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Financial Econometrics and Models
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Financial Econometrics and Models

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 15026

Special Issue Editor

Dr. Baiding Hu

E-Mail Website
Guest Editor
Department of Global Value Chains and Trade, Lincoln University, Christchurch 7647, New Zealand
Interests: stock market volatility; industrial energy demand and supply; oil price; market spillover; technological progress
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the Asian Financial Crisis in 1997, global financial markets have become increasingly interdependent both in terms of asset returns and return volatilities. This interdependence has further manifested by the 2008 Global Financial Crisis when a globally coordinated expansionary policy response was deemed necessary to bail out financial institutions to prevent a collapse of the global financial system. This Special Issue focuses on the use of “Financial Econometrics and Models” to investigate issues related to the interdependence of financial markets. To this end, we seek research articles that address modelling of dynamic relationships between asset returns and risks in various financial markets. In particular, we welcome articles on modelling the stochastic volatility of asset returns, return and volatility spillover between financial markets, for example, return and volatility spillover between stock markets across countries, the asymmetries of the spillover and or performance linkages between an industry (such as the hedge funds industry) and the stock market. In addition, articles on the effects of monetary policies (such as the QE) on financial markets, dynamic models for high frequency data, market indexes for better approximations of the market portfolio, characterisation of distributions of asset returns with heavy tails, and managerial behaviour and credit risk management in the financial sector are also encouraged.

Dr. Baiding Hu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • stochastic volatility
  • GARCH
  • VAR
  • spillover
  • interdependence

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Published Papers (6 papers)

