Modeling the joint dynamics of risk-neutral stock index and bond yield volatilities

This study examines the joint evolution of risk-neutral stock index and bond yield volatilities by using the Chicago Board Option Exchange S&P500 volatility index (VIX) and the Bank of America Merrill Lynch Treasury Option Volatility Estimate Index (MOVE). I use bivariate regime-switching models to investigate the alternation of “high-risk” and “low-risk” markets, where the high-risk regime is characterized by higher and more volatilities with weaker cross-market linkages. Common information about economic and financial conditions appears to drive VIX and MOVE fluctuations between the two risk regimes. Two-regime specifications also distinguish between information spillover and common information effects. Ignoring regime shifts leads to spurious extreme persistence and incomplete inferences about asymmetric volatility. The findings carry important implications for asset allocation.     

Asymmetric information about volatility: How does it affect implied volatility,option prices and market liquidity?

This paper develops a model of asymmetric information in which an investor has information regarding the future volatility of the price process of an asset and trades an option on the asset. The model relates the level and curvature of the smile in implied volatilities as well as mispricing by the Black-Scholes model to net options order flows (to the market maker). It is found that an increase in net options order flows (to the market maker) increases the level of implied volatilities and results in greater mispricing by the Black-Scholes model, besides impacting the curvature of the smile. The liquidity of the option market is found to be decreasing in the amount of uncertainty about future volatility that is consistent with existing evidence. This revised version was published online in June 2006 with corrections to the Cover Date.     

The information content of risk-neutral skewness for volatility forecasting

The paper investigates whether risk-neutral skewness has incremental explanatory power for future volatility in the S&P 500 index. While most of previous studies have investigated the usefulness of historical volatility and implied volatility for volatility forecasting, we study the information content of risk-neutral skewness in volatility forecasting model. In particular, we concentrate on Heterogeneous Autoregressive model of Realized Volatility and Implied Volatility (HAR-RV-IV). We find that risk-neutral skewness contains additional information for future volatility, relative to past realized volatilities and implied volatility. Out-of-sample analyses confirm that risk-neutral skewness improves significantly the accuracy of volatility forecasts for future volatility.     

Structural breaks in volatility spillovers between international financial markets: Contagion or mere interdependence?

This paper conducts an investigation of volatility transmission between stock markets in Hong Kong, Europe and the United States covering the time period from 2000 up to 2011. Using intra-daily data we compute realized volatility time series for the three markets and employ a Heterogeneous Autoregressive Distributed Lag Model as our baseline econometric specification. Motivated by the presence of various crisis events contained in our sample, we detect time-variation and structural breaks in volatility spillovers. Particularly during the financial crisis of 2007, we find effects consistent with the notion of contagion, suggesting strong and sudden increases in the cross-market synchronization of chronologically succeeding volatilities. Investigating the role of mean breaks and conditional heteroskedasticity in the realized volatilities, however, we find the latter to be the main driver of breaks in volatility spillovers. Taking the volatility of realized volatilities into account, we find no evidence of contagion anymore.     

Evaluating the performance of futures hedging using factors-driven realized volatility

The complexity and uncertainty of the financial market mainly stem from the rich market internal transaction information and a wide range effect of external factors. To this end, this paper proposes the combination factors-driven forecasting method to predict realized volatilities of the CSI 300 index and index futures. Based on the volatilities predicted by the proposed method, we further evaluate the ex-ante hedging performance in comparison to the conventional HAR model as well as GARCH-type models. The empirical results indicate that the factors-driven realized volatility model significantly dominates the other commonly used models in terms of hedging effectiveness. Furthermore, the superiority of the proposed method is robust in different market conditions, including significant rising or falling and abnormal market fluctuations in the COVID-19 pandemic, and in different index markets. Therefore, this paper improves the prediction accuracy of volatility by integrating market internal transaction information and external factor information, and the proposed method in this paper can be used by investors to obtain an excellent hedging effect.     

