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.

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.

The State Price Density Implied by Crude Oil Futures and Option Prices

2021 ◽  
Author(s):  
Peter Christoffersen ◽  
Kris Jacobs ◽  
Xuhui (Nick) Pan
Keyword(s):  
The State ◽  
Stock Index ◽  
Economic Conditions ◽  
Option Prices ◽  
Oil Futures ◽  
State Price ◽  
Price Increases ◽  
Crude Oil Futures ◽  
State Price Density ◽  
Positive Return

Abstract Both large oil price increases and decreases are associated with deteriorating economic conditions. The projection of the state price density (SPD) onto oil returns estimated from oil futures and option prices displays a U-shaped pattern. Because investors assign high state prices to large negative and large positive oil returns, the U-shaped SPD may steepen in either tail when economic conditions deteriorate. The positive return region of the SPD is more closely related to economic conditions. The oil SPD contains information about economic conditions and future security returns that is distinct from the information in the stock index SPD.

Predicting the trend of stock index based on feature engineering and CatBoost model

2021 ◽  
pp. 2150027
Author(s):  
Renzhe Xu ◽  
Yudong Chen ◽  
Tenglong Xiao ◽  
Jingli Wang ◽  
Xiong Wang
Keyword(s):  
Time Scale ◽  
Stock Index ◽  
Feature Engineering ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Back Testing ◽  
Price Series ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Sharp Ratio

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.

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.

Usefulness of Accounting Estimates

2016 ◽  
Vol 32 (1) ◽  
pp. 123-135 ◽  
Author(s):  
Li Li Eng ◽  
Thanyaluk Vichitsarawong
Keyword(s):  
Cash Flow ◽  
Exploratory Study ◽  
Abnormal Returns ◽  
Cash Flows ◽  
Recent Period ◽  
Future Earnings ◽  
China And India ◽  
Accounting Estimates ◽  
Out Of Sample ◽  
Over Time

This is an exploratory study to examine the quality or usefulness of accounting estimates of companies in China and India over time. Specifically, we examine how well the accounting estimates are able to predict future earnings and cash flows during the period 2003-2013. The results for India indicate that the out-of-sample earnings and cash flow predictions derived are more accurate and more efficient in the more recent period (2010-2013) than the earlier period (2003-2006). In contrast, the out-of-sample earnings and cash flow predictions for China are generally more biased, less accurate, and less efficient. The results indicate abnormal returns earned on hedge portfolios formed on earnings (cash flow) predictions for India in the recent period. In contrast, none of the portfolios for China earn positive returns. The results suggest that the accounting estimates in India in recent years have become better predictors of future earnings and cash flow than accounting estimates in the earlier period. However, the accounting estimates in China are not relevant for predicting earnings and cash flows over the years in the sample period.

Thanks, neighbor! Stock market experience around significant European tax cuts

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huabing Wang ◽  
Anne Macy
Keyword(s):  
Stock Market ◽  
Spillover Effect ◽  
Abnormal Returns ◽  
The Other ◽  
Stock Index ◽  
Corporate Tax ◽  
Cross Sectional ◽  
Content Type ◽  
Study Approach ◽  
Tax Cuts

PurposeThis paper analyzes the effect of corporate tax cuts on the competitiveness of the tax-cutting countries and neighbor countries.Design/methodology/approachThis study utilizes four significant corporate tax reforms among the OECD countries in Europe that offer a one-time tax cut of 6% or more. The short-term event study approach examines the stock index reactions for both the tax-cutting countries and the other countries. Multivariate fixed-effect regressions are employed to study the cross-sectional variations in the non-tax-cut countries.FindingsThis paper finds positive excess returns for Slovakia and Germany around the tax-cut passage. Multivariate analysis of stock market reactions of the non-tax-cutting countries reveals some evidence supporting both the positive spillover effect and the negative competitive loss effect. More advanced countries are more likely to experience higher abnormal returns, while higher tax countries are more likely to suffer lower abnormal returns. Other factors identified that might have influenced the effect of a foreign tax cut include the existing trade flows with the tax-cutting countries, whether the country has a common currency and the export orientation of the economy.Research limitations/implicationsThe findings are subject to sample-size issues. The lack of results for the other two countries is due to complicating events, as suggested by the further investigation of concurrent news events around the event days.Practical implicationsThe simultaneous analysis of the reform countries and the other countries in the region suggests that policymakers need to consider the relative positioning of their country vs the other countries in terms of economic development and current tax burdens when determining the optimal policy for their country or to respond to the tax policy changes in the other countries.Originality/valueThis study offers empirical evidence regarding the effect of corporate tax changes on competitiveness through the lens of stock markets' reactions, which depend on the net results of the spillover gain vs the competitive loss.

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