RESEARCH INTERESTS

Applied Statistics and Financial Time Series

EDUCATIONAL BACKGROUND

• Bachelor Degree (Physics), University of Malaya, Malaysia (1998)

• Master Degree (Applied Statistics), Universiti Putra Malaysia, Malaysia (2003)

• Ph. D Degree (Statistics), National University Malaysia, Malaysia (2011)

WORKING EXPERIENCE

• Xiamen University Malaysia Campus, Assoc. Prof. (2017-present).

• Multimedia University, Malaysia, Lecturer (2003-2017).

RESEARCH EXPERIENCE / GRANTS

• FRGS (2016-2018) - Modelling and Forecasting Global Shari'ah Stock Market Realized Volatility using High-frequency Data

• XMUMRF Cycle 3 (2019-2021), Various Heterogeneous Autoregressive Volatility Modeling and Forecast Evaluations for Global Financial Stock Markets.

REPRESENTATIVE PUBLICATIONS

• Cheong, C.W., Abu Hassan S.M.N., Zaidai Isa. (2007) Asymmetry and long memory volatility: some empirical evidence using Garch. Physica A 373, 651-664.

• Cheong, C.W. (2008) Time-varying volatility in Malaysian stock exchange: an empirical study using multiple-volatility-shifts fractionally integrated model. Physica A 378 (4), 889-898

• Cheong, C.W. (2008) Heavy-tailed value-at-risk analysis for Malaysian stock exchange. Physica A 378 (16), 4285 - 4298.

• Cheong, C.W. (2009) Modeling and Forecasting Crude oil Markets using ARCH-models. Energy Policy 37, 2346 - 2355.

• Chenog, C.W. (2010) Optimal choice of sample fraction in univariate financial tail index estimation. Journal of Applied Statistics 37 (12), 2043 - 2056.

• Cheong, C.W. (2010) Self-similarity in financial markets: A fractionally integrated approach. Mathematical and Computer Modeling 52, 471 - 495.

• Cheong, C.W. (2010). Estimating the Hurst Parameter in financial time series via heuristic approaches. Journal of Applied Statistics 37 (2), 201 - 214.

• Cheong, C.W. (2011). Parametric and non-parametric approaches in evaluating martingale hypothesis of energy spot markets. Mathematical and Computer Modeling 54 (5), 1499 - 1509.

• Cheong, C.W & Cherng, L.M. (2017) High-frequency volatility combine forecast evaluations: An empirical study for DAX. The Journal of Finance and Data Science 3 (1-4), 1-12.

• Cheong, C.W. , Cherng, L.M., Pei, T.P. (2017). Heterogenous market hypothesis evaluation using multipower variation volatility. Communications in Statistics-Simulation and Computation 46 (8),6574-6587.

• Ng SewLai, Cheong, C.W, Chong LeeLee. (2017). Multivariate market risk evaluation between Malaysian Islamic stock index and sectoral indices.  Borsa Istanbul Review. Volume 17(1), 49-61.

• Cheong, C.W. & Cherng, L.M. (2018). S&P500 volatility analysis using high-frequency multipower variation volatility proxies. Empirical Economics 54 (3), 1297-1318.

• Cheong, C.W., Liu ChengZhi, J ShengZhe, Ye ZhiQing. (2019). Dynamic Average-Forecast value-at-Risk by Using High Frequency IPC Mexican Index. International Journal of Economics &Management 13 (1), 153-164.

• Lai, N.S, Cheong, C.W. & Lee, C.L. (2019). Modelling Volatility in the Presence of Abrupt Jumps: Empirical Evidence from Islamic Stock Markets. International Journal of Economics & Management 13(1), 93-109.

• Cheong, C.W. & Cherng, L.M. (2019). Nonlinear high-frequency stock market time series: Modeling and combine forecast evaluations, Communication in Statistics- Simulation and computation. Volume 50(7), 2126-2144.

• Ng SewLai, Cheong, C.W, Chong LeeLee. (2021). Realized volatility transmission within Islamic stock markets: A multivariate HAR-GARCH-type with nearest neighbour truncation estimator.  Borsa Istanbul Review. Volume 20(1), S26-S39.

• Cheong, C.W, Lee Min Cherng, Liu ChengZhi, Zhang YiHuai. (2021). Do general elections affect fractal structure of stock market? Journal of Statistics and Management Systems.  Volume 24(5), 951-964.

• Hoy ZX, Woon KS, Cheong CW, Hashim H, Fan YV. (2022). Forecasting heterogeneous municipal solid waste generation via Bayesian-optimised neural network with ensemble learning for improved generalisation. Computers & Chemical Engineering, 166, 107946.