Information in the Term Structure of Yield Curve Volatility

Using a novel no‐arbitrage model and extensive second‐moment data, we decompose conditional volatility of U.S. Treasury yields into volatilities of short‐rate expectations and term premia. Short‐rate expectations become more volatile than premia before recessions and during asset market distress. Correlation between shocks to premia and shocks to short‐rate expectations is close to zero on average and varies with the monetary policy stance. While Treasuries are nearly unexposed to variance shocks, investors pay a premium for hedging variance risk with derivatives. We illustrate the dynamics of the yield volatility components during and after the financial crisis.     

Financial conditions,macroeconomic factors and disaggregated bond excess returns

Bond excess returns can be predicted by macro factors, however, large parts remain still unexplained. We apply a novel term structure model to decompose bond excess returns into expected excess returns (risk premia) and the innovation part. In order to explore these risk premia and innovations, we complement macro variables by financial condition variables as possible determinants of bond excess returns. We find that the expected part of bond excess returns is driven by macro factors, whereas innovations seem to be mainly influenced by financial conditions, before and after the financial crisis. Thus, financial conditions, such as financial stress, deserve attention when analyzing bond excess returns.     

Implied Foreign Exchange Risk Premia

This paper uses implied volatilities from foreign exchange option prices and the results of no‐arbitrage theory to estimate foreign exchange risk premia. In particular, under the assumption of no‐arbitrage, the foreign exchange risk premium is driven by the difference between investors’ market prices of risk in the two currencies. In an international economy with three currencies, sterling, US dollar and Deutschemark, we can use the information on implied volatilities of the three cross rates to derive estimates of implied or ex ante market prices of risk and of foreign exchange risk premia. The foreign exchange risk premia estimates are then compared to survey‐based risk premia.     

How asymmetric is U.S. stock market volatility?

This paper explores differences in the impact of equally large positive and negative surprise return shocks in the aggregate U.S. stock market on: (1) the volatility predictions of asymmetric time-series models, (2) implied volatility, and (3) realized volatility. Following large negative surprise return shocks, both asymmetric time-series models (such as the EGARCH and GJR models) and implied volatility predict an increase in volatility and, consistent with this, ex post realized volatility normally rises as predicted. Following large positive return shocks, asymmetric time-series models predict an increase in volatility (albeit a much smaller increase than following a negative shock of the same magnitude), but both implied and realized volatilities generally fall sharply. While asymmetric time-series models predict a decline in volatility following near-zero returns, both implied and realized volatility are normally little changed from levels observed prior to the stable market. The reasons for the differences are explored.     

The bias in Black-Scholes/Black implied volatility: An analysis of equity and energy markets

In this paper we examine the extent of the bias between Black and Scholes (1973)/Black (1976) implied volatility and realized term volatility in the equity and energy markets. Explicitly modeling a market price of volatility risk, we extend previous work by demonstrating that Black-Scholes is an upward-biased predictor of future realized volatility in S&P 500/S&P 100 stock-market indices. Turning to the Black options-on-futures formula, we apply our methodology to options on energy contracts, a market in which crises are characterized by a positive correlation between price-returns and volatilities: After controlling for both term-structure and seasonality effects, our theoretical and empirical findings suggest a similar upward bias in the volatility implied in energy options contracts. We show the bias in both Black-Scholes/Black implied volatilities to be related to a negative market price of volatility risk. JEL Classification G12 · G13     

The joint cross-sectional variation of equity returns and volatilities

This paper analyzes the determinants of the simultaneous cross-sectional variation of return and volatility risk premia. Independently of the model specification employed, the estimated risk premium associated with the default premium beta is always positive and statistically different from zero. Moreover, the risk premium of the market volatility risk premium beta is negative and statistically significant. However, both risk factors are priced economically and statistically differently in the volatility and return segments of the market. On average, common factors in both segments explain 90% of the variability of volatility risk premium portfolios, but only 65% of the variability of equity return portfolios.     

Volatility co-movements: A time-scale decomposition analysis

In this paper, we are interested in detecting contagion from US to European stock market volatilities in the period immediately after the Lehman Brothers collapse. The analysis is based on a factor decomposition of the covariance matrix, in the time and frequency domain, using wavelets. The analysis aims to disentangle two components of volatility contagion (anticipated and unanticipated by the market). Once we focus on standardized factor loadings, the results show no evidence of contagion (from the US) in market expectations (coming from implied volatility) and evidence of unanticipated contagion (coming from the volatility risk premium) for almost any European country. Finally, the estimation of a three-factor model specification shows that a European common shock plays an important role in determining volatility co-movements mainly in the tranquil period, while in the period of financial turmoil, the US common shock is the main driver of volatility co-movements.     

