Volatility Premium Research Articles

THE LEVERAGE EFFECT ON THE VALUE PREMIUM VOLATILITY: FROM AN INTERNATIONAL PERSPECTIVE

This paper investigates the leverage effect on the value premium volatility usingGARCH and TARCH models utilizing a unique dataset, for twenty nine countries. Thefindings show that value premium returns are bigger in developed than in developingcountries and vary from negative values in some countries to positive in other countries,suggesting that different markets may need different long-run investment strategies.Moreover, we show persistence of a finite unconditional variance that appears strongin developed countries but less significant in developing countries. Finally, leverageappears to have asymmetric effects on the value premium in eight countries including:USA, Canada, Denmark, Finland, New Zealand, Sweden, the UK and Poland.

Option Pricing and Dynamic Discrete Time Hedging for Regime-Switching Geometric Random Walks Models

We propose optimal mean-variance dynamic hedging strategies in discrete time under a multivariate Gaussian regime-switching model. The methodology, which also performs pricing, is robust to time-varying and clustering risk observed in financial time series. As such, it overcomes the main theoretical drawbacks of the Black-Scholes model. To support our approach, we provide univariate pricing results for monthly S&P 500 vanilla options. Then, we present the associated out-of-sample hedging results in the context of harvesting the implied versus realized volatility premium. Using the proposed methodology, the Sharpe ratio derived from the strategy doubles over the classical Black-Scholes delta-hedging methodology.

Noisy Rational Expectation Equilibrium with Actively Managed Fund Sector

As there is virtually no empirical evidence that fund managers outperform passive benchmarks, public investors face a serious question as to whether they should invest in actively managed funds. This paper develops a model of a dynamic asymmetric information economy with heterogenous risk averse investors and active funds and solves for the noisy rational expectation equilibrium. Low risk tolerance investors are found to be strictly better off with the active funds, but the high risk tolerance ones are not. The size of an actively managed fund is not monotonically increasing in the fund manager's skill level, so fund size is not a reliable indicator of managerial skill. The market reacts to the manager's increasing risk-taking behavior by reducing the market price of risk, volatility and risk premium. Asymmetric information creates an automatic mechanism for public investors to protect themselves from the manager's excessive risk-taking. Indeed, the distribution of risk tolerance coupled with adverse selection set limits on the fund manager's risk-taking levels.

Deciding to enter a monetary union: The role of trade and financial linkages

This paper evaluates the role of trade and financial linkages in the decision to enter a monetary union. We estimate a two-country DSGE model for the U.K. economy and the euro area with financial intermediaries as in Gertler and Karadi (2011). We use the model to compute the welfare trade-offs from joining the euro. We compare the gains from trade that would occur after the adoption of the euro against the costs of relinquishing monetary policy, both conventional and unconventional. We also study the effects of the changes in the corporate risk premium observed during the recent crisis. We find that in tranquil times, when the risk premium volatility is low, the net welfare gain of joining the monetary union is 2.4 percent of life-time consumption. During financial crises, when there is a sharp increase in the volatility of the risk premium, joining a monetary union would lead to a net welfare loss of 2.2 percent of life-time consumption. The welfare analysis underscores the importance of financial stability to sustain a monetary union over time.

Seasonal Quality Premiums for Wheat: A Case Study for Northern Germany

ABSTRACTSeasonal variations of the price premium for bread and feed wheat indicate opportunities to profitably adjust the grain marketing strategy of farmers that harvest (and store) both wheat types. In this article, we estimate the seasonal pattern of wheat price premiums on the German market using a vector error correction approach, which accounts for multivariate autoregressive conditional heteroscedasticity of the error terms. Our results indicate a significant downward trend for the premium during the marketing season, with the trend's magnitude depending on the average quality of harvested wheat. If farmers separately store both bread and feed wheat, they should tend to sell bread wheat before they sell feed wheat, particularly in years of low average wheat qualities. Further, the volatility of the price premium significantly decreases over the season, indicating higher risks at the beginning and lower risks towards the end. The volatility of the premium may present opportunities that require a reevaluation of the recommended temporal sequence of selling bread and feed wheat.

