Taiwan Stock Index Options Research Articles

Forecasting Volatility With Spot Index and Index Futures: Evidence From Taiwan

In this article, we use the volatility of the Taiwan Stock Index (TAIEX) and its futures in the encompassing regression model to respectively make asynchronous forecasts of realized volatility (RV) and implied volatility (IV). Initially, we discover that, to obtain a stationary RV with a stable, long memory parameter, the optimal sampling intervals for the intraday return were 9 and 30 minutes. We uncover that the spot volatility is more predictive of RV than the futures volatility. To forecast IV, the volatility of futures has more information content, helping to improve overall forecast performance. The result implies that the underlying asset of the TAIEX options (TXO) is approximately the index futures rather than the spot index, owing mainly to the demands for hedging and arbitrage from the TXO holders. Finally, we confirm the forecasting capability of our various models via extensive forecasting error and simulation exercises.

Forecasting Volatility in Taiwan with Encompassing Regression Models

Volatility forecasting is important both theoretically and in practice, varying by forecasting methods and financial markets. In this article, we explore this topic in the Taiwanese markets, using the encompassing regression models. We use the volatility of the Taiwan Stock Index (TAIEX) and its futures in the encompassing regression model to respectively make asynchronous forecasts of realized volatility (RV) and implied volatility (IV). Besides trading frequency, we find that transaction matching time is a key factor for obtaining steady RV values. Also, we find that the TAIEX index RV has a long memory. Moreover, we discover that, to obtain a stationary RV with a stable, long memory parameter, the optimal sampling intervals for the intraday return were nine (9) and thirty (30) minutes. In addition, we uncover that the spot volatility is more predictive of RV than the futures volatility. In the forecasting of IV, the volatility of futures has more information content, which can help improve overall forecast performance, especially when employing the ARFIMA+Jump model in the non-bear market and the ARFIMA+Jump/Leverage model in the bear market. The empirical result implies that the underlying asset of the TAIEX options (TXO) is approximately the index futures rather than the spot index, owing mainly to the demands for hedging and arbitrage from the TXO holders.

Does options trading convey information on futures prices?

This paper studies the presence of informed trading in Taiwan stock index options (TXO) and analyzes the informational role of foreign institutions in incorporating information into Taiwan stock index futures (TX). We have found that only the option-induced part (OOI) of the total TX order imbalance can predict future TX prices, and the OOI calculated from open-buy TXO, defined by Ni et al. (2008), provides incremental predictability. This finding shows that the price predictability stems from the information flow resulting from option transactions rather than from liquidity pressure. We conclude further that option transactions from foreign institutions provide the most significant predictability, out-of-the-money option transactions in particular. These empirical results show that option transactions conducted by foreign institutions have played the primary role in conveying the information inherent in the TXO market to the TX market, foreign institutions being delta-informed traders. Retail investors, the major players in both the TXO and TX markets, have done almost nothing of significance with regard to TXO information transmission into the TX market, with the exception of some near-the-money and out-of-the-money options.

Influence of individual investor sentiment on Taiwan option prices during 2007-2010 financial crisis

Purpose – This paper investigates changes in risk-neutral distribution derived from Taiwan stockindex options under different market conditions. The purpose of this paper is to explore whether individual investor sentiment significantly influences the Taiwan option prices. Design/methodology/approach – The authors adopt the optimization method to estimate the risk-neutral distribution from the Taiwan stock index options and use the t-test to examine the difference in risk-neutral skewness, kurtosis, and confidence interval between the pre-crisis and crisis periods. This paper tests the impact of individual investor sentiment on risk-neutral skewness and confidence interval in two sub-periods. Findings – The authors find that errors in individual investors’ expectations significantly influence the Taiwan stock index option prices. Research limitations/implications – The data concerning the sentiment of speculative institutional investors are incomplete for the Taiwan option market. Therefore, this paper focusses on the analysis of individual investor sentiment. Further research can study the impact of institutional investor sentiment in emerging markets. Social implications – The previous literature has suggested that option prices reflect information before the information is revealed in stock prices. Therefore, an important implication is to analyze the information quality revealed in option prices by studying whether the changes in option prices are due to investor sentiment or non-sentiment-related components. Originality/value – Most of the studies in the literature have focussed on the US option market, and their applicability may vary across different microstructures. This paper shows that the influence of individual investor sentiment in an emerging market is different from that in the US market.

