scholarly journals Volatility Model Choice for Sub-Saharan Frontier Equity Markets - A Markov Regime Switching Bayesian Approach

2019 ◽  
Vol 9 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Carl Hope Korkpoe ◽  
Nathaniel Howard
Keyword(s):  
Bayesian Approach ◽  
Regime Switching ◽  
Equity Markets ◽  
Equity Returns ◽  
Information Criteria ◽  
Model Choice ◽  
Markov Regime Switching ◽  
Volatility Models ◽  
Volatility Model ◽  
Sub Saharan

We adopt a granular approach to estimating the risk of equity returns in sub-Saharan African frontier equity markets under the assumption that, returns are influenced by developments in the underlying economy. Four countries were studied – Botswana, Ghana, Kenya and Nigeria. We found heterogeneity in the evolution of volatility across these markets and also that two-regime switching volatility models describe better the heteroscedastic returns generating processes in these markets using the deviance information criteria. We backtest the results to assess whether the models are a good fit for the data. We concluded that, the selected models are the most suitable for predicting the volatility of future returns in the markets studied. 

scholarly journals Pemodelan Deteksi Krisis Keuangan di Indonesia Berdasarkan Indikator M2/Cadangan Devisa

2019 ◽  
Vol 16 (2) ◽  
pp. 98-103
Author(s):  
Aisyah Zahrotul Hidayah ◽  
Sugiyanto Sugiyanto ◽  
Isnandar Slamet
Keyword(s):  
Financial Crisis ◽  
Regime Switching ◽  
Structural Changes ◽  
Detection System ◽  
International Reserves ◽  
Markov Regime Switching ◽  
Volatility Models ◽  
Macroeconomic Indicators ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Very High

The banking crisis reflects the liquidity crisis and bankruptcy of banks in the financial system. The financial crisis that occurred in mid-1997 resulted in a financial crisis that had a severe impact on the Indonesian economy. This made it aware of the importance of building a financial crisis early detection system to prepare for a crisis. The crisis occurs due to several macroeconomic indicators undergoing structural changes (regimes) and contain very high fluctuations. Combined volatility models and Markov regime switching are very suitable for explaining crises. The M2/international reserves indicator from 1990 to 2018 was used to build a crisis model. The results showed that the Markov regime switching autoregressive conditional heteroscedasticity model MRS-ARCH(2,1) could explain the crisis that occurred in mid-1997. Based on this model, in the future the crisis might occur if the M2/international reserves indicator decreased minimum of 13%

A swaption volatility model using Markov regime switching

2008 ◽  
Vol 12 (1) ◽  
pp. 79-114 ◽  
Author(s):  
Richard White ◽  
Riccardo Rebonato
Keyword(s):  
Regime Switching ◽  
Markov Regime Switching ◽  
Volatility Model

scholarly journals Modeling Precious Metal Returns through Fractional Jump-Diffusion Processes Combined with Markov Regime-Switching Stochastic Volatility

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 407
Author(s):  
Martha Carpinteyro ◽  
Francisco Venegas-Martínez ◽  
Alí Aali-Bujari
Keyword(s):  
Decision Making ◽  
Stochastic Volatility ◽  
Regime Switching ◽  
Precious Metal ◽  
Precious Metals ◽  
Stochastic Volatility Model ◽  
Decision Making Process ◽  
Markov Regime Switching ◽  
Volatility Model ◽  
Gold Silver

This paper is aimed at developing a stochastic volatility model that is useful to explain the dynamics of the returns of gold, silver, and platinum during the period 1994–2019. To this end, it is assumed that the precious metal returns are driven by fractional Brownian motions, combined with Poisson processes and modulated by continuous-time homogeneous Markov chains. The calibration is carried out by estimating the Jump Generalized Autoregressive Conditional Heteroscedasticity (Jump-GARCH) and Markov regime-switching models of each precious metal, as well as computing their Hurst exponents. The novelty in this research is the use of non-linear, non-normal, multi-factor, time-varying risk stochastic models, useful for an investors’ decision-making process when they intend to include precious metals in their portfolios as safe-haven assets. The main empirical results are as follows: (1) all metals stay in low volatility most of the time and have long memories, which means that past returns have an effect on current and future returns; (2) silver and platinum have the largest jump sizes; (3) silver’s negative jumps have the highest intensity; and (4) silver reacts more than gold and platinum, and it is also the most volatile, having the highest probability of intensive jumps. Gold is the least volatile, as its percentage of jumps is the lowest and the intensity of its jumps is lower than that of the other two metals. Finally, a set of recommendations is provided for the decision-making process of an average investor looking to buy and sell precious metals.

scholarly journals Effect of Oil Prices on Exchange Rate Movements in Korea and Japan Using Markov Regime-Switching Models

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4402
Author(s):  
Suyi Kim ◽  
So-Yeun Kim ◽  
Kyungmee Choi
Keyword(s):  
Exchange Rate ◽  
Financial Crisis ◽  
Foreign Exchange ◽  
Regime Switching ◽  
Oil Prices ◽  
Information Criteria ◽  
Markov Regime Switching ◽  
Regime Switching Models ◽  
Switching Models ◽  
Exchange Rate Movements

We examined the effects of oil prices along with fundamental economic variables on exchange rate movements in the Korean and Japanese foreign exchange markets, using two-regime Markov Regime Switching Models (MRSMs) over the period from January 1991 to March 2019. We selected the best MRSMs explaining their exchange rate movements using the Maximum Log-Likelihood and Akaike Information Criteria, and analyze effects of oil prices on their exchange rates based on the selected best MRSMs. We consider two regimes, regime 1 with high-volatility and regime 2 with low-volatility. In Korea, two apparent regimes are observed, and unstable regime 1 consists of two distinct prolonged periods, the 1997 Asian Financial Crisis and the 2008 Global Financial Crisis. Meanwhile in Japan, no evident prolonged regimes are observed. Rather, the two regimes occasionally alternate. Oil prices influence exchange rate movements in regime 2 with low-volatility in Korea, while they do not influence exchange rate movements in either regimes in Japan. The Japanese foreign exchange market is more resistant to external oil price shocks because the Japanese industry and economy has less dependence on oil than Korea.

