Momentum, Market Regime and Stocks & Options Trading Strategies

2018 ◽  
Author(s):  
Oluwaseyi Awoga
Keyword(s):  
Trading Strategies ◽  
Market Regime ◽  
Options Trading

Volatility Forecast Incorporating Investors’ Sentiment and its Application in Options Trading Strategies: A Behavioural Finance Approach at Nifty 50 Index

2020 ◽  
Vol 24 (2) ◽  
pp. 217-227
Author(s):  
Kelvin Mutum
Keyword(s):  
Decision Making ◽  
Investor Sentiment ◽  
Trading Strategies ◽  
Percentage Error ◽  
Forecasting Model ◽  
Decision Making Process ◽  
Options Market ◽  
Volatility Forecasting ◽  
Behavioural Finance ◽  
Options Trading

The present study was to examine whether the performance of options trading strategies can be improved if volatility forecasting incorporating investors’ sentiment was incorporated in the decision-making process at the Indian options market. The study adopted the multiple-factor model to build the Indian volatility forecasting model. The benchmark forecasting model (BMF) includes absolute daily returns (|RA|), daily high–low range (HLR) and daily realized volatility (RV). The proxies of investors’ sentiment considered in the study were India volatility index (IVIX), advance decline ratio (ADR), put-call open interest (PCOI) and their changes. The results of the causality and regression test indicate that investors’ sentiment and their changes should be included in the forecasting model. Mean absolute percentage error (MAPE) indicates that 15-day holding period shows the minimum error. Straddle strategies were simulated 15 days ahead before the options maturity date base on the direction of the forecast for different volatility forecasting models. The simulation result shows that the options trading performance might be improved if volatility forecasting incorporating investor sentiment, particularly IVIX, was incorporated in the decision-making process at the Indian options market. From the behavioural finance point of view, the study bridges the gap between options trading, volatility forecasting and information content of investors’ sentiment at the Indian financial market.

scholarly journals DERIVATIVE MARKETS-USING OPTIONS TRADING STRATEGY IN MONGOLIAN STOCK MARKET

2021 ◽  
pp. 36-50
Author(s):  
Davaadalai Darkhabaatar
Keyword(s):  
Stock Market ◽  
Key Words ◽  
Practical Experience ◽  
Trading Strategies ◽  
Market Mechanisms ◽  
Derivative Instruments ◽  
Financial Derivative ◽  
Options Trading ◽  
Derivative Markets ◽  
Derivative Market

This study identifies some opportunities to introduce derivative instruments based on the practical experience of other countries, and provides some estimates of the implementation of derivative market mechanisms, pricing, and trading strategies. KEY WORDS: Financial derivative, option, and spread

scholarly journals Options Trading Costs Are Lower than You Think

2020 ◽  
Vol 33 (11) ◽  
pp. 4973-5014 ◽  
Author(s):  
Dmitriy Muravyev ◽  
Neil D Pearson
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Fair Value ◽  
Price Impact ◽  
Trading Strategies ◽  
Trading Costs ◽  
Options Markets ◽  
Price Changes ◽  
Options Trading ◽  
Effective Spread

Abstract Conventional estimates of the costs of taking liquidity in options markets are large. Nonetheless, options trading volume is high. We resolve this puzzle by showing that options price changes are predictable at high frequency, and many traders time executions by buying (selling) when the option fair value is close to the ask (bid). Effective spreads of traders who time executions are less than 40% of the size of conventional measures, and the overall average effective spread is one-quarter smaller than conventional estimates. Price impact measures are also affected. These findings alter conclusions about the after-cost profitability of options trading strategies.

High-Frequency Financial Econometrics

2014 ◽  
Author(s):  
Yacine Aït-Sahalia ◽  
Jean Jacod
Keyword(s):  
High Frequency ◽  
Practical Importance ◽  
Financial Econometrics ◽  
Trading Strategies ◽  
Financial Data ◽  
Jump Processes ◽  
High Frequency Trading ◽  
Microstructure Noise ◽  
Econometric Methods ◽  
Mathematical Foundations

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. The book covers the mathematical foundations of stochastic processes, describes the primary characteristics of high-frequency financial data, and presents the asymptotic concepts that their analysis relies on. It also deals with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As the book demonstrates, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. The book approaches high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.

Confidence and Investors' Reliance on Disciplined Trading Strategies

CFA Digest ◽  
2003 ◽  
Vol 33 (4) ◽  
pp. 71-71
Author(s):  
William H. Sackley
Keyword(s):  
Trading Strategies
Sign in / Sign up

Export Citation Format

Share Document