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Research

16 pages, 382 KiB  
Article
Particle MCMC in Forecasting Frailty-Correlated Default Models with Expert Opinion
by Ha Nguyen
J. Risk Financial Manag. 2023, 16(7), 334; https://doi.org/10.3390/jrfm16070334 - 14 Jul 2023
Viewed by 1145
Abstract
Predicting corporate default risk has long been a crucial topic in the finance field, as bankruptcies impose enormous costs on market participants as well as the economy as a whole. This paper aims to forecast frailty-correlated default models with subjective judgements on a [...] Read more.
Predicting corporate default risk has long been a crucial topic in the finance field, as bankruptcies impose enormous costs on market participants as well as the economy as a whole. This paper aims to forecast frailty-correlated default models with subjective judgements on a sample of U.S. public non-financial firms spanning January 1980–June 2019. We consider a reduced-form model and adopt a Bayesian approach coupled with the Particle Markov Chain Monte Carlo (Particle MCMC) algorithm to scrutinize this problem. The findings show that the 1-year prediction for frailty-correlated default models with different prior distributions is relatively good, whereas the prediction accuracy ratios for frailty-correlated default models with non-informative and subjective prior distributions over various prediction horizons are not significantly different. Full article
(This article belongs to the Special Issue Financial Econometrics and Models)
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16 pages, 1970 KiB  
Article
The Generalised Extreme Value Distribution Approach to Comparing the Riskiness of BitCoin/US Dollar and South African Rand/US Dollar Returns
by Delson Chikobvu and Thabani Ndlovu
J. Risk Financial Manag. 2023, 16(4), 253; https://doi.org/10.3390/jrfm16040253 - 21 Apr 2023
Cited by 3 | Viewed by 2501
Abstract
In this paper, the generalised extreme value distribution (GEVD) model is employed to estimate financial risk in the form of return levels and the value at risk (VaR) for the two exchange rates, BitCoin/US dollar (BTC/USD) and the South African rand/US dollar (ZAR/USD). [...] Read more.
In this paper, the generalised extreme value distribution (GEVD) model is employed to estimate financial risk in the form of return levels and the value at risk (VaR) for the two exchange rates, BitCoin/US dollar (BTC/USD) and the South African rand/US dollar (ZAR/USD). The Basel Committee on Banking Supervision (BCBS) responsible for developing supervisory guidelines for banks and financial trading desks recommended that VaR be computed and reported. The maximum likelihood estimation (MLE) method is used to estimate the parameters of the GEVD. The estimated risk values are used to compare the riskiness of the two exchange rates and help both traders and investors to define their position in forex trading. This is to helping understanding the risk they are taking when they convert their savings/investments to BitCoin instead of the South African currency, the rand. The high extreme value index associated with the BTC/USD compared to the ZAR/USD implies that BitCoin is riskier than the rand. The BTC/USD has higher values of expected extreme/tail losses of 13.44%, 18.02%, and 23.41% at short (6 months), medium (12 months), and long (24 months) terms, compared to the ZAR/USD expected extreme/tail losses of 2.40%, 2.84%, and 3.28%, respectively. The computed VaR estimates for losses of USD 0.17, USD 0.22, and USD 0.38 per dollar invested in BTC/USD at 90%, 95%, and 99%, compared to ZAR/USD’s USD 0.03, USD 0.03, and USD 0.04 at the respective confidence levels, confirm the high risk associated with BitCoin. The conclusion drawn from this study is that BTC/USD is riskier than ZAR/USD, despite the rand being a developing country’s currency, hence perceived as being risky. The perception is that the rand is riskier than BitCoin and perceptions do influence exchange rates. Kupiec’s backtest results confirmed the model’s adequacy. These findings are helpful to investors, traders, and risk managers when deciding on trading positions for the two currencies. Full article
(This article belongs to the Special Issue Financial Econometrics and Models)
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31 pages, 1966 KiB  
Article
Spot–Futures Price Adjustments in the Nikkei 225: Linear or Smooth Transition? Financial Centre Leadership or Home Bias?
by Jieye Qin, Christopher J. Green and Kavita Sirichand
J. Risk Financial Manag. 2023, 16(2), 117; https://doi.org/10.3390/jrfm16020117 - 12 Feb 2023
Cited by 3 | Viewed by 2296
Abstract
This paper studies price discovery in Nikkei 225 markets through the nonlinear smooth transition price adjustments between spot and future prices and across all three futures markets. We test for smooth transition nonlinearity and employ an exponential smooth transition error correction model (ESTECM) [...] Read more.
This paper studies price discovery in Nikkei 225 markets through the nonlinear smooth transition price adjustments between spot and future prices and across all three futures markets. We test for smooth transition nonlinearity and employ an exponential smooth transition error correction model (ESTECM) with exponential generalised autoregressive conditional heteroscedasticity (EGARCH), allowing for the effects of transaction costs, heterogeneity, and asymmetry in Nikkei price adjustments. We show that the ESTECM-EGARCH is the appropriate model as it offers new insights into Nikkei price dynamics and information transmission across international markets. For spot–futures price dynamics, we find that futures led spot prices before the crisis, but spot prices led afterwards. This can be explained by the lower level of heterogeneity in the underlying spot transaction costs after the crisis. For cross-border futures prices, the foreign exchanges (Chicago and Singapore) lead in price discovery, which can be attributed to their roles as global information centres and their flexible trading conditions, such as a more heterogeneous structure of transaction costs. The foreign leadership is robust to the use of linear or nonlinear models, the time differences between Chicago and the other markets, and the long-run liquidity conditions of the Nikkei futures markets, and strongly supports the international centre hypothesis. Full article
