Options Market Research Articles

Heston‐GA Hybrid Option Pricing Model Based on ResNet50

(1) Background. This study aims to improve the accuracy of the pricing model. (2) Methods. Heston model is combined with ResNet50 convolutional neural network model. Based on the optimization of Heston model parameters by genetic algorithm (GA), ResNet50 model is used to correct the deviation between market option price and Heston price, so a new hybrid option pricing model is established based on the empirical research on the European call options of Huatai‐PB CSI 300ETF (code 510300), Harvest CSI 300ETF (code 159919), and SSE 50ETF (code 510050). (3) Results. The pricing result of the hybrid model is better than other single models and hybrid models. The model is applicable to the pricing of options with short and long remaining terms. (4) Conclusions. It is shown that the combination of Heston model and ResNet50 model with optimized parameters can ensure the interpretability of the model and enhance the nonlinear fitting ability of the model, which confirms the effectiveness of the hybrid model and provides a reference for investors and institutions to make scientific decisions.

A Study on the Potential of Online Marketing of Small and Medium Enterprises (SMEs) in India

An important part of the Indian economy comprises micro, small, and medium enterprise businesses, which contribute a great deal to industrial output, exports, employment, manufacturing for home and foreign markets, and other factors. Small Medium Enterprises in the service industry are the focus of this study, which examines the current marketing options accessible to them and the potential of online marketing. It has been divided into two parts. The evaluations focused on the sorts of service organizations in the Small Medium Enterprise sector that use SME marketing and social media platforms to sell themselves online. According to the study’s findings, small Medium Enterprise service providers have been shown to represent a niche market in their respective industries. For small and medium enterprise businesses, online media has grown in relevance over time, and it might be a great platform for marketing success. Online marketing and social media facilitate the acquisition, nurturing, and consolidation of leads based on recommendations and reviews, allowing companies to better interact with customers. Research has proven that internet marketing is cost-effective to establish and retain client lists for small and medium enterprises.

Methods and Analysis of Collar Strategies

An equity portfolio with a collar strategy consists of a long position in the underlying index together with long put options for insurance and short call options to mitigate the cost of insurance or enhance the return. Using the S&P/ASX 200 index, this study investigates the performances of fourteen collar strategies and for comparison two protective put strategies. We consider two simulations: one with traded option market data and the other utilizing Black-Scholes option prices. The investigation period is 2008−2016 and we consider three sub-periods representing different market conditions. The active collar strategies considered follow those introduced by Szado & Schneeweis (2010a). As a summary of our results, the best performing strategies are a protective put and a zero-cost collar.

Information and the arrival rate of option trading volume

AbstractIn this paper we investigate the interaction between liquidity and information in the options market and its impact on the pricing of the underlying asset. We model option trade duration and volume jointly, for the first time, as a natural measure of options' trading intensity and we associate it with differential degrees of information present in option trades. We report a highly significant association between option trading intensity with contemporaneous and future underlying volatility and returns, which is robust to the presence of other information measures, market factors, and structural forms.

The Skewness Risk in the Energy Market

In this paper, we study the skewness risk and its return predictability in the energy market. Skewness risk is often used to measure the possibility of market crash. We study both physical skewness (market skewness and cross-sectional average realized skewness) estimated from underlying stock returns and risk-neutral skewness evaluated from the options market. We find a significant positive relationship between one-month-ahead market return and average realized skewness in the energy market. This unique feature should be noted by investors and carefully considered by energy policymakers.

The information content of the volatility index options trading volume

AbstractThis paper investigates the predictive content of the Volatility Index (VIX) options trading volume for the future dynamics of the underlying VIX index. Using a novel data set from the Chicago Board Options Exchange, we calculate the put–call ratio based on the VIX option volume initiated by buyers to open new positions. We show that the put–call ratio negatively predicts the subsequent changes in the VIX index. The predictability is stronger during periods of elevated VIX levels and for short‐dated contracts. These results support the hypothesis that informed traders use the VIX option market as a venue for their trading.

