An automated financial indices-processing scheme for classifying market liquidity regimes

Gu, Xing Sam; Mamon, Rogemar; Davison, Matt; Hao, Yu

doi:10.1080/00207179.2019.1616225

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…The apparent limitation in this study is that the parameter estimations have to be re-run every time a new batch of real or simulated data becomes available. Thus, a particular research's natural direction is to extend the MSM approach to a hidden Markov model (HMM) approach with an online parameter estimation scheme (Tenyakov et al, 2016a(Tenyakov et al, , 2016bGu et al, 2020;Xiong and Mamon, 2018). Then, a further extension with the embedding of a higher-order HMM could be formulated; see, for example, Xi and Mamon (2011) or Xiong and Mamon (2019).…”

Section: Discussionmentioning

confidence: 99%

Modelling exchange-driven fish price dynamics

Xiang

Jones

Mamon

et al. 2021

JM2

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Purpose This paper aims to put forward and compare two accessible approaches to model and forecast spot prices in the fishing industry. The first modelling approach is a Markov-switching model (MSM) in which a Markov chain captures different economic regimes and a stochastic convenience yield is embedded in the spot price. The second approach is based on a multi-factor model (MFM) featuring three correlated stochastic factors. Design/methodology/approach The two proposed approaches are analysed in terms of parameter-estimation accuracy, information criteria and prediction performance. For MSM’s calibration, the quasi-log-likelihood method was applied directly while for the MFM’s parameter estimation, this paper designs an enhanced multi-variate maximum likelihood method with the aid of moments matching. The numerical experiments make use of both simulated and actual data compiled by the Fish Pool ASA. Data on both the Fish Pool’s forwards and Norwegian T-bill yields were additionally used in the MFM’s implementation. Findings Using simulated data sets, the MSM estimation gives more accurate results than the MFM estimation in terms of the norm in $l^2$ between the “true” and “computed” parameter estimates and significantly lower standard errors. With actual data sets used to evaluate the forecast values, both approaches have similar performances based on the error analysis. Under some metrics balancing goodness of fit and model complexity, the MFM outperforms the MSM. Originality/value With the aid of simulated and observed data sets examined in this paper, insights are gained concerning the appropriateness, as well as the benefits and weaknesses of the two proposed approaches. The modelling and estimation methodologies serve as prelude to reliable frameworks that will support the pricing and risk management of derivative contracts on fish price evolution, which creates price risk transfer mechanisms from the fisheries/aquaculture sector to the financial industry.

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Section: Discussionmentioning

confidence: 99%

Modelling exchange-driven fish price dynamics

Xiang

Jones

Mamon

et al. 2021

JM2

Self Cite

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“…The literature [14] examines asymmetric risk spillovers between the global market for crude oil and other markets, including the commodities and financial markets, and concludes that these risk spillovers greatly improve the reaction to rising returns. Additionally, literature [15] discusses a multivariate Hidden Markov model that can simultaneously analyze the dynamics of four financial market indicators, which analyzes the liquidity of financial markets through the fluctuations of financial market indicators and also provides an effective forecast for liquidity risk avoidance. According to the literature [16], the R-vine linkage and spillover index models analyze the reliance pattern and information spillover effects between the RMB exchange rate and the Chinese stock market.…”

Section: Introductionmentioning

confidence: 99%

“…Rui Mao, Fuxiang Liang and Jingjing Wang. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] …”

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A multi-objective optimization algorithm based on the identification of risk spillover intensity measurement in Chinese financial markets

Mao,

Liang,

Wang

2023

Applied Mathematics and Nonlinear Sciences

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Identifying the intensity of risk spillover in China’s financial market can provide an important empirical basis and information reference for monitoring and preventing financial risks. This paper uses a multi-objective application of particle swarm optimization algorithm under a multi-objective optimization algorithm to induce the MOPSO algorithm. The algorithm identifies and measures the intensity of risk spillover in China’s financial market in two aspects, including the risk spillover from policy instability to the stock market and the two-way risk spillover between the financial industry and the real estate industry. Regarding policy instability, the risk spillover intensities of downside fiscal policy, monetary policy, trade policy, and foreign exchange policy are 14.83%, 53.88%, 7.54%, and 31.06%, respectively. Regarding the two-way risk spillover intensity, the average risk spillover intensity of real estate to finance is 70.28%, which is 5.57 percentage points higher than that of finance. This indicates that the multi-objective optimization algorithm can identify and measure the risk spillover intensity of China’s financial market, providing data support and information reference for preventing financial risks.

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