(This article belongs to the Special Issue Financial Econometrics and Models)
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15 pages, 338 KiB  
Article
Price Stability Properties and Volatility Analysis of Precious Metals: An ICSS Algorithm Approach
by Sameen Fatima, Christopher Gan and Baiding Hu
J. Risk Financial Manag. 2022, 15(10), 465; https://doi.org/10.3390/jrfm15100465 - 17 Oct 2022
Cited by 4 | Viewed by 2079
Abstract
This paper investigates the price stability properties of precious metals during the 1997 Asian Financial Crisis, 2007–2008 Global Financial Crisis, and 2010 Eurozone Crisis. To analyse the interaction between precious metal prices and the US stock market stock performances, we use the ICSS [...] Read more.
This paper investigates the price stability properties of precious metals during the 1997 Asian Financial Crisis, 2007–2008 Global Financial Crisis, and 2010 Eurozone Crisis. To analyse the interaction between precious metal prices and the US stock market stock performances, we use the ICSS algorithm along with the GARCH model to evaluate how the number of rapid changes in volatility of precious metals has been reduced. The results suggest gold is the most stable of the precious metals. However, silver, platinum, and palladium showed positive price correlation when the US Dow Jones market was unstable. These results imply that: (1) the correlation among stocks market returns has little to no significant impact on the price movement of precious metals, but the US Dow Jones has some influence on precious metal markets except gold, which means investors can reap this benefit from diversification; (2) investors can systematically increase their portfolio returns by going short with the gold investments with low price co-movement and long on silver, platinum, and palladium with high co-movement with stock prices. Full article
(This article belongs to the Special Issue Financial Econometrics and Models)
19 pages, 4299 KiB  
Article
The Effects of National Fundamental Factors on Regional House Prices: A Factor-Augmented VAR Analysis
by Xiang Gao, Wen Kong and Zhijun Hu
J. Risk Financial Manag. 2022, 15(7), 309; https://doi.org/10.3390/jrfm15070309 - 15 Jul 2022
Cited by 1 | Viewed by 1808
Abstract
Using panel data from 30 regions in China during the period 1999:01–2020:12, this paper evaluates the effects of national fundamentals affecting the movement of regional house prices by estimating a factor-augmented VAR model. We construct and examine a hypothesis that national fundamentals affecting [...] Read more.
Using panel data from 30 regions in China during the period 1999:01–2020:12, this paper evaluates the effects of national fundamentals affecting the movement of regional house prices by estimating a factor-augmented VAR model. We construct and examine a hypothesis that national fundamentals affecting regional house prices, such as monetary policy (short-term interest rate and M2), real output, and inflation rate, may affect regional house prices through their impacts on common factors. The empirical results show that monetary shocks (both interest rate and M2) can significantly affect regional house prices, but the effects are pretty different across regions. However, the effects of the real output and inflation rate are less important. Therefore, this study offers valuable information for regulators to improve the effectiveness of monetary policy to stabilize house markets from a regional perspective. Full article
(This article belongs to the Special Issue Financial Econometrics and Models)
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15 pages, 3779 KiB  
Article
Stock Market Synchronization: The Role of Geopolitical Risk
by Kazi Sohag, Rogneda Vasilyeva, Alina Urazbaeva and Valentin Voytenkov
J. Risk Financial Manag. 2022, 15(5), 204; https://doi.org/10.3390/jrfm15050204 - 28 Apr 2022
Cited by 7 | Viewed by 4239
Abstract
Given the importance of stock market synchronization for international portfolio diversification, we estimate the degrees of co-movements among US, Chinese and Russian markets. By applying the TVP-VAR approach, we measure total and bivariate synchronization indices utilizing daily data from 1998 to 2021. Our [...] Read more.
Given the importance of stock market synchronization for international portfolio diversification, we estimate the degrees of co-movements among US, Chinese and Russian markets. By applying the TVP-VAR approach, we measure total and bivariate synchronization indices utilizing daily data from 1998 to 2021. Our analysis demonstrates that the total connectedness index (TCI) is 26.15% among the three markets. We find that the US market is the highest volatility contributor, whereas the Russian market is the highest receiver. Since stock market synchronization is exposed to geopolitical risk, at the second stage, we apply the Quantile-on-Quantile framework to measure the response of total and bilateral connectedness indices to geopolitical risk (GPR). The findings affirm our proposition that GPR impedes TCI when it has a bullish state and a higher quantile of GPR. The response of bilateral connectedness is negative towards GPR concerning US–China and US–Russian pairs. However, the degree of connectedness between Russian and Chinese stock markets is less responsive to GPR. Full article
(This article belongs to the Special Issue Financial Econometrics and Models)
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