Options Trading and Hedging Strategies Based on Market Data Analytics

Hedging Strategies Used in Selection of “Options” and “Forward” Contracts in Derivative Market

Shanlax International Journal of Commerce ◽

10.34293/commerce.v8i1.837 ◽

2020 ◽

Vol 8 (1) ◽

pp. 1-12

Author(s):

Jayaraman Balakrishnan

Keyword(s):

Risk Management ◽

Forward Contracts ◽

Hedging Strategy ◽

Price Movement ◽

Hedging Strategies ◽

Options Trading ◽

Derivative Market ◽

Selection Of

This Article focuses on the derivatives market, which has crossed several milestones during its developing phase, but there is still a long way to go, mainly when the International derivatives market has seen a variety of products, with sufficient liquidity, depth, and volume. One remarkable thing in the derivative market was the existence of forwarding contracts. But the major milestone in developing the derivatives market in India was the introduction of Options. The objective of introducing Options was to provide a complicated hedging strategy for the corporate in its risk management activities. Options trading can be taken to the next level with the help of understanding of Greeks (Delta Δ, Gamma Γ, Vega ν, Theta Θ, Rho ρ) and their Hedging techniques. Each Greek separates a variable that can drive an option’s price movement, giving insight on how the option’s premium will vary if that variable changes.

Download Full-text

OPTIMALITY OF PAYOFFS IN LÉVY MODELS

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024914500411 ◽

2014 ◽

Vol 17 (06) ◽

pp. 1450041 ◽

Cited By ~ 10

Author(s):

ERNST AUGUST VON HAMMERSTEIN ◽

EVA LÜTKEBOHMERT ◽

LUDGER RÜSCHENDORF ◽

VIKTOR WOLF

Keyword(s):

Cost Savings ◽

Martingale Measure ◽

Market Data ◽

Hedging Strategies ◽

Cost Strategy ◽

Delta Hedging ◽

German Stock ◽

Cost Efficient ◽

The Cost ◽

Normal Inverse Gaussian

In this paper, we determine the lowest cost strategy for a given payoff in Lévy markets where the pricing is based on the Esscher martingale measure. In particular, we consider Lévy models where prices are driven by a normal inverse Gaussian (NIG)- or a variance Gamma (VG)-process. Explicit solutions for cost-efficient strategies are derived for a variety of vanilla options, spreads, and forwards. Applications to real financial market data show that the cost savings associated with these strategies can be quite substantial. The empirical findings are supplemented by a result that relates the magnitude of these savings to the strength of the market trend. Moreover, we consider the problem of hedging efficient claims, derive explicit formulas for the deltas of efficient calls and puts and apply the results to German stock market data. Using the time-varying payoff profile of efficient options, we further develop alternative delta hedging strategies for vanilla calls and puts. We find that the latter can provide a more accurate way of replicating the final payoff compared to their classical counterparts.

Download Full-text

An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction

Journal of Information Technology Research ◽

10.4018/jitr.2022010102 ◽

2022 ◽

Vol 15 (1) ◽

pp. 1-19

Author(s):

Ravinder Kumar ◽

Lokesh Kumar Shrivastav

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Stock Market ◽

Data Analytics ◽

Gradient Boosting ◽

Ensemble Model ◽

Market Data ◽

Stock Price Prediction ◽

Gradient Boosting Machine

Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics. This has opened the door of opportunities for the developer and researcher to develop intelligent and machine learning based tools and techniques for data analytics. This paper proposed an ensemble for stock market data prediction using three most prominent machine learning based techniques. The stock market dataset with raw data size of 39364 KB with all attributes and processed data size of 11826 KB having 872435 instances. The proposed work implements an ensemble model comprises of Deep Learning, Gradient Boosting Machine (GBM) and distributed Random Forest techniques of data analytics. The performance results of the ensemble model are compared with each of the individual methods i.e. deep learning, Gradient Boosting Machine (GBM) and Random Forest. The ensemble model performs better and achieves the highest accuracy of 0.99 and lowest error (RMSE) of 0.1.

Download Full-text

Evidence-based regulation of labour migration in OECD countries: setting quotas, selection criteria, and shortage lists

MIGRATION LETTERS ◽

10.33182/ml.v11i1.166 ◽

2014 ◽

Vol 11 (1) ◽

pp. 11-22 ◽

Cited By ~ 3

Author(s):

Jonathan Chaloff

Keyword(s):

Labour Market ◽

Selection Criteria ◽

Oecd Countries ◽

Market Analysis ◽

Multiple Criteria ◽

Labour Migration ◽

Evidence Based ◽

Periodic Review ◽

Market Data ◽

Almost All

The growing complexity of selection criteria for discretionary labour migration in OECD countries has been accompanied by an expanded demand for labour market analysis and consultation with stakeholders. While some features of general or detailed criteria may be fixed in legislation, numerical quotas or targets, shortage lists, and multiple-criteria points-based systems are generally subject to periodic review and revision based on labour market data and consultation with stakeholders. Official government bodies have maintained co-ordination of this process, with varying degrees of externalization. In most countries expertise is internal, with recourse to external mandated bodies rare. In almost all cases, however, the process is designed to promote consensus around the policy while maintaining political control.

Download Full-text

Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Download Full-text

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

Download Full-text