Hang Seng Index Research Articles

Option hedging using LSTM-RNN: an empirical analysis

This paper proposes an optimal hedging strategy in the presence of market frictions using the Long Short Term Memory Recurrent Neural Network (LSTM-RNN) method, which is a modification of the method proposed in Buehler et al. (Deep hedging. Quant. Finance, 2019, 19(8), 1271–1291). The market frictions are transaction costs, liquidity constraints, trading limits and cost of funds. The loss function is a spectral risk measure. We first make an empirical analysis of the LSTM-RNN model of real option markets, which are the Asian market (domestic market 50 ETF option, Hong Kong Hang Seng Index Option, Nikkei Index Option), the North American market (S&P 500 Index Option) and the European market (FTSE 100 Index Option). The benchmark models are from Leland (Option pricing and replication with transaction costs. J. Finance., 1985, 40(5), 1283–1301), Boyle and Vorst (Option replication in discrete time with transaction costs. J. Finance, 1992, 47(1), 271–293) and Whalley and Wilmott (A hedging strategy and option valuation model with transaction costs. OCIAM Working Paper, Mathematical Institute, Oxford, 1993). Finally, we compare the results from the LSTM-RNN model with benchmark models involving transaction costs for both simulated market data generated by Geometric Brownian Motion (GBM) and the Heston model and real market data. The results show that the LSTM-RNN model outperforms benchmark models for low or medium volatility (<0.8), OTM moneyness and under a certain risk level (<80%) in the GBM market setting. For the 50ETF option market, the LSTM-RNN model outperforms benchmark models for ATM and under a certain risk level (<15%). For the HSI option market, the LSTM-RNN model outperforms benchmark models when transaction costs are smaller than 1.5%. For the Nikkei and S&P 500 option markets, the LSTM-RNN model always outperforms benchmark models. For the FTSE option market, the LSTM-RNN model outperforms benchmark models when moneyness is not too deeply ITM.

Is gold favourable than bitcoin during the COVID-19 outbreak? Comparative analysis through wavelet approach

The novel coronavirus (COVID-19) has tremendously oscillated the global financial markets. Consequently, investors feel pressured to find safe-haven investments during the pandemic crisis. Numerous studies have evaluated bitcoin's safe-haven properties during the COVID-19; however, the present study considered gold as a potential safe-haven for investors of renowned stock markets of Asia, Europe, and the US. The present investigation computed the ratio of gold to bitcoin (Gold_Bitcoin) and compared the safe-haven properties of gold in contrast to bitcoin. The present study analysed the Morlet Wavelet approach and found that most of the time during the COVID-19, gold investments proved to be more beneficial than bitcoin. Remarkably, the findings highlighted that the Gold_Bitcoin ratio increased in higher and lower frequencies combined with CAC40. In the long run, the return on investments in gold increased in contrast to bitcoin returns pooled with DAX30. Also, the Gold_Bitcoin ratio of the US stock market increased during the one-week and one-month cycles of January and August. Likewise, the Hang Seng Index caused the Gold_Bitcoin ratio to rise at a much higher frequency (i.e., the second half of January, the first half of February and April, and the first half of June and August), whereas IBEX35 surged Gold_Bitcoin at a lower frequency (i.e., during January, February, and August). In higher frequency bands, LSE increased the Gold_Bitcoin ratio (i.e., in February and March); nevertheless, Gold_Bitcoin showed a positive connection with FTSEMIB in the one-to-two month's frequency band (i.e., throughout January, February, and August). Interestingly, the returns on the Gold_Bitcoin ratio increased in the SSEC stock market in the high-frequency band (i.e., during March, May, and July 2020).