Risk aversion, investor information and stock market volatility

This paper employs a standard asset pricing model to derive theoretical volatility measures in a setting that allows for varying degrees of investor information about the dividend process. We show that the volatility of the price–dividend ratio increases monotonically with investor information but the relationship between investor information and equity return volatility (or equity premium volatility) can be non-monotonic, depending on risk aversion and other parameter values. Under some plausible calibrations and information assumptions, we show that the model can match the standard deviations of equity market variables in long-run U.S. data. In the absence of concrete knowledge about investors׳ information, it becomes more difficult to conclude that observed volatility in the data is excessive.

The conditional equity premium, cross-sectional returns and stochastic volatility

Bansal and Yaron (2004) demonstrate, by calibration, that the Consumption-based Capital Asset Pricing Model (CCAPM) can be rescued by assuming that consumption growth rate follows a stochastic volatility model. They show that the conditional equity premium is a linear function of conditional consumption and market return volatilities, which can be estimated handily by various Generalized Autoregressive Conditonal Heteroskedasticity (GARCH) and Stochastic Volatility (SV) models. We find that conditional consumption and market volatilities are capable of explaining cross-sectional return differences. The Exponential GARCH (EGARCH) volatility can explain up to 55% variation of return and the EGARCH model augmented with cay^ — a cointegrating factor of consumption, labor income and asset wealth growth — greatly enhances model performance. We proceed to test another hypothesis: if Bansal and Yaron estimator is an unbiased estimator of true conditional equity premium, then the instrumental variables for estimating conditional equity premium should no longer be significant. We demonstrate that once the theoretical conditional risk premium is added to the model, it renders all instrumental variables redundant. Also, the model prediction is consistent with observed declining equity premium.

Why Was Asia Resilient? Lessons from the Past and for the Future

Asia proved to be remarkably resilient in the face of the global financial crisis, but why was its output performance stronger than that of other regions? The paper shows that better initial conditions—in the form of lower external and financial vulnerabilities—contributed significantly to Asia’s resilience. Key pre-crisis factors included moderate credit expansion, reliance on deposit funding, enhanced bank asset quality, reduced external financing, and improved current accounts. These improvements reflected the lessons from the Asian financial crisis in the late 1990s, which helped reshape both public policies and private sector behavior. For example, several countries stepped up their use of macroprudential policies, well before they were recognized as an essential component of the financial stability toolkit. They also overhauled financial regulations and strengthened oversight of financial institutions, which helped reduce risk-taking by households and firms before the global financial crisis. Looking ahead, Asia is in the process of adjusting to more volatile external conditions and higher risk premiums. By drawing the right lessons from its pre-crisis experiences, Asia’s economies will be better equipped to address new risks associated with increased cross-border capital flows and greater integration with the rest of the world.

Real-Time Learning, Macroeconomic Uncertainty, and the Variance Risk Premium

The variance risk premium represents the compensation paid to index option sellers for the risk of losses following upward movements in realized market return volatility. Common wisdom connects these spikes with elevated uncertainty on economic fundamentals. I incorporate this link within a single-agent general equilibrium model, embedding real-time learning on state variables and parameters. I show that infrequent, large and relatively transitory macroeconomic uncertainty shocks produce a sizable and volatile variance risk premium. These shocks coincide with major events such as the LTCM/Russian crisis, the onset of the second Gulf War, and the great financial crisis of 2008-2009. I compute macroeconomic uncertainty as the dispersion of the agent’s belief about the expected growth rate of consumption. Its time-varying nature reflects in the variance risk premium, generating short-term predictability for market excess returns, consistent with the data. In addition, the model matches the higher order moments of the realized equity premium, with a reasonably low level of relative risk aversion equal to five. I finally provide evidence that parameter uncertainty may represent an extra-source of risk which is priced in equilibrium. In fact, a model with parameter learning and standard CRRA preferences, matches around half of the historical variance risk premium.