When noise trading fades,volatility rises

We hypothesize and test an inverse relation between liquidity and price volatility derived from microstructure theory. Two important facets of liquidity trading are examined: volume and noisiness. As represented by the expected turnover rate (volume) and realized average commission cost per share (noisiness) of NYSE equity trading, both facets are found negatively associated with the ex post and ex ante return volatilities of the NYSE stock portfolios and the NYSE composite index futures. Furthermore, the inverse association between noisiness and volatility is amplified in times of market crisis. The negative noisiness–volatility relation is also supported by our analysis on the effects of trade size on price volatility. The overall results demonstrate that volatility increases as noise trading declines.     

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     

Does vega-neutral options trading contain information?

This study suggests a novel approach for decomposing net options demands into the options order imbalances with and without volatility risk. By analyzing a high-frequency index futures and options dataset, we examine the information content of (i) the direction-motivated order imbalance induced by a single option type, which is exposed to volatility risk, and (ii) that constructed by both calls and puts, which is vega-neutral. The aggregate options order imbalance does not convey information after controlling for futures market trading. However, the intraday options order imbalance by trading without volatility risk significantly predicts spot index returns, though its longer-horizon forecasting ability is relatively weak because of a possible cross-market hedging effect. The predictive abilities of informed foreigners’ trades and out-of-the-money options trading are prominent. Our empirical results suggest that the vega-neutral options trading conveys additional information distinct from the futures order imbalance.     

Assessing the compensation for volatility risk implicit in interest rate derivatives

We use the risk neutral volatilities which market participants use to price dollar, euro and pound swaptions to the aim of assessing the size and the sign of the daily compensation for interest rate volatility risk between October 1998 and August 2006. The measurement of the unobservable volatility risk premium rests on a simple garch model, which generates the parameters of the volatility process under the physical measure and produces paths of future volatilities, whose averages represent the realized volatility forecasts. Results show that interest rate volatility has embodied a large — negative — compensation for volatility risk, in line with other studies focusing on different asset classes. We also document that the volatility risk premium has exhibited a term structure across the analyzed maturity spectrum and that it has changed through time, but much less than risk neutral volatilities. Compensation for volatility risk is positively related to risk neutral volatility, although the relation is not completely linear, and it is influenced, as expected, by the level of the short term rate and its realized volatility. Also a small but robust number of macroeconomic surprises affect compensation for volatility risk, with macroeconomic uncertainty in one country spilling over to other currencies. Estimates of the risk aversion coefficient computed over the same sample as the volatility risk premium suggest that (minus) the volatility risk premium can be almost directly read as risk aversion.     

Impact of volatility estimation method on theoretical option values

The volatility of an asset price measures how uncertain we are about future asset price movements. It is one of the factors affecting option price and the only input into the Black–Scholes model that cannot be directly observed. Thus, estimating volatility properly is vital. Two approaches to calculating volatility are historical and implied volatilities. Using index options listed on the Chicago Board of Options Exchange, this paper focuses on historical volatility. Since numerous methods of estimating volatility may provide different results, this paper assesses the impact of volatility estimation method on theoretical option values.     

General approximation schemes for option prices in stochastic volatility models

In this paper we develop a general method for deriving closed-form approximations of European option prices and equivalent implied volatilities in stochastic volatility models. Our method relies on perturbations of the model dynamics and we show how the expansion terms can be calculated using purely probabilistic methods. A flexible way of approximating the equivalent implied volatility from the basic price expansion is also introduced. As an application of our method we derive closed-form approximations for call prices and implied volatilities in the Heston [Rev. Financial Stud., 1993, 6, 327–343] model. The accuracy of these approximations is studied and compared with numerically obtained values.     

Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks

Oil markets are subject to extreme shocks (e.g. Iraq’s invasion of Kuwait), causing the oil market price exhibits extreme movements, called jumps (or spikes). These jumps pose challenges on oil market volatility forecasting using conventional volatility dynamic models (e.g. GARCH model) This paper characterizes dynamics of jumps in oil market price using high frequency data from three perspectives: the probability (or intensity) of jump occurrence, the sign (e.g. positive or negative) of jumps, and the concurrence with stock market jumps. And then, the paper exploits predictive ability of these jump-related information for oil market volatility forecasting under the mixed data sampling (MIDAS) modeling framework. Our empirical results show that augmenting standard MIDAS model using the three jump-related information significantly improves the accuracy of oil market volatility forecasting. The jump intensity and negative jump size are particularly useful for predicting future oil volatility. These results are widely consistent across a variety of robustness tests. This work provides new insights on how to forecast oil market volatility in the presence of extreme shocks.     

Options markets, self-fulfilling prophecies, and implied volatilities

This paper answers the following often asked question in option pricing theory: if the underlying asset's price does not satisfy a lognormal distribution, can market prices satisfy the Black-Scholes formula just because market participants believe it should? In complete markets, if the underlying asset's objective distribution is not lognormal, then the answer is no. But, in an incomplete market, if the underlying asset's objective distribution is not lognormal and all traders believe it is, then the answer is yes! The Black-Scholes formula can be a self-fulfilling prophecy. The proof of this second assertion consists of generating an economy where self-confirming beliefs sustain the Black-Scholes formula as an equilibrium. An asymmetric information model is provided, where the underlying asset's price has stochastic volatility and drift. This model is distinct from the existing pricing models in the literature, and it provides new empirical implications concerning Black-Scholes implied volatilities and the bid/ask spread. Similar to stochastic volatility models, this model is consistent with the implied volatility “smile” pattern in strike prices. In addition, it is consistent with implied volatilities being biased predictors of future volatilities.     

The cross section of cashflow volatility and expected stock returns

I show that historical cashflow volatility is negatively related to future returns cross-sectionally. The negative association is large; economically meaningful; long-lasting up to five years; robust to known return-informative effects of size, value, price and earnings momentums and illiquidity; and extends to both systematic and idiosyncratic cashflow volatilities. Using the standard deviations of cashflow to sales and of cashflow to book equity as proxies for cashflow volatility, the least volatile decile portfolio outperforms the most volatile decile portfolio by 13% a year relative to the Fama–French four factors. The cashflow volatility effect is closely related to the idiosyncratic return volatility effect documented in Ang et al. [Ang, A., Hodrick, R.J., Xing, Y. and Zhang, X. “The cross-section of volatility and expected returns.” Journal of Finance, 51 (2006), 259–299.]. However, in portfolios simultaneously sorted on both cashflow and return volatilities, and in cross sectional regressions of returns at the firm level, these two effects neither drive out nor dominate each other. While the pricing of idiosyncratic cashflow volatility represents an anomaly against the traditional asset pricing theories, the pricing of historical cashflow uncertainty sheds light on potential fundamental risks embodied in the Fama–French HML and SMB factors.     

Option implied volatilities and the cost of issuing equity

The structure of a firm-commitment Seasoned Equity Offering (SEO) resembles a put-option underwritten by an investment bank syndicate (Smith, 1977). Employing implied volatilities from issuers’ stock options as a direct forward-looking measure, this paper examines the impact of expected price risk around SEO issue dates on the direct cost of issuing equity. Using a comprehensive sample of 1208 SEOs between 1996 and 2009, we find issuers with higher option implied volatilities raise less external equity capital and pay higher investment bank fees in the stock market, ceteris paribus. The effect of implied volatility on the investment bank fees is stronger for larger issuers with lower pre-SEO abnormal realized stock volatilities, and for SEOs with higher expected price pressures around issue dates. These relationships are robust to adjustments for correlations among control variables, sample selection bias and also simultaneous determination of offer size and SEO fees.     