Neural network with fixed noise for index-tracking portfolio optimization

Index tracking portfolio optimization is popular form of passive investment strategy, with a steady and profitable performance compared to an active investment strategy. Due to the revival of deep learning in recent years, several studies have been conducted to apply deep learning in the field of finance. However, most studies use deep learning exclusively to predict stock price movement, not to optimize the portfolio directly. We propose a deep learning framework to optimize the index-tracking portfolio and overcome this limitation. We use the output distribution of the softmax layer from the fixed noise as the portfolio weights and verify the tracking performance of the proposed method on the S&P 500 index. Furthermore, by performing the ablation studies on the training-validation dataset split ratio and data normalization, we demonstrate that these are critical parameters for applying deep learning to the portfolio optimization problem. We also verify the generalization performance of the proposed method through additional experiments with another index of a major stock market, the Hang Seng Index (HSI).

To assess the multiperiod market risk with deep learning method taking the boosting additive quantile regression as an example

AbstractCompared with current risks, future risks are more important for investment decisions and risk management. This paper modifies the Square Root of Time Rule with the boosting additive quantile regression model to forecast multi‐period horizon market risk. In J. P. Morgan's RiskMetrics Model, the k‐period horizon value at risk (VaR) equals to . Since its assumptions are too strict, expected capacity of Risk Metrics is not so well. Taylor relaxed assumptions of this model, used the GARCH model to replace the IGARCH model, and obtained multi‐period horizon VaR, which is a nonlinear function of the one‐step‐ahead volatility forecast .The conditional mean μt is zero in Taylor's model, but Tsay pointed out that this assumption (μt = 0) does not always hold. Therefore, we relax this assumption about the conditional mean, and obtain the VaR which is mixed function consisting of two parts, one is a linear function of conditional mean, and the other is a nonlinear function of , given the holding horizon k. For our mixed VaR function, we chose a more appropriate method, the boosting additive quantile regression model, to forecast multi‐period horizon VaR. Taking log‐returns of the Hang Seng Index from January 1, 2007 to November 1, 2016 as the sample, weforecast the 5‐, 10‐, 15‐, and 20‐day horizon VaRs, and compare the prediction accuracy of Morgan's model withour quantile regression model through likelihood ratio tests. Results show that VaR based on the quantile regression model is not only more accurate, but also sensitive to volatility, and is conducive to maintaining a reasonable risk reserve level for financial institutions, enabling them to pay less for regulation andachieve incentive compatibility.

Analisis pengaruh indeks saham asing terhadap indeks harga saham gabungan dengan pendekatan Error Correction Model

The Composite Stock Price Index (IHSG) is a composite index of many shares listed on the stock exchange and their movements show conditions that occur in the capital market. JCI is confident of macroeconomic factors and foreign exchange indexes. The purpose of this study was to analyze the effect of the Dow Jones Index, the Straits Time Index, the Hang Seng Index, the Nikkei 225 Index, and the FTSE 100 Index on the composite price index. The research method used is the Error Correction Model (ECM). In the short term, the DJIA and FTSE 100 variables have a positive effect on the JCI, the STI and Hang Seng variables have no significant on the JCI, while the Nikkei 225 has a negative effect on the JCI. In the long term, the DJIA and STI variables have a positive effect on the IHSG, the JSI and FTSE 100 variables have no effect on the IHSG, while the Nikkei 225 variable has a negative effect on the JCI.

Pengaruh Harga Minyak Dunia, Indeks Dow Jones dan Indeks Hang Seng terhadap Indeks Harga Saham Gabungan Periode 2016-2020

&lt;strong&gt;&lt;em&gt;&lt;span&gt;Purpose&lt;/span&gt;&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;&lt;span&gt; This study aims to analyze the effect of world oil prices, Dow Jones index, and Hang Seng index on the Composite Stock Price Index (JCI). &lt;strong&gt;Methodology&lt;/strong&gt; The study was conducted using secondary data per month in the 2016-2020 period and analyzed using multiple linear regression methods. &lt;strong&gt;Findings&lt;/strong&gt; The results showed that world oil price and Dow Jones index partially had a positive and significant effect on the JCI, while Hang Seng Index had no significant effect on the JCI.&lt;strong&gt;Suggestion &lt;/strong&gt;Investors who want to invest should always pay attention to the movements in world oil prices and the Dow Jones index.&lt;/span&gt;&lt;/em&gt;

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model.

