Time-varying Volatility Research Articles

STRUCTURAL BREAKS AND VOLATILITY PERSISTENCE IN HOTEL OCCUPANCY RATE

Purpose: Although Singapore has been one of the most attractive destinations for international tourists over the past 20 years, little evidence exists on the dynamics of its hotel occupancy rate. Our study aims to extend this scarce literature Design/Methodology/Approach: The present study employs time-varying volatility processes (e.g., GARCH process) and models the uncertainty associated with the hotel occupancy rate in Singapore. Findings: The main results indicate that shocks to the occupancy rate have a long run persistence and that there are asymmetric impacts in the occupancy rate. Further analysis suggests that structural breaks emanating from extreme news or shocks exert substantial impacts on the level of uncertainty for hotel occupancy rate. Originality/Value: Managers in the hotel industry could benefit from our findings while adopting active measures to reduce risk in tourism demand and hotel occupancy and attract more international tourists.

Modelling Multivariate Return Volatilities by Independent Components Using Johnson SU Likelihood

Time-varying volatility modelling is crucial in financial econometrics. For multivariate data, time-varying correlations across securities are important to explain the co-movement of the data. Therefore, we need to estimate the covariance matrix. Estimating a large conditional covariance matrix requires the estimation of a large number of parameters, which is a hard task and often leads to convergence difficulties in the estimation algorithm. We introduce a framework for forecasting the conditional covariance matrix of a large data set by employing a factor model to reduce dimensionality, with the factors estimated using independent components. The main contribution of our article is the estimation of the unobserved independent components using Johnson SU likelihood, which captures the skewness and excess kurtosis—stylized facts in stock returns. First, we extract the independent components using the Johnson SU distribution, maximizing their non-Gaussianity. Second, we sorted the independent components in terms of their explained variability and fit a univariate GARCH process on a few common independent components. Next, we approximate the conditional covariance matrix of the data through a linear combination of the conditional variances of those select few independent components. We apply this procedure to 50 stock returns data (listed in Nifty 50) that exhibit excess kurtosis and skewness, and the findings show that our methodology dominates other algorithms in extracting the independent components. This method enables fund managers to accurately forecast a large conditional covariance matrix, facilitating effective risk management and portfolio construction.

Asset prices, regime switches, and monetary policy: evidence from China

ABSTRACT This paper develops a regime-switching DSGE model that can capture time-varying volatilities in economic activity and monetary policy. The model is then used to study regime switches in macroeconomic dynamics and monetary policy in China. The results suggest a better fitness of the regime-switching model than conventional constant-parameter models. We also find notable differences in macroeconomic dynamics and monetary policy reactions between high and low volatility regimes. Further counterfactual analysis suggests that, at least in an environment with regime-switching volatilities, the optimal reaction of monetary policy involves responses to asset price variations. The findings of the paper shed new light on the regime-switching properties of the economy and the optimal choice of monetary policy within this context.

Labour at risk

We propose a Bayesian VAR model with stochastic volatility and time varying skewness to estimate the degree of labour at risk in the euro area and in the United States. We model the asymmetry of the shocks to changes in the unemployment rate as a function of real activity and financial risk factors. We find that the conditional distribution of the changes in the unemployment rate displays time-varying volatility and skewness, with peaks coinciding with the Global Financial Crisis and the COVID-19 pandemic, in both areas. We also take advantage of the multivariate nature of our parametric model to measure the joint risk of large increases in the unemployment rate together with large annual rates of inflation, a proxy for “stagflation” risks. The model captures an increase of the risk of stagflation with the surge in inflation that followed the recent energy crisis in 2022. Nevertheless, stagflation risks were contained by the resilient performance of the labour market in both areas. Labour at risk is therefore important for the assessment of the inflation-unemployment trade-off.

The Dissimilar Market Volatility in Neighboring Financial Markets: An Empirical Study Using A Multivariate Garch Model