Oil prices implied volatility or direction: Which matters more to financial markets?

We examine the impact of oil price uncertainty on US stock returns by industry using the US Oil Fund options implied volatility OVX index and a GJR-GARCH model. We test the effect of the implied volatility of oil on a wide array of domestic industries’ returns using daily data from 2007 to 2016, controlling for a variety of variables such as aggregate market returns, market volatility, exchange rates, interest rates, and inflation expectations. Our main finding is that the implied volatility of oil prices has a consistent and statistically significant negative impact on nine out of the ten industries defined in the Fama and French (J Financ Econ 43:153–193, 1997) 10-industry classification. Oil prices, on the other hand, yield mixed results, with only three industries showing a positive and significant effect, and two industries exhibiting a negative and significant effect. These findings are an indication that the volatility of oil has now surpassed oil prices themselves in terms of influence on financial markets. Furthermore, we show that both oil prices and their volatility have a positive and significant effect on corporate bond credit spreads. Overall, our results indicate that oil price uncertainty increases the risk of future cash flows for goods and services, resulting in negative stock market returns and higher corporate bond credit spreads.     

Why do we smile? On the determinants of the implied volatility function

We report simple regressions and Granger causality tests in order to understand the pattern of implied volatilities across exercise prices. We employ all calls and puts transacted between 16:00 and 16:45 on the Spanish IBEX-35 index from January 1994 to April 1996. Transaction costs, proxied by the bid–ask spread, seem to be a key determinant of the curvature of the volatility smile. Moreover, time to expiration, the uncertainty associated with the market and the relative market momentum are also important variables in explaining the smile.     

The cross-sectional relation between conditional heteroskedasticity,the implied volatility smile,and the variance risk premium

This paper estimates how the shape of the implied volatility smile and the size of the variance risk premium relate to parameters of GARCH-type time-series models measuring how conditional volatility responds to return shocks. Markets in which return shocks lead to large increases in conditional volatility tend to have larger variance risk premia than markets in which the impact on conditional volatility is slight. Markets in which negative (positive) return shocks lead to larger increases in future volatility than positive (negative) return shocks tend to have downward (upward) sloping implied volatility smiles. Also, differences in how volatility responds to return shocks as measured by GARCH-type models explain much, but not all, of the variations in excess kurtosis and multi-period skewness across different markets.     

FORECASTING VOLATILITY FOR PORTFOLIO SELECTION

The volatility of an asset is a primary input to the portfolio selection problem. Information about volatility is available from two sources, namely the share market and the option market. This paper examines the forecasting performance, over a three month investment horizon, of time series forecasts (from the share market) and option based implied volatilities. Three time series models, including GARCH, are used and twenty four implied volatility estimation models are employed. Using a data set of twelve UK companies, it is demonstrated that implied volatilities produce better individual forecasts than time series. However, more remarkably, forecasts combining implied volatilies and time series estimates significantly outperform both component forecasts.     

State‐preference pricing and volatility indices

This study uses a state‐preference pricing approach to develop a state‐price volatility index (SVX), as a forecast for market future realised volatility. We show that SVX is a more efficient forecaster than CBOE VIX for 30‐day realised volatility of SPX returns, using both in‐the‐sample and out‐of‐the‐sample tests. This result is robust to different measures of realised market volatilities. We also show that SVX provides a better volatility forecast than other alternative measures, including the at‐the‐money implied volatilities and GARCH (1, 1) volatility. Our results provide a foundation for forecasting higher risk‐neutral moments using the same state prices.     

China,Japan and the US Stock Markets and the Global Financial Crisis

In this paper, while focusing on the impact that the global financial crisis had on the stock markets of China, Japan, and the United States, the stock-price volatilities and linkage between these three countries are analyzed. In addition, the relationships between macroeconomic variables (real-economy variables and monetary-policy variables) and stock price volatility in each country are investigated. The estimation results of the EGARCH model revealed that although China’s stock price volatility was far greater than those of Japanese and US stock prices, China was less affected by the global financial crisis in 2007 than Japan and the United States. For China, stock price volatility was greater in the early 1990s, shortly after the stock market had been established, than in 2007 when the global financial crisis occurred. Furthermore, it has been revealed that the linkage of Chinese, Japanese, and US stock prices has increased since the global financial crisis. Moreover, Granger causality testing revealed China’s real-economy variables and monetary-policy variables do not affect China’s stock price volatility.     

On the Role of Futures Trading in Spot Market Fluctuations: Perpetrator of Volatility or Victim of Regret?