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.

Quasi‐maximum exponential likelihood estimation for double‐threshold GARCH models

AbstractWe consider the nonparametric inference for the double‐threshold generalized autoregressive conditional heteroscedastic models. The quasi‐maximum exponential likelihood estimators (QMELEs) of the model parameters are obtained, and their asymptotic properties are established. Simulation studies imply that the estimators are asymptotically normally distributed. An empirical investigation of stock returns illustrates our findings. Both the simulations and the example indicate that the QMELE is feasible, reliable and appropriate to fit the financial time series data of the Hang Seng Index.

Do economic statistics contain information to predict stock indexes futures prices and returns? Evidence from Asian equity futures markets

This research examines the impact of local and international market factors on the pricing of stock indexes futures in East Asian countries. The purpose of this paper is to present a study of the significant factors that determine the major stock indexes futures’ prices of Hong Kong, Malaysia, Singapore, South Korea and Taiwan. This study first investigates the relationships between Hang Seng Index Futures, KLCI Futures, SiMSCI Futures, KOSPI Futures, Taiwan Exchange Index Futures and local interest rates, dividend yields, local exchange rates, overnight SP 10 years in-sample data are used for testing and developing the pricing models, and 1 year out-of-sample data is used for the purpose of verifying the predicted values of the stock indexes futures. Using simple linear regressions, local interest rates, dividend yields, exchange rates, overnight S&P500 and ATMI are found to have significant impact on these futures contracts. In this research, the next period close is predicted using simple linear regression and non-linear artificial neural network (ANN). An examination of the prediction results using nonlinear autoregressive ANN with exogenous inputs (NARX) shows significant abnormal returns above the passive threshold buy and hold market returns and also above the profits of simple linear regression (SLR). The empirical evidence of this research suggests that economic statistics contain information which can be extracted using a hybrid SLR and NARX trading model to predict futures prices with some degree of confidence for a year forward. This justifies further research and development of pricing models using fundamentally significant economic determinants to predict futures prices.

The Performance of Hybrid Artificial Neural Network Models for Option Pricing during Financial Crises

this paper provides a novel research on the pricing ability of the hybrid ANNs based upon the Hang Seng Index Options spanning a period of from Nov, 2005 to Oct, 2011, during which time the 2007-20008 financial crisis had developed. We study the performances of two hybrid networks integrated with Black-Scholes model and Corrado and Su model respectively. We find that hybrid neural networks trained by using the financial data retained from a booming period of a market cannot have good predicting performance for options for the period that undergoes a financial crisis (tumbling period in the market), therefore, it should be cautious for researchers/practitioners when carry out the predictions of option prices by using hybrid ANNs. Our findings have likely answered the recent puzzles about NN models regarding to their counterintuitive performance for option pricing during financial crises, and suggest that the incompetence of NN models for option pricing is likely due to the fact NN models may have been trained by using data from improper periods of market cycles (regimes), and is not necessarily due to the learning ability and the flexibility of NN models.

A Markov regime switching model for asset pricing and ambiguity measurement of stock market

Based on the theoretical framework of expected utility with uncertain probabilities, this paper uses actual prices of CSI300 and Hang Seng index to empirically measure ambiguity degrees in the Chinese mainland and Hong Kong stock markets. A Markov regime-switching model is proposed to divide the stock market into bear and bull states, and then test whether there exist significant differences in the ambiguity degrees under different states. An ambiguity factor and a risk factor are then proposed to analyze the time-varying relationship among risk, ambiguity, and return under different states. In addition to the mean and variance, the high-order moments, including skewness and kurtosis, are used to test whether they affect the relationship among them. The results show that the ambiguity degrees in the Chinese mainland stock market are significantly higher than those in the Hong Kong stock market, and there are significant differences between bear and bull states for the two markets. Moreover, the regression results among risk, ambiguity, and return indicate that with ambiguity, the effects of risk factors on excess returns is significantly negative under bear state, and significantly positive under bull state, while it is significantly negative under the two states without ambiguity.