Information content of order imbalance in the index options market

We use proprietary transaction level data of Borsa Istanbul to compute the order imbalance of index options in order to investigate the linkages between option trades and spot index returns. Our findings show that weeks with higher call (put) order imbalance are associated with higher (lower) contemporaneous spot index returns. In addition, higher call order imbalance significantly predicts negative next-week index returns. The spot index return predictability by call options is absorbed neither by the stock order imbalance nor the index futures imbalance. Indeed, this predictability is consistent with the view that the hedging demand of counterparties in the option market that leads to the transfer of order imbalance from option market to stock market is the driver of predictability. Results are robust after controlling for various factors.

Volatility model applications in China's SSE50 options market

AbstractWe investigate the effectiveness of various volatility models using China's Shanghai Stock Exchange‐50 (SSE50) Index. Regarding in‐sample fit, the generalized autoregressive conditional heteroscedasticity (GARCH) and the variants of the GARCH model perform much better than the autoregressive conditional heteroscedasticity model. However, we do not observe any significant asymmetric volatility response to past returns in the GJR–GARCH model. For out‐of‐sample forecasting of the future realized volatility, in five of the seven options we investigate, the GARCH volatility forecast outperforms the option implied volatility. We formulate a trading strategy by exploiting the spread between the GARCH volatility forecast and the option implied volatility, and show robust profits when applied to the SSE50 options.

Risk analysis model and agricultural derivative market use

This research aims to build conceptual guidelines regarding price risk management through the agricultural derivatives market. Specifically, to identify the common price risk management methods and strategies employed, the risk analysis models of derivative markets, and the barriers to agricultural risk management. This is an integrative review, the search for literature on the models of risk management analysis of agricultural derivatives started by listing the largest possible number of keywords on the topic, in the Scopus and Web of Science. Forty-five publications were found meeting the pre-established criteria that served as the basis for this research. Based on the literature review, we list the main information on the subject and we also propose a theoretical model for analyzing the market risks of agricultural derivatives. Still, it was possible to notice that among the methodologies for measuring market risk, Value at Risk (VaR) stands out. We exemplify and demonstrate the existence of several statistical analyzes and mathematical models, as well as software available for the management of price risks. It is concluded that strategies with the futures and options market, even though they are the most efficient for risk management, lack incentives to become practical.

Learning sequential option hedging models from market data

Following a direct data-driven approach, we propose a robust encoder-decoder Gated Recurrent Unit (GRU), GRU δ , for optimal discrete option hedging. The proposed GRU δ utilizes the Black-Scholes model as a pre-trained model and incorporates sequential information and feature selection. Using the S&P 500 index European option market data, we demonstrate that the weekly and monthly hedging performance of the proposed GRU δ significantly surpasses that of the data-driven minimum variance (MV) method, the regularized kernel data-driven model, and the SABR-Bartlett method. In addition, the daily hedging performance of the proposed GRU δ also surpasses that of MV methods based on parametric models, the kernel method, and SABR-Bartlett method.

Strategic marketing initiatives for small co-operative enterprises generated from SWOT-TOWS analysis and evaluated with PROMETHEE-GAIA

• It offers a methodology in defining, developing, and prioritizing strategic marketing initiatives for co-operative enterprises. • The crafting of strategic marketing initiatives is carried out through a SWOT-TOWS analysis. • The PROMETHEE-GAIA approach is used to determine priority strategic marketing initiatives. • Partnerships with secondary and tertiary co-operatives on R&D activities is the most preferred initiative. • The inclusion of competitive employee compensation and benefits yields the least preferred initiative. Due to the long-term agenda strategic marketing initiatives pose to co-operative enterprises, the development and the priority identification of these initiatives under multiple criteria must be rigorous and systematic for greater tractability of the decision-making processes. Thus, this work offers an integrated methodological framework in determining the viable strategic marketing options of the case co-operative based on a SWOT-TOWS analysis, adopting relevant strategic marketing criteria for small co-operatives, and evaluating the degree of importance of identified strategic marketing initiatives via a Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) - Geometrical Analysis for Interactive Decision Aid (GAIA) approach, popularly known as PROMETHEE-GAIA. The proposed framework elucidates inclusive strategy development and systematic decision analysis under a group decision-making environment. A case study of a small co-operative enterprise in the Philippines is used to demonstrate the applicability of the proposed integrated approach. The case findings suggest that partnerships with secondary and tertiary co-operatives on R&D activities as the most preferred initiative, followed by the implementation of business process improvement activities for customer relationship management. Meanwhile, the inclusion of competitive employee compensation and benefits yields as the least preferred initiative. The results of the PROMETHEE are further analyzed with GAIA in order to gain detailed insights into the case decision-making problem. The sensitivity analysis yields stability intervals for criteria weights. Also, it is found that the PROMETHEE results are robust to changes in the choice of criterion types and minor changes in parameter values of the criteria. This work advances the domain literature by offering small co-operative enterprises an integrated approach to systematically develop and evaluate strategic marketing initiatives – crucial activities for planning, resource allocation, and policymaking.