We examine the role of index futures trading in spot market volatility. We use the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) approach to measure volatility, analyze causality and feedback relations between volatilities in the spot and futures markets, and test various hypotheses in the context of a multivariate model that incorporates other macrostate variables. Our empirical results suggest index futures trading may not be blamed for the observed volatility in the spot market. Rather, we find stronger and more consistent support for the alternative posture that volatility in the futures market is an outgrowth of a turbulent cash market. We use the regret (cognitive dissonance) theory to explain our results.     

引入国债期货合约能否发挥现货市场稳定效应?——基于中国金融周期的研究视角

我国国债期货市场能否发挥稳定现货市场功能,金融周期风险是否会改变国债期货市场对现货市场波动的影响,是投资者实施风险管理和监管部门构建市场稳定机制的重要依据。本文通过信息传递机制和交易者行为两个维度探析国债期货市场发挥稳定功能的微观机理,分析金融周期风险对衍生工具稳定功能的影响,解析引入国债期货合约能否缓解金融周期波动对国债市场冲击,同时关注我国国债期货交易机制改进与现券波动关系。研究发现:(1)我国国债期货市场已实现抑制现货市场波动的功能,金融周期风险会引发现货价格波动,国债期货市场能够降低金融周期的波动冲击;(2)改善现货市场深度和套保交易是国债期货市场发挥稳定功能的微观路径,国债期货市场增进国债预期交易量流动性、减弱非预期交易量干扰,金融周期低波动区间套保交易稳定作用受到抑制;(3)国债期货投机交易和波动溢出效应助长现货市场波动,正负期现基差对国债波动影响具有非对称特征。     

Credit default swap spreads and variance risk premia

We find that the firm-level variance risk premium has a prominent explanatory power for credit spreads in the presence of market- and firm-level control variables established in the existing literature. Such predictability complements that of the leading state variable—the leverage ratio—and strengthens significantly with a lower firm credit rating, longer credit contract maturity, and model-free implied variance. We provide further evidence that (1) the variance risk premium has a cleaner systematic component than implied variance or expected variance, (2) the cross-section of firms’ variance risk premia capture systematic variance risk in a stronger way than firms’ equity returns in capturing market return risk, and (3) a structural model with stochastic volatility can reproduce the predictability pattern of variance risk premia for credit spreads.     

Using implied volatility on options to measure the relation between asset returns and variability

Prior research has documented that volatility in financial asset markets is most directly related to trading rather than calendar days, and that there is an inverse asymmetric relation between volatility and returns in both stocks and long-term bonds. We examine these relations in 37 futures options markets representing a wide variety of asset types. Using futures prices and implied volatilities from this extensive array of markets, we confirm that in all of them, save one, market volatility is more directly related to trading days. However, the nature of the association between implied volatility and underlying asset returns varies greatly across asset categories and across exchanges. Thus, we show that findings from equity markets apparently are not generalizable to other asset classes.     

An Intraweek Seasonality in the Implied Volatilities of Individual and Index Options

This paper studies intraweek seasonalities in the implied volatilities of options on stock market indices. Oneway analysis of variance isolates the daily behavior of implied volatilities. The differential between call implied volatility and put implied volatility tends to drop on Friday and rise on Monday. Relying on a synthetic futures contract created from options, an explanatory model is proposed. The model complements previous research on the difference between the intraweek behavior of stock market indices and that of derivative instruments based on the indices.     

International Capital Markets and Foreign Exchange Risk

Relations between foreign exchange risk premia, exchange ratevolatility, and the volatilities of the pricing kernels forthe underlying currencies, are derived under the assumptionof integrated capital markets. As predicted, the volatilityof exchange rates is significantly associated with the estimatedvolatility of the relevant pricing kernels, and foreign exchangerisk premia are significantly related to both the estimatedvolatility of the pricing kernels and the volatility of exchangerates. The estimated foreign exchange risk premia mostly satisfyFama’s (1984) necessary conditions for explaining theforward premium puzzle, but the puzzle remains in several caseseven after taking account of the pricing kernel volatilities.     

Empirical analysis of real and financial volatilities on stock excess returns: evidence from Taiwan industrial data

This paper tests the relation between stock excess returns and risk factors measured by volatility. The sources of the volatility are based on the volatility of macroeconomic factors and time-series volatility. To model the macroeconomic fundamentals, we divide the risk into real and financial volatilities pertinent to Taiwan's economic environment. By examining the data of indusry excess returns and market excess returns, we find evidence to reject the hypothesis that the stock excess returns are independent of the real and financial volatilities.