A Two-Factor Fuzzy-Fluctuation Time Series Forecasting Model for Stock Markets Based on a Probabilistic Linguistic Preference Relationship and Similarity Measure

An increasing number of scholars have tried to incorporate external factors affecting the disturbance of a time series into their forecasting models. However, these studies only verify the linkage relationship of two or more time series by empirical tests without providing any theoretical explanation. This makes it difficult to choose a linkage time series without using many tests. In this paper, a novel two-factor fuzzy-fluctuation time series (FFTS) forecasting model is proposed based on the probabilistic linguistic preference relationship (PLPR) and similarity measure. It not only proposes the idea of combining external factors with internal potential trends but also explains the linkage mechanism of time series fluctuations from the perspective of behavioral preference. Specifically, the probabilistic linguistic preference logical relationship (PLPLR) is employed to express the fluctuation behavior rule and preference attribute from the history testing dataset. The Euclidean distance or Hamming distance between the “current state” and the left side of training PLPLRs is introduced as a similarity comparison method for the identification of appropriate rules. The proposed model is tested using a traditional time series (e.g., the enrollment of the University of Alabama) to compare its performance with existing models. The model is also employed to forecast realistic stock markets, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Index (HSI). The performance comparison illustrates the effectiveness and universality of the model.

Financial centers of new industrial countries in the world financial architecture

The views of scholars concerning the definition of «international financial center», which makes it possible to formulate the definition of international financial center essence are summarized in this paper. The factors influencing the formation of international financial center incluing: stable financial system, stable currency, exchange rate stability, political and social stability in the country, favorable geographical location, developed financial infrastructure, open economy for free movement of capital, etc. are identified. The role of international financial centers in increasing their global competitiveness, achieving higher levels of economic growth, prosperity and social progress is substantiated. The classification of international financial centers is investigated. The place of the Hong Kong Financial Center in the financial architecture of the world is determined. The main parameters of the current state of the Hong Kong Financial Center: the USD / HKD exchange rate, the effective exchange rate index weighted by trade, the Hang Seng index, market capitalization, etc. are diagnosed. SWOT-analysis of the Hong Kong Financia Cente is carried out in order to identify its strengths, weaknesses, opportunities and threats. Based on the results of the problem of its functioning, a set of measures to improve competitiveness is also proposed. Hong Kong has favorable macroeconomic and institutional environment, qualified personnel, and is the fifth most competitive international financial center in the world. However, there is a low degree of international element in the domestic stock market, there is also risk of losing the status of «international financial center», other regional economies that have greater access to international investment opportunities in the stock market, such as Singapore, may be a threat. The financial center should focus on overcoming the effects of the coronavirus, promote the share of foreign investment in the economy, take a set of measures to overcome the country's recession, review legal, regulatory and tax requirements to promote development, improve quality of life and attractiveness, and become Asia's leading currency hub.

Experimental Evidence of Source Preference: Familiarity and Home Bias

This paper experimentally tests the Fox-Tversky (1995) source preference hypothesis as axiomatized in Chew and Sagi (2008) where people may have preference between equally distributed risks depending on the underlying sources of uncertainty. We study two forms of source preference. One is based on familiarity of risks arising from the trailing digit of stock prices in the home city exchange. Another is based on the trailing digit of the market index of the home city versus that of a foreign city. We find a familiarity-based source preference in portfolios comprising strictly dominated bets associated with more familiar stocks and in valuation of bets elicited using an ascending-price auction. Complementarily, we find a home bias in terms of Shanghai (Hong Kong) subjects preferring to bet on the trailing digit of the Shanghai Stock Exchange Index (Hang Seng Index) even though the same bets based on Dow Jones Industrial Average would pay more. Taken together, our study suggests that home bias is driven mainly by source preference rather than ambiguity aversion.