Consumer Demand for Circular Products: Identifying Customer Segments in the Circular Economy

Understanding consumer preferences in the circular economy can help producers develop profitable strategies, lowering the risk involved in transitioning to circular business models and circular product design. This study uses a choice experiment to identify customer segments for mobile phones and robot vacuum cleaners at different levels of circularity. The experiment observes how a product’s theoretical Circular Economy Score (ranging from 0 to 100) influences consumer preferences as compared to other product attributes like price, appearance, warranty, battery life, reseller type, or ease of repair. Drawing from 800 UK respondents, the results indicate the presence of three customer segments that are sensitive to a product’s Circular Economy Score, including two that appear willing to purchase recirculated items and one that expresses a preference against them. The results offer initial evidence that a market for recirculated consumer electronics exists and that circularity labeling is a marketable option. The results also present a strong rationale for further research that probes a greater variety of products and contexts.

Blockchain for Transacting Energy and Carbon Allowance in Networked Microgrids

This paper proposes a blockchain application for transacting energy and carbon allowance in networked microgrids (MGs). MGs submit trading energy and carbon allowance data to the centralized distribution system operator (DSO) operation, which would optimize the provision of energy and carbon allowance trading among MGs for satisfying power distribution network constraints. The hourly demand response along with onsite MG generation and the DSO’s trading exchanges with ISO are considered among market options to maximize MG payoffs and satisfy distribution network constraints. A cooperative game with externalities is applied to model the market behavior of networked MGs. A two-stage payoff allocation problem is devised to allocate the grand coalition payoff to participating MGs. A solution algorithm is proposed which consists of column-and-constraint generation (C&CG) and Karush-Kuhn-Tucker (KKT) conditions to solve the proposed two-stage market optimization problem with acceptable computational performance. Also, blockchain is applied to provide secure and effective transaction settlements and transparent distribution market operations in the proposed transactive energy and carbon allowance trading strategy. The proposed centralized transactive market is tested on a 4-MG system, the IEEE 33-bus system, and the IEEE 123-bus system. The numerical results show the effectiveness of the proposed method in incentivizing MGs to trade energy and carbon allowance while satisfying the distribution network constraints.

Immigrant Entrepreneurship: The Effect of Early Career Immigration Constraints on New Venture Formation

We examine how institutional factors may affect microlevel career decisions by individuals to create new firms by impacting their ability to exercise entrepreneurial preferences, their accumulation of human capital, and the opportunity costs associated with new venture formation. We focus on an important institutional factor—immigration-related work constraints—given that technologically intensive firms in the United States not only draw upon immigrants as knowledge workers but also because such firms are disproportionately founded by immigrants. We examine the implications of these constraints using the National Science Foundation’s Scientists and Engineers Statistical Data System, which tracks the careers of science and engineering graduates from U.S. universities. Relative to natives, we theorize and show that immigration-related work constraints in the United States suppress entrepreneurship as an early career choice of immigrants by restricting labor market options to paid employment jobs in organizational contexts tightly matched with the immigrant’s educational training (job-education match). Work experience in paid employment job-education match is associated with the accumulation of specialized human capital and increased opportunity costs associated with new venture formation. Consistent with immigration-related work constraints inhibiting individuals with entrepreneurial preferences from engaging in entrepreneurship, we show that when the immigration-related work constraints are released, immigrants in job-education match are more likely than comparable natives to found incorporated employer firms. Incorporated employer firms can both leverage specialized human capital and provide the expected returns needed to justify the increased opportunity costs associated with entrepreneurial entry. We discuss our study’s contributions to theory and practice.

