Options Trading Based on the Forecasting of Volatility Direction with the Incorporation of Investor Sentiment

Using options price data on the Taiwanese stock market, we propose an options trading strategy based on the forecasting of volatility direction. The forecasting models are constructed with the incorporation of absolute returns, heterogeneous autoregressive-realized volatility (HAR-RV), and proxy of investor sentiment. After we take into consideration the margin-based transaction costs, the results of our simulated trading indicate that a straddle trading strategy that considers the forecasting of volatility direction with the incorporation of market turnover achieves the best Sharpe ratios. Our trading algorithm bridges the gap between options trading, market volatility, and the information content of investor overreaction.     

The calm after the storm: implied volatility and future stock index returns

This article explores the predictive power of five implied volatility indices for subsequent returns on the corresponding underlying stock indices from January 2000 through October 2013. Contrary to previous research, very low volatility levels appear to be followed by significantly positive average returns over the next 20, 40 or 60 trading days. Rolling trading simulations show that positive adjusted excess returns can be achieved when long positions in the stock indices are taken on days of very low implied volatility. This may be a hint that market inefficiencies exist in some markets, especially outside the USA. The excess returns measured against a buy and hold benchmark are significant for the German and Japanese market when tested with a bootstrap methodology. The results are robust against a broad spectrum of specifications.     

运用Matlab基于LSM方法对羹式期权定价的新探究

传统期权定价方法是通过主观假定初始价格、执行价格、期限、波动率、无风险利率等条件来对期权进行定价,很少联系实际的期权市场报价对期权进行定价。本文根据股票期权市场报价,通过Matlab快速方便地求解出隐含的波动率和无风险利率,并在此基础上运用Matlab基于最／bZ．乘蒙特卡洛模拟（LSM）方法对该股票的美式期权进行定价。本文揭示了如何根据期权市场报价实现隐含波动率和无风险利率的求解,进而结合LSM方法对美式期权进行定价的一种新方法。此外,本文对LSM方法的改进技术也进行了探讨。     

Modelling the Implied Volatility of Options on Long Gilt Futures

This paper investigates the properties of implied volatility series calculated from options on Treasury bond futures, traded on LIFFE. We demonstrate that the use of near-maturity at the money options to calculate implied volatilities causes less mis-pricing and is therefore superior to, a weighted average measure encompassing all relevant options. We demonstrate that, whilst a set of macroeconomic variables has some predictive power for implied volatilities, we are not able to earn excess returns by trading on the basis of these predictions once we allow for typical investor transactions costs.     

The relation between implied and realised volatility in the Danish option and equity markets

We show that the conclusions to be drawn concerning the informational efficiency of illiquid options markets depend critically on whether one carefully recognises and appropriately deals with the econometrics of the errors‐in‐variables problem. This paper examines the information content of options on the Danish KFX share index. We consider the relation between the volatility implied in an option's price and the subsequently realised index return volatility. Since these options are traded infrequently and in low volumes, the errors‐in‐variables problem is potentially large. We address the problem directly using instrumental variables techniques. We find that when measurement errors are controlled for, call option prices even in this very illiquid market contain information about future realised volatility over and above the information contained in historical volatility.     

Interest Rate Derivatives in a Duffie and Kan Model with Stochastic Volatility: An Arrow-Debreu Pricing Approach

Simple analytical pricing formulae have been derived, by different authors and for several derivatives, under the Gaussian Langetieg (1980) model. The purpose of this paper is to use such exact Gaussian solutions in order to obtain approximate analytical pricing formulas under the most general stochastic volatility specification of the Duffie and Kan (1996) model. Using Gaussian Arrow-Debreu state prices, first order stochastic volatility approximate pricing solutions will be derived only involving one integral with respect to the time-to-maturity of the contingent claim under valuation. Such approximations will be shown to be much faster than the existing exact numerical solutions, as well as accurate.     

Binomial Option Pricing Biases and Inconsistent Implied Volatilities

We evaluate the binomial option pricing methodology (OPM) by examining simulated portfolio strategies. A key aspect of our study involves sampling from the empirical distribution of observed equity returns. Using a Monte Carlo simulation, we generate equity prices under known volatility and return parameters. We price American–style put options on the equity and evaluate the risk–adjusted performance of various strategies that require writing put options with different maturities and moneyness characteristics. The performance of these strategies is compared to an alternative strategy of investing in the underlying equity. The relative performance of the strategies allows us to identify biases in the binomial OPM leading to the well–known volatility smile . By adjusting option prices so as to rule out dominated option strategies in a mean–variance context, we are able to reduce the pricing errors of the OPM with respect to option prices obtained from the LIFFE. Our results suggest that a simple recalibration of inputs may improve binomial OPM performance.     

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.