Low-Volatility Investing

It is well established that stocks with lower price fluctuations tend to outperform riskier ones. This article reviews plausible explanations for the low volatility anomaly and reproduce the performance of low volatility strategies in different market environments as well as in different geographical applications. We further attribute the outperformance by decomposing the excess returns of low volatility strategies into well-known sources of equity factor premiums. We find that standard long-only low volatility strategies capture not just the low volatility premium, but, in many cases, also rely on other premiums. Additionally, we illustrate the benefit of incorporating low volatility equity strategies in an asset allocation exercise; the inclusion of low volatility strategies can significantly expand the efficient frontier and improve the resulting portfolio’s overall risk/return profile. <b>TOPICS:</b>Volatility measures, portfolio construction, passive strategies

The Volatility of Bid-Ask Spreads

This paper provides evidence that supports the original hypothesis of Chordia, Subrahmanyam, and Ashuman (2001) that greater variability in liquidity should lead to higher expected returns. While prior research has often found a negative relation between the volatility of liquidity and expected stock returns, we find that the volatility of the bid-ask spread is positively related to future returns. The average risk-adjusted return for stocks in the highest spread volatility quintile is around 1.7 percent per month, with returns from High-Low quintiles as high as 2.7 percent per month. Furthermore, the spread volatility premium is robust to a variety of multivariate tests that control for the market risk factor, SMB, HML, momentum, and illiquidity risk. Our findings provide support for the hypothesis that variability in liquidity affects expected returns and is an important component of illiquidity.

Idiosyncratic Volatility, Measurement Frequency and Return Reversal

This paper examines whether the negative relation between idiosyncratic volatility and expected returns is due to stock return reversals as argued by Fu (2009) and Huang, Liu, Rhee and Zhang (2010). Controlling the return reversal effect, it shows that stocks with different past returns have different relations. The positive relation is mainly driven by stocks with low past returns, while the negative relation is result from stocks with high past returns. Additionally, the relation is very sensitive to the measurement frequency of idiosyncratic volatility, and the daily realized idiosyncratic volatility measure is a better proxy for the expected idiosyncratic volatility than the monthly measure. By employing an exponential generalized autoregressive conditional heteroskedascticity-in-mean (EGARCH-M) model, this paper finds a strong positive relation between time-varying risk premium and idiosyncratic volatility for portfolios containing stocks with low past returns and small portfolio, and a negative relation for growth portfolio.

Impact of idiosyncratic volatility on stock returns: A cross-sectional study

This paper proposes a new approach to estimate the idiosyncratic volatility premium. In contrast to the popular two-pass regression method, this approach relies on a novel GMM-type estimation procedure that uses only a single cross-section of return observations to obtain consistent estimates. Also, it enables a comparison of idiosyncratic volatility premia estimated using stock returns with different holding periods. The approach is empirically illustrated by applying it to daily, weekly, monthly, quarterly, and annual US stock return data over the course of 2000–2011. The results suggest that the idiosyncratic volatility premium tends to be positive on daily return data, but negative on monthly, quarterly, and annual data. They also indicate the presence of a January effect.

Hidden persistent disasters and asset prices

This study analyzes the effects of agents’ learning about hidden persistent economic disasters on asset prices. In this study, it is assumed that aggregate consumption follows a hidden Markov regime-switching process and a representative agent infers the current regime, normal regime, or disaster regime, sequentially from the realized path of the past consumption process. In this setting, the fluctuation in the agent’s posterior probabilities of the disaster regime augments the volatility of equity returns. By utilizing the stochastic differential utility, this study demonstrates that the current model can help resolve many asset pricing puzzles including the equity premium puzzle, equity volatility puzzle, and risk-free rate puzzle simultaneously. Further, the current model predicts the counter-cyclical pattern in the equity premium and equity-return volatility on the normal regime, although asset returns are negative and highly volatile during disasters. The study also demonstrates that, if the agent’s preferences are restricted to time-additive power utility, the consideration of hidden persistent disasters deepens the asset pricing puzzles.