Rationality of Option Prices Relative to Underlying Asset Prices in Taiwan

This study explores whether Taiwan stock index option (TXO) prices vary rationally with underlying asset prices, using an intraday sample covering January 1, 2009 through June 30, 2009. This study focuses on two violations: the call (put) price and its underlying asset moving in the opposite (same) direction, and the absolute change in the option price exceeding that in the underlying asset. Overall, these violations in this study are extremely significant relative to the findings of Bakshi et al. [5]. This inconsistent result probably results from the market mechanism, namely that the TXO market is emerging and order-driven rather than developed and quote-driven. Despite this, both the impractical assumption of one-dimensional diffusion model and the market microstructure can significantly affect the rational movement of TXO options with their underlying assets.

Two-State Volatility Transition Pricing and Hedging of TXO Options

The spot markets often exhibit high and low volatilities that persist for a while. We classify the spot market volatility into two states: high and low and use the Markov chain theory to construct a Two-State volatility model for pricing and hedging Taiwan stock index options (TXO). Compared to binomial option pricing model, the Two-State model is more stable in convergence and faster in early periods of convergence but is much more time-consuming as the number of periods and computations extensively increase. The growth order of total node number for quadrinomial lattice is O(n 4) while it is O(n 2) for binomial lattice. Empirically, the Taiwan stock index has high-volatility = 42.85%, low-volatility = 17.39%, and probability of being in high-volatility state = 0.3487 over the in-sample period from 1/6/1990 to 04/30/2008 according to Markov chain. Using as large as 87,160 datasets of TXO covering out-of-sample period from 05/02/2008 to 03/17/2010 and strike prices from 3600 to 8700, we demonstrated that the Two-State volatility model has the most outstanding performance in high-volatility period as applying put options for pricing and hedging. However, to avoid the cost of taxes resulting from position changes, a longer-term (e.g. 10 day) hedge is more properly than a short-term (e.g. 5 day) hedge.

Empirical of the Taiwan stock index option price forecasting model – applied artificial neural network

This work presents a novel neural network model for forecasting option prices using past volatilities and other options market factors. Out of different approaches to estimating volatility in the option pricing model, this study uses backpropagation neural network to forecast prices for Taiwanese stock index options. The ability to develop accurate forecasts of grey prediction volatility enables practitioners to establish an appropriate hedging strategy at in-the-money option.

Stock index volatility: evidence for stock index options in Taiwan

This study applies artificial neural network to forecast Taiwan stock index option prices from 2005 to 2006. This study compares the option pricing model using three approaches to volatility: GARCH, EGARCH and GJR-GARCH, and compares estimators with three sets of criteria: RMSE, MAE and MAPE. The analytic results indicate that the asymmetric GARCH model is useful in predicting neural networks for stock index option prices.

Informed trading under different market conditions and moneyness: Evidence from TXO options

We examine the informational role of options across exercise prices under different market conditions. We analyze the influence of options' leverage effect, and market cycles on the cause–effect relation between stock and options markets based on an emerging options market—the Taiwan stock index options market. When aggregating market data irrespective of market cycles and options moneyness, we find that the equity market leads the options market. However, as we control options' moneyness and market cycles, we find that out-of-the-money options lead the stock market by up to 90 min with more pronounced results in downtrends and periods of political tension. Our findings suggest that the informational role of options is interacted with leverage effect and market conditions.

Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices

This investigation integrates a novel hybrid asymmetric volatility approach into an Artificial Neural Networks option-pricing model to upgrade the forecasting ability of the price of derivative securities. The use of the new hybrid asymmetric volatility method can simultaneously decrease the stochastic and nonlinearity of the error term sequence, and capture the asymmetric volatility. Therefore, analytical results of the ANNS option-pricing model reveal that Grey-EGARCH volatility provides greater predictability than other volatility approaches.

The valuation of Taiwan stock index option price - comparison of performances between Black-Scholes and neural network model

This study forecasts the option prices of Taiwan stock index options using back-propagation neural networks and Black-Scholes pricing model. The research data comprises the daily prices of sample options for the period from 2 January 2002 to 31 December 2003. The empirical evidence reveals that in a volatile market a neural network option pricing model outperforms the traditional Black-Scholes model.