Bayesian estimation of the stochastic volatility model with double exponential jumps

This paper generalizes the stochastic volatility model to allow for the double exponential jumps. To derive the jumps and time-varying volatility in returns, we implement an efficient Markov chain Monte Carlo approach based on the band and sparse matrix algorithms used in Chan and Hsiao (SSRN Electron J., 2013, https://doi.org/10.2139/ssrn.2359838 ) to estimate this model. We illustrate the the methodology using the daily data for the Shanghai Composite Index, Hangseng Index, Nikkei 225 Index and Kospi Index. We find that the stochastic volatility model with double exponential jumps provide better fitness in sample period.

DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods

The remarkable performance of deep learning is based on its ability to learn high-level features by processing large amounts of data. This exceptionally superior performance has attracted the attention of researchers studying option pricing. However, option data are more expensive and less accessible than other types of data and are imbalanced because of the liquidity of options. This motivated us to propose a new option pricing and delta-hedging framework called DeepOption. This framework, which is based on deep learning, can improve the performance even when applying imbalanced real option data. In particular, the framework fuses simulated big data, known as distilled data, obtained using various traditional parametric methods. The proposed model employs the following three-stage training approach: Our model is pre-trained using big distilled data after it is fine-tuned using real option data through transfer learning. Finally, a delta branch is added to the model and trained. We experimentally evaluated the proposed method using three sets of real option data, namely S&P 500 European call options, EuroStoxx50 call options, and Hang Seng Index put options. Our experimental results on option pricing demonstrate that our proposed model outperforms parametric methods and other machine learning methods. Specifically, our model, which uses pre-training with distilled data, reduces the overall mean absolute percentage error (MAPE) by more than 50%, compared with that of a deep learning model using only real option data without pre-training.

Forecasting of stock price index using support vector regression with multivariate empirical mode decomposition

PurposeDespite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.Design/methodology/approachQuantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.FindingsThe results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.Originality/valueThis research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

Test for parameter change in the presence of outliers: the density power divergence-based approach

ABSTRACT This study considers the problem of testing for parameter change, particularly in the presence of outliers. To lessen the impact of outliers, we propose a robust test based on the density power divergence introduced by Basu et al. (Biometrika, 1998), and then derive its limiting null distribution. Our test procedure can be naturally extended to any parametric model to which MDPDE can be applied. To illustrate this, we apply our test procedure to GARCH models. We demonstrate the validity and robustness of the proposed test through a simulation study. In a real data application to the Hang Seng index, our test locates some change-points that are not detected by the existing tests such as the score test and the residual-based CUSUM test.

A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation

Forecasting time series is an emerging topic in operational research. Existing time-series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, in this article we propose a fuzzy interval time-series forecasting model on the basis of network-based multiple time-frequency spaces and the induced-ordered weighted averaging aggregation (IOWA) operation. Specifically, a time-series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two well-known sets of financial time-series data, Taiwan Stock Exchange Capitalization Weighted Stock Index and Hang Seng Index, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.

Composite Multiscale Partial Cross-Sample Entropy Analysis for Quantifying Intrinsic Similarity of Two Time Series Affected by Common External Factors.

In this paper, we propose a new cross-sample entropy, namely the composite multiscale partial cross-sample entropy (CMPCSE), for quantifying the intrinsic similarity of two time series affected by common external factors. First, in order to test the validity of CMPCSE, we apply it to three sets of artificial data. Experimental results show that CMPCSE can accurately measure the intrinsic cross-sample entropy of two simultaneously recorded time series by removing the effects from the third time series. Then CMPCSE is employed to investigate the partial cross-sample entropy of Shanghai securities composite index (SSEC) and Shenzhen Stock Exchange Component Index (SZSE) by eliminating the effect of Hang Seng Index (HSI). Compared with the composite multiscale cross-sample entropy, the results obtained by CMPCSE show that SSEC and SZSE have stronger similarity. We believe that CMPCSE is an effective tool to study intrinsic similarity of two time series.