Macroeconomic determinants of stock volatility and volatility premiums

How does stock market volatility relate to the business cycle? We develop, and estimate, a no-arbitrage model, and find that (i) the level and fluctuations of stock volatility are largely explained by business cycle factors and (ii) some unobserved factor contributes to nearly 20% to the overall variation in volatility, although not to its ups and downs. Instead, this “volatility of volatility” relates to the business cycle. Finally, volatility risk-premiums are strongly countercyclical, even more than stock volatility, and partially explain the large swings of the VIX index during the 2007–2009 subprime crisis, which our model captures in out-of-sample experiments.

Are Derivative Warrants Overpriced?

This study investigates the pricing efficiency of Hang Seng Index (HSI) derivative warrants in Hong Kong. Different from similar research, the study examines the pricing efficiency of index warrants by comparing their implied volatilities (IV) with realized volatility (RV). Although prior studies find that warrants are more expensive than the corresponding options, they are not necessarily overpriced in the conventional sense—that is, relative to the RV. This approach allows the study to test the pricing efficiency of warrants via a test of their information content. Moreover, unlike studies that focus on data at market close, the study uses a large sample of highly synchronous intraday, firm, bid–ask quote data to avoid possible distortions arising from intraday variations in liquidity and pricing in the instruments. The data also helps eliminate the potential nonsynchronous price problem that may affect the test results. Consistent with the results from previous studies, we find that warrants are often more expensive than options. This result is attributed to the inability of non‐issuers to sell short, and the high participation rate of unsophisticated investors in the warrants market. However, regression analysis shows that IVs from ATM and OTM warrants provide unbiased volatility forecasts, and that IVs from ATM options do not subsume the information content of ATM warrants. ATM warrant prices are in line with the RV and are efficiently priced. Simulation results show that writing warrants is more profitable than writing options, and that the overpricing is directly related to the volatility premium.

Management Insights

Management Insights

Equilibrium Growth, Inflation, and Bond Yields

This paper explores bond pricing implications of a stochastic endogenous growth model with imperfect price adjustment. In this setting, the production and price-setting decisions of firms drive low-frequency movements in macro growth and inflation rates that are negatively related, as in the data. With recursive preferences, these endogenous long-run growth and inflation dynamics are crucial for explaining a number of stylized facts in bond markets. Notably, when calibrated to a wide range of macroeconomic data, the model quantitatively explains the means and volatilities of nominal bond yields. The model also generates a sizeable equity premium and high investment volatility.

Modeling Dynamic Term Structure of Equity Premia with Regime Switching Economy

This paper proposes a consumption based asset pricing model in which the dynamic term structure of equity premia captures the stylized behavior of empirical counterpart. In the fi nancial crisis in late 2000s, the level of equity premium went up and especially the equity premium for a short horizon surged with a spike. To model these phenomena, I estimate two state regime switching model for the bivariate process of consumption and dividend over the period 1930-2011, and fi nd that the risk of regime switching can explain the level and slope of term structure of equity premia and the high volatility of short duration equity premium. In the economic downturn, accompanied with low growth and high volatility, the representative agent requires higher premium on equity, and since short duration assets are more exposed to the recession, those assets are heavily discounted. Consequently, the dynamics of term structure of equity premia show the following features: countercyclical level, procyclical slope, and high volatility of short term equity premium.

Impact of Idiosyncratic Volatility on Stock Returns: A Cross-Sectional Study

This paper proposes a new approach to estimate the idiosyncratic volatility premium. In contrast to the popular two-pass regression method, this approach relies on a novel GMM-type estimation procedure that uses only a single cross-section of return observations to obtain consistent estimates. Also, it enables a comparison of idiosyncratic volatility premia estimated using stock returns with different holding periods. The approach is empirically illustrated by applying it to daily, weekly, monthly, quarterly, and annual US stock return data over the course of 2000–2011. The results suggest that the idiosyncratic volatility premium tends to be positive on daily return data, but negative on monthly, quarterly, and annual data. They also indicate the presence of a January effect.