SPX Calibration of Option Approximations under Rough Heston Model

The volatility of stock return does not follow the classical Brownian motion, but instead it follows a form that is closely related to fractional Brownian motion. Taking advantage of this information, the rough version of classical Heston model also known as rough Heston model has been derived as the macroscopic level of microscopic Hawkes process where it acts as a high-frequency price process. Unlike the pricing of options under the classical Heston model, it is significantly harder to price options under rough Heston model due to the large computational cost needed. Previously, some studies have proposed a few approximation methods to speed up the option computation. In this study, we calibrate five different approximation methods for pricing options under rough Heston model to SPX options, namely a third-order Padé approximant, three variants of fourth-order Padé approximant, and an approximation formula made from decomposing the option price. The main purpose of this study is to fill in the gap on lack of numerical study on real market options. The numerical experiment includes calibration of the mentioned methods to SPX options before and after the Lehman Brothers collapse.

The Profitability of Volatility Trading on Exchange-traded Dollar-rupee Options: Evidence of a Volatility Risk Premium?

This article examines the profitability of short volatility strategies in the exchange-traded USDINR options market. Returns from delta-hedged short positions in straddles, strangles and individual call and put options are examined across different trading horizons and volatility regimes. The study finds that short volatility strategies yield significant mean and median returns regardless of the trading horizon and option moneyness before considering transaction costs. This is suggestive of a volatility risk premium priced in USDINR options. However, the returns are found to be insignificant and even negative after accounting for trading costs such as bid-ask spreads and brokerage. The study concludes that although USDINR options appear to be overpriced because of the volatility risk premium, short option strategies can be profitably exploited only by market makers and institutional investors facing low spreads and funding costs. The findings are suggestive of an informationally efficient market.

Order imbalance and commonality: Evidence from the options market

Using a market model and principal component analysis, we investigate the existence of common effects in order imbalance in the Borsa Istanbul's option market. Accordingly, we find the presence of commonality in order imbalance for call options and an even more dominant presence in put options. We investigate the impact of this commonality on the underlying equity market's price discovery; however, the results indicate no significant impact. Our results suggest that, from the order imbalance perspective, equity order imbalance contributes more than options to explaining stock return variations.

A pre-predictive analysis of trading volume in the options market

Subject. This article examines the relationship between the three indicators of the derivatives market, namely price, trading volume, and open interest. Objectives. The article aims to characterize the dynamics of option trading volumes in terms of regularity and predictability of changes. Methods. For the study, we used general scientific methods. Results. The article substantiates the expediency of using fractal analysis methods to identify the stability of market trends, describes approaches to the classification of options, on the basis of which the range of options traded on the Moscow Exchange is characterized, and conducts a pre-predictive analysis of the time series of trading volume. Relevance. The results of the study can be useful to persons studying financial markets, market analysts and developers of option contracts.

American option pricing with machine learning: An extension of the Longstaff-Schwartz method

Pricing American options accurately is of great theoretical and practical importance. We propose using machine learning methods, including support vector regression and classification and regression trees. These more advanced techniques extend the traditional Longstaff-Schwartz approach, replacing the OLS regression step in the Monte Carlo simulation. We apply our approach to both simulated data and market data from the S&P 500 Index option market in 2019. Our results suggest that support vector regression can be an alternative to the existing OLS-based pricing method, requiring fewer simulations and reducing the vulnerability to misspecification of basis functions.

Statistical arbitrage in the freight options market

ABSTRACT We investigate whether there are consistent misspecifications of volatility in the freight options markets that can be exploited in profitable trading strategies. We derive smooth forward freight rate curves from observed market prices and convert these to term structures of historical volatility for the Capesize, Panamax and Supramax segments of the drybulk shipping market. The differences between the historical and implied volatility term structures form the basis for executing option trading strategies. We find that there exist statistical arbitrage profits in the freight option markets, suggesting a degree of market inefficiency.