A Likelihood Ratio Test of Price Volatilities: Comparing Stock Index Spot and Futures

This paper uses three methods to estimate the price volatility of two stock market indexes and their corresponding futures contracts. The classic variance measure of volatility is supplemented with two newer measures, derived from the Garman-Klass and Ball-Torous estimators. A likelihood ratio test is used to compare the classic variance measure of price volatilities of two stock market indexes and their corresponding futures contracts during the bull market of the 1980s. The stock market volatilities of the Standard & Poor's 500 (S&P 500) and New York Stock Exchange (NYSE) indexes were found to be significantly lower than their respective futures price volatilities. Since information may flow faster in the futures markets than in the corresponding stock market, our results support Ross's information-volatility hypothesis. It was also noted that the NYSE spot volatility was lower than the S&P 500 spot volatility. If the rate of information flow and firm size are positively related, then the lower NYSE spot volatility is explained by the size effect. The futures price volatilities for the two indexes were insignificantly different from each other. With stock index spot-futures price correlations approaching unity, one implication of our results for index futures activity is that smaller positions in futures contracts may suffice to achieve hedging or arbitrage goals.     

Capital controls and monetary policy autonomy in a small open economy

Is there a link between capital controls and monetary policy autonomy in a country with a floating currency? Shocks to capital flows into a small open economy lead to volatility in asset prices and credit supply. To lessen the impact of capital flows on financial instability, a central bank finds it optimal to use the domestic interest rate to “manage” the capital account. Capital account restrictions affect the behavior of optimal monetary policy following shocks to the foreign interest rate. Capital controls allow optimal monetary policy to focus less on the foreign interest rate and more on domestic variables.     

Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach

Volatility is an important element for various financial instruments owing to its ability to measure the risk and reward value of a given financial asset. Owing to its importance, forecasting volatility has become a critical task in financial forecasting. In this paper, we propose a suite of hybrid models for forecasting volatility of crude oil under different forecasting horizons. Specifically, we combine the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)-GARCH with long short-term memory (LSTM) to create three new forecasting models named GARCH–LSTM, GJR-LSTM, and GARCH-GJRGARCH LSTM in order to forecast crude oil volatility of West Texas Intermediate on different forecasting horizons and compare their performance with the classical volatility forecasting models. Specifically, we compare the performances against existing methodologies of forecasting volatility such as GARCH and found that the proposed hybrid models improve upon the forecasting accuracy of Crude Oil: West Texas Intermediate under various forecasting horizons and perform better than GARCH and GJR-GARCH, with GG–LSTM performing the best of the three proposed models at 7-, 14-, and 21-day-ahead forecasts in terms of heteroscedasticity-adjusted mean square error and heteroscedasticity-adjusted mean absolute error, with significance testing conducted through the model confidence set showing that GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling-window schemes. The contribution of the paper is that it enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature and enhances the forecasting accuracy of crude oil volatility by fusing a neural network model with multiple econometric models.     

Behavioral asset pricing under expected feedback mode

We develop an enhanced DSSW model of behavior asset pricing by introducing the expected feedback mode. Through numerical simulation, it has been theoretically proved that risky asset price is jointly determined by trend extrapolated effect of expected feedback traders and the creating space effect of noise traders, and that price fluctuation depends on the expected feedback coefficient. As expected feedback traders' expectation return has been realized and the above two effects are obviously imbalance, their confidence will deteriorate prior to price collapse, and eventually achieve self-fulfilling expectation of price reversal in the process of price momentum. This model sheds lights on financial anomalies of trade size clustering and the formation of stock bubble, besides it provides a new perspective for avoiding the “bewitching” trading and reducing market volatility.     

The Effect of the Transparency of Order Flows in a Dealer Market with Several Securities

This paper compares two trading mechanisms in a dealer market with several securities exhibiting asymmetric information and imperfect competition. These two market structures differ in the information received by market-makers. While in the first of them when setting the price of an asset, they observe the order flows of all assets, in the second one they only observe the order flow corresponding to this asset. In order to make this comparison, we analyze several market indicators such as the informed expected traded volume, the market depth, the volatility and the informativeness of equilibrium prices, and the informed traders' ex-ante expected profits. Journal of Economic Literature Classification Numbers: G10.