Stochastic Arbitrage Opportunities for Stock Index Options

2018 ◽  
Author(s):  
Thierry Post ◽  
IIaki Rodrrguez Longarela
Keyword(s):  
Stock Index ◽  
Index Options ◽  
Arbitrage Opportunities

Risk Arbitrage Opportunities for Stock Index Options

2020 ◽  
Author(s):  
Thierry Post ◽  
Iňaki Rodríguez Longarela
Keyword(s):  
Abnormal Returns ◽  
Stock Index ◽  
Small Scale ◽  
Option Prices ◽  
Index Options ◽  
Arbitrage Opportunities ◽  
Data Limitations ◽  
Out Of Sample ◽  
Risk Arbitrage ◽  
Equity Index

Research about equity index options has shown that option prices systematically violate rational pricing bounds for the risk-averse representative investor. These results raise the question of whether profitable trading possibilities exist in this market. Standard portfolio optimization does not apply because of the large bid-ask spreads and low quote sizes in this market. Motivated by these complications, a system of linear inequalities is developed that completely characterizes all risk arbitrage opportunities in the presence of transaction costs and portfolio restrictions. The practical use of this system is illustrated with an application to front-month S&P500 stock index options. Small-scale portfolios seem to produce surprisingly large abnormal returns out of sample; outperformance, however, seems elusive for institutional investors because of the limited quote size, possibly reflecting data limitations.

A COMPARISON OF NEURAL NETWORK MODEL SELECTION STRATEGIES FOR THE PRICING OF S&P 500 STOCK INDEX OPTIONS

2007 ◽  
Vol 16 (06) ◽  
pp. 1093-1113 ◽  
Author(s):  
N. S. THOMAIDIS ◽  
V. S. TZASTOUDIS ◽  
G. D. DOUNIAS
Keyword(s):  
Neural Network ◽  
Model Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Information Criteria ◽  
Stock Index ◽  
Statistical Hypothesis ◽  
Neural Network Models ◽  
Index Options ◽  
Pruning Technique

This paper compares a number of neural network model selection approaches on the basis of pricing S&P 500 stock index options. For the choice of the optimal architecture of the neural network, we experiment with a “top-down” pruning technique as well as two “bottom-up” strategies that start with simple models and gradually complicate the architecture if data indicate so. We adopt methods that base model selection on statistical hypothesis testing and information criteria and we compare their performance to a simple heuristic pruning technique. In the first set of experiments, neural network models are employed to fit the entire options surface and in the second they are used as parts of a hybrid intelligence scheme that combines a neural network model with theoretical option-pricing hints.

scholarly journals How Risky Are the Options? A Comparison with the Underlying Stock Using MaxVaR as a Risk Measure

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 76
Author(s):  
Saswat Patra ◽  
Malay Bhattacharyya
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Value At Risk ◽  
Risk Measure ◽  
Stock Index ◽  
Risk Exposure ◽  
Index Options ◽  
Significance Level ◽  
Significance Levels ◽  
Better Than

This paper investigates the risk exposure for options and proposes MaxVaR as an alternative risk measure which captures the risk better than Value-at-Risk especially. While VaR is a measure of end-of-horizon risk, MaxVaR captures the interim risk exposure of a position or a portfolio. MaxVaR is a more stringent risk measure as it assesses the risk during the risk horizon. For a 30-day maturity option, we find that MaxVaR can be 40% higher than VaR at a 5% significance level. It highlights the importance of MaxVaR as a risk measure and shows that the risk is vastly underestimated when VaR is used as the measure for risk. The sensitivity of MaxVaR with respect to option characteristics like moneyness, time to maturity and risk horizons at different significance levels are observed. Further, interestingly enough we find that the MaxVar to VaR ratio is higher for stocks than the options and we can surmise that stock returns are more volatile than options. For robustness, the study is carried out under different distributional assumptions on residuals and for different stock index options.

scholarly journals Predictability of OTC Option Volatility for Future Stock Volatility

2020 ◽  
Vol 12 (12) ◽  
pp. 5200
Author(s):  
Jungmu Kim ◽  
Yuen Jung Park
Keyword(s):  
Information Content ◽  
Unbiased Estimator ◽  
Implied Volatility ◽  
Realized Volatility ◽  
Stock Index ◽  
Options Market ◽  
Over The Counter ◽  
Index Options ◽  
Stock Volatility ◽  
Future Realized Volatility

This study explores the information content of the implied volatility inferred from stock index options in the over-the-counter (OTC) market, which has rarely been studied in the literature. Using OTC calls, puts, and straddles on the KOSPI 200 index, we find that implied volatility generally outperforms historical volatility in predicting future realized volatility, although it is not an unbiased estimator. The results are more apparent for options with shorter maturity. However, while implied volatility has strong predictability during normal periods, historical volatility is superior to implied volatility during a period of crisis due to the liquidity contraction of the OTC options market. This finding suggests that the OTC options market can play a role in conveying important information to predict future volatility.

CHANCE DISCOVERY IN STOCK INDEX OPTION AND FUTURES ARBITRAGE

2005 ◽  
Vol 01 (03) ◽  
pp. 435-447 ◽  
Author(s):  
EDWARD TSANG ◽  
SHERI MARKOSE ◽  
HAKAN ER
Keyword(s):  
Historical Data ◽  
Stock Index ◽  
Domain Experts ◽  
Programming Tool ◽  
Promising Tool ◽  
Arbitrage Opportunities ◽  
Out Of Sample ◽  
Computer Scientists ◽  
The One ◽  
Index Option

The prices of the option and futures of a stock both reflect the market's expectation of futures changes of the stock's price. Their prices normally align with each other within a limited window. When they do not, arbitrage opportunities arise: an investor who spots the misalignment will be able to buy (sell) options on the one hand, and sell (buy) futures on the other and make risk-free profits. Historical data suggest that option and futures prices on the LIFFE Market do not align occasionally. Arbitrage chances are rare. Besides, they last for seconds only before the market adjusts itself. The challenge is not only to discover such chances, but to discover them ahead of other arbitragers. In the past, we have introduced EDDIE as a genetic programming tool for forecasting. This paper describes EDDIE-ARB, a specialization of EDDIE, for forecasting arbitrage opportunities. As a tool, EDDIE-ARB was designed to enable economists and computer scientists to work together to identify relevant independent variables. Trained on historical data, EDDIE-ARB was capable of discovering rules with high precision. Tested on out-of-sample data, EDDIE-ARB out-performed a naive ex ante rule, which reacted only when misalignments were detected. This establishes EDDIE-ARB as a promising tool for arbitrage chances discovery. It also demonstrates how EDDIE brings domain experts and computer scientists together.

Sign in / Sign up

Export Citation Format

Share Document