Intertemporal risk-return relationship in BRIC equity markets after the US financial crisis

2017 ◽  
Vol 59 (4) ◽  
pp. 547-570 ◽  
Author(s):  
Amanjot Singh ◽  
Manjit Singh
Keyword(s):  
Financial Crisis ◽  
Regime Switching ◽  
Global Financial Crisis ◽  
Equity Markets ◽  
Impulse Response Functions ◽  
Markov Regime Switching ◽  
Content Type ◽  
Risk Return ◽  
The Global Financial Crisis ◽  
Markov Regime Switching Model

PurposeThis paper aims to attempt to capture the intertemporal/time-varying risk–return relationship in the Brazil, Russia, India and China (BRIC) equity markets after the global financial crisis (2007-2009), i.e. during a relative calm period. There has been a significant increase in advanced economies’ equity allocations to the emerging markets ever since the financial crisis. So, the present study is an attempt to account for the said relationship, thereby justifying investments made by the international investors. MethodologyThe study uses non-linear models comprising asymmetric component generalised autoregressive conditional heteroskedastic model in mean (CGARCH-M) (1,1) model, generalised impulse response functions under vector autoregressive framework and Markov regime switching in mean and standard deviation model. The span of data ranges from 1 July 2009 to 31 December 2014. FindingsThe ACGARCH-M (1,1) model reports a positive and significant risk-return relationship in the Russian and Chinese equity markets only. There is leverage and volatility feedback effect in the Russian market because falling returns further increase conditional variance making the investors to expect a risk premium in the expected returns. The impulse responses indicate that for all of the BRIC markets, the ex-ante returns respond positively to a shock in the long-term risk component, whereas the response is negative to a shock in the short-term risk component. Finally, the Markov regime switching model confirms the existence of two regimes in all of the BRIC markets, namely, Bull and Bear regimes. Both the regimes exhibit negative relationship between risk and return. Practical implicationsIt is an imperative task to comprehend the relationship shared between risk and returns for an investor. The investors in the emerging economies should understand the risk-return dynamics well ahead of time so that the returns justify the investments made under riskier environment. Originality/valueThe present study contributes to the literature in three senses. First, the data relate to a period especially after the global financial crisis (2007-2009). Second, the study has used a relatively newer version of GARCH based model [ACGARCH-M (1,1) model], generalised impulse response functions and Markov regime switching model to account for the relationship between risk and return. Finally, the study provides an insightful understanding of the risk–return relationship in the most promising emerging markets group “BRIC nations”, making the study first of its kind in all the perspectives.

On the Bayesian Risk Evaluation of Minimum Guarantees in Variable Annuities

2015 ◽  
Vol 0 (0) ◽  
Author(s):  
Byoung Hark Yoo ◽  
Bangwon Ko ◽  
Hyuk-Sung Kwon
Keyword(s):  
Regime Switching ◽  
Capital Requirements ◽  
Variable Annuities ◽  
Substantial Impact ◽  
Statistical Framework ◽  
Volatility Models ◽  
Volatility Model ◽  
Significant Difference ◽  
Korean Market

AbstractLong-term equity return models have a substantial impact on calculating reserves and capital requirements for minimum guarantees in variable annuities (VAs). Under a Bayesian statistical framework, we reinvestigate the standard long-term equity return models (independent lognormal, regime-switching lognormal and stochastic log volatility models) with the Korean equity market data. Our empirical analysis shows that the long-term behaviors of the Korean market are best described by the regime-switching lognormal models, and there can be significant difference in the amount of the reserves and the capital requirements depending on the model selection. The long-term nature of the VA contracts requires that more caution be paid for modeling the mean part of the stochastic log volatility model.

scholarly journals A Markov regime switching approach to estimating the volatility of Johannesburg Stock Exchange (JSE) returns

2019 ◽  
Vol 16 (1) ◽  
pp. 215-225
Author(s):  
Emmanuel K. Oseifuah ◽  
Carl H. Korkpoe
Keyword(s):  
Regime Switching ◽  
Stock Exchange ◽  
Information Criteria ◽  
Markov Regime Switching ◽  
Regime Switching Model ◽  
Switching Model ◽  
Johannesburg Stock Exchange ◽  
Industry Standard ◽  
Egarch Model ◽  
Markov Regime Switching Model

The study used the Markov regime switching model to investigate the presence of regimes in the volatility dynamics of the returns of JSE All-Share Index (ALSI). Volatility regimes are as a result of sudden changes in the underlying economy generating the market returns. In all, twelve candidate models were fitted to the data. Estimates from the regime switching model were compared to the industry standard non-switching GARCH (1,1) using the Deviance Information Criteria (DIC). The results show that the two-regime switching EGARCH model with skewed Student t innovations describes better the return of the JSE Index. Additionally, we backtest the model results in order to confirm our findings that the two-regime switching EGARCH is the best of the models for the sample period.

Sign in / Sign up

Export Citation Format

Share Document