest rates are employed in developing a positive link between changes in volatility and the news. Empirically, our results uncover a positive and statistically significant response of the CBOE volatility index, VIX, to unanticipated changes in employment, but not to inflation. Hence, agents' expectations for the policy response to news have an important influence on the expected volatility of stock prices. (JEL E44, E52)     

Robustness of stable volatility strategies

The paper analyzes the robustness of stable volatility strategies, i.e. strategies in which the portfolio weight of the stock is inversely proportional to its local volatility. These strategies are optimal for a CRRA investor if the stock follows a diffusion process, the expected excess return is proportional to its volatility, and the hedging demand is zero. We assess the performance of stable volatility strategies when these restrictive assumptions do not hold, in particular, when the risk premium is not proportional to volatility and when the stock price is subject to jumps. We find that stable volatility strategies are indeed robust or close to robust under a maxmin decision rule. In addition to our theoretical results, we perform a simulation analysis to evaluate strategies that scale the portfolio weight by the volatility, variance or a constant portfolio weight, and also analyze the strategies using empirical excess returns. Both analyses confirm the robustness of stable volatility strategies.     

Does financial integration affect real exchange rate volatility and cross-country equity market returns correlation?

Existing empirical studies show that financial integration affects the behavior of average excess returns, cross-country equity market returns (EMR) correlations and real exchange rate (RER) volatility. We employ a recently developed two-country model with recursive preferences, frictionless and complete markets and highly correlated long-run innovations to examine whether full financial integration (i.e. full risk-sharing) affects the US-Canada EMR correlation and the US RER volatility, consistently with existing empirical findings. First, full risk-sharing gives rise to a relatively high RER volatility. Second, it induces very strong positive cross-country EMR correlations. Both quantities are higher than those observed in the US-Canada asset pricing data, and increase as the risk-sharing incentive increases. In contrast, “international consumption quantities” are weakly sensitive to changes in the level of aversion to consumption and utility risk.     

Do interest rate options contain information about excess returns?

There is strong empirical evidence that long-term interest rates contain a time-varying risk premium. Options may contain valuable information about this risk premium because their prices are sensitive to the underlying interest rates. We use the joint time series of swap rates and interest rate option prices to estimate dynamic term structure models. The risk premiums that we estimate using option prices are better able to predict excess returns for long-term swaps over short-term swaps. Moreover, in contrast to the previous literature, the most successful models for predicting excess returns have risk factors with stochastic volatility. We also show that the stochastic volatility models we estimate using option prices match the failure of the expectations hypothesis.     

投资者行为与证券市场波动

证券市场的剧烈波动性是难以用新古典金融理论解释.行为金融学的相关理论可以很好地解释证券市场的过度波动性,投资者情绪、潮流会推动资产价格偏离基本价值,形成市场泡沫。立足于投资者行为,引入适应性预期,可以对股票收益的自相关和过度波动特征一个一致和完备的解释。     

The dynamic impact of oil price shocks on the stock market and the USD/RMB exchange rate: Evidence from implied volatility indices

Using daily data from March 16, 2011, to September 9, 2019, we explore the dynamic impact of the oil implied volatility index (OVX) changes on the Chinese stock implied volatility index (VXFXI) changes and on the USD/RMB exchange rate implied volatility index (USDCNYV1M) changes. Through a TVP-VAR model, we analyse the time-varying uncertainty transmission effects across the three markets, measured by the changes in implied volatility indices. The empirical results show that the OVX changes are the dominant factor, which has a positive impact on the USDCNYV1M changes and the VXFXI changes during periods of important political and economic events. Moreover, USDCNYV1M changes are the key factor affecting the impact of OVX changes on VXFXI changes. When the oil crisis, exchange rate reform, and stock market crash occurred during 2014–2016, the positive effects of uncertainty transmission among the oil market, the Chinese stock market, and the bilateral exchange rate are significantly strengthened. Finally, we find that the positive effects are significant in the short term but diminish over time.     

On the predictability of the distribution of excess returns in currency markets

This paper investigates the predictability of foreign exchange (FX) volatility and liquidity risk factors on returns to the carry trade, an investment strategy that borrows in currencies with low interest rates and invests in currencies with high interest rates. Previous studies have suggested that this predictability could have been spuriously accounted for due to the persistence of the predictors. The analysis uses a predictive quantile regression model developed by Lee (2016) that allows for persistent predictors. We find that predictability changes remarkably across the entire distribution of currency excess returns. Predictability weakens substantially in the left tail once persistence is accounted for, implying a moderate negative predictive relation between FX volatility risk and carry trade returns. By contrast, it becomes stronger in the right tail. Furthermore, we provide evidence that FX volatility risk still dominates liquidity risk after controlling for persistence. These findings suggest that the persistence of the predictors needs to be taken into account when one measures predictability in currency markets. Finally, out-of-sample forecast performance is also presented.     

Structural analysis of portfolio risk using beta impulse response functions

We estimate the data generating process of daily excess returns of 20 major German stocks in a CAPM framework with time varying betas. Our sample spans a 23 year period from 1974 to 1996. An asymmetric dependence of volatility on lagged innovations is taken into account. We introduce beta impulse response functions to shed light on the structural implications of systematic risk associated with competing volatility models. The dependence of beta on news is characterized with respect to different sources (asset specific vs. market general news). The empirical results suggest that negative news emerging from the market involve a stronger impact on beta relative to positive news. Concerning firm specific news the opposite relation is found for the majority of the analysed data sets.     

Evolving United States stock market volatility: The role of conventional and unconventional monetary policies

Despite the econometric advances of the last 30 years, the effects of monetary policy stance during the boom and busts of the stock market are not clearly defined. In this paper, we use a structural heterogeneous vector autoregressive (SHVAR) model with identified structural breaks to analyse the impact of both conventional and unconventional monetary policies on U.S. stock market volatility. We find that contractionary monetary policy enhances stock market volatility, but the importance of monetary policy shocks in explaining volatility evolves across different regimes and is relative to supply shocks (and shocks to volatility itself). In comparison to business cycle fluctuations, monetary policy shocks explain a greater fraction of the variance of stock market volatility at shorter horizons, as in medium to longer horizons. Our basic findings of a positive impact of monetary policy on equity market volatility (being relatively stronger during calmer stock market periods) are also corroborated by analyses conducted at the daily frequency based on an augmented heterogeneous autoregressive model of realised volatility (HAR-RV) and a multivariate k-th order nonparametric causality-in-quantiles framework. Our results have important implications both for investors and policymakers.