ABSTRACT: Conventional knowledge of market volatility represents that two proximate financial markets operated in the same country would display a similar pattern in their time-varying volatility. In this paper, we propose that the heterogeneity in financial policy orientation of stock markets creates significant inter-market differences in terms of the volatility of stock returns, even if they are culturally and geographically linked to each other. For the empirical investigation of our proposition, the volatility data from two major stock exchanges in the Middle East, the Abu Dhabi Securities Exchange (ADX) and the Dubai Financial Market (DFM) are used. Both the ADX and DFM stock exchanges operate in the same country, the United Arab Emirates (UAE). The ADX is based in Abu Dhabi, the administrative capital, while the DFM is based in Dubai, the commercial and business hub of the UAE. They were both established in the year 2000, and their operational headquarters are located about 150 km from each other. This paper uses a multivariate GARCH model, in particular a diagonal VECH GARCH (1, 1) model to estimate volatility measures for stock returns between ADX-listed and DFM-listed stocks. The paper finds that the stock returns are significantly less volatile in the ADX compared to those in the DFM suggesting that the volatility transmission is incomplete between these two neighboring financial markets. This difference in volatility can potentially be attributed to the relatively conservative financial policies adopted by the oil-rich emirate of Abu Dhabi compared to the more market-oriented economic policies of Dubai, the country’s financial and commercial hub. Our findings have considerable implications for portfolio managers in ascertaining risk premiums when allocating investments across the two stock exchanges. For instance, this disparity in volatility should be taken into account by investors and policymakers when designing risk management strategies because it suggests that investors in the DFM may be exposed to higher levels of risk and uncertainty compared to their counterparts in the ADX. Investors operating in the DFM may require more robust risk management strategies, such as asset diversification and hedging, compared to their ASX counterparts.

Evaluating the Efficacy of GARCH Models in Forecasting Volatility Dynamics Across Major Global Financial Indices: A Decade-long Analysis

This study investigates the volatility dynamics of five major global financial indices—FTSE 100, Hang Seng, NIKKEI 225, NSE 50, and S&P 500—using a range of GARCH models over a ten-year period from January 1, 2014, to December 31, 2023. The analysis involves preprocessing the data to ensure stationarity, calculating log returns, and conducting stationarity and ARCH effect LM tests. Various GARCH models, including GARCH (0,1), GARCH (1,1), GARCH (1,2), and GARCH (2,2), are applied to capture and forecast volatility. The study aims to determine the most effective model for accurately reflecting volatility dynamics while accounting for significant market events such as the COVID-19 pandemic. The findings reveal that the GARCH (1,1) model generally provides a robust balance between model simplicity and statistical significance, effectively capturing the time-varying volatility of the indices. Despite some complex models offering better fit measures according to the Akaike Information Criterion (AIC) and Schwarz Criterion (SC), the GARCH (1,1) model consistently demonstrates significant parameter estimates and reliable predictive performance, as evidenced by consistent Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. This suggests that the GARCH (1,1) model is a preferred choice for volatility forecasting due to its effectiveness and parsimony, although future research might explore more advanced GARCH model variations for further refinement.

Complex and Multifaceted Nature of Cryptocurrency Markets: A Study to Understand its Time-Varying Volatility Dynamics

Complex and Multifaceted Nature of Cryptocurrency Markets: A Study to Understand its Time-Varying Volatility Dynamics

Exploring interconnections and risk evaluation of green equities and bonds: fresh perspectives from TVP-VAR model and wavelet-based VaR analysis

PurposeThis study attempts to examine the time-varying volatility spillovers between environmentally sustainable assets and quantify the value-at-risk of the portfolios across various frequencies.Design/methodology/approachTo accomplish these objectives, this paper utilizes a connectedness index-based TVP-VAR model and applies the wavelet-based VaR ratio to daily data spanning from January 2018 to September 2023.FindingsThe empirical findings reveal a notable increase in the connectedness index between green stocks and green bonds during the COVID-19 crisis, signifying evidence of a contagion effect. The portfolio’s risk ratio also exhibited a sharp rise amid the pandemic, particularly over medium and long-term horizons, driven by increased spillover among green assets. Notably, our analysis indicates that green bonds influence the connectedness system between green stocks and the value-at-risk ratio, reducing volatility spillover and portfolio risk ratios across various investment horizons. These results highlight the role of green bonds as an effective diversification asset against the risks associated with green equities.Originality/valueThis research investigates the dynamic connectedness and value-at-risk ratio between eight green sectoral renewable energy and non-energy equities and green bonds. We put forward some portfolio implications for green investors with an environmental consciousness who desire to decarbonize their portfolios and mitigate environmental issues.

Volatility spillover and dynamic correlation between Islamic, conventional, cryptocurrency and precious metal markets during the immediate outbreak of COVID-19 pandemic

PurposeThis paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic.Design/methodology/approachThe MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons.FindingsThe authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons.Practical implicationsThe study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios.Originality/valueThis study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.

The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis

This paper presents a comprehensive multiscale analysis of volatility spillover mechanisms among diverse financial markets utilizing a novel combination of Normalized Adaptive Multivariate Empirical Mode Decomposition (NA-MEMD) and Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. Our analysis spans Bitcoin, crude oil, gold, foreign exchange, emerging stock markets, and developed stock markets, examining their respective roles in global volatility transmission. Utilizing the NA-MEMD methodology and a time-varying volatility spillover index, we find that in the short-term, Bitcoin and crude oil primarily act as volatility receivers, while other markets serve as volatility transmitters. Over the long-term, gold and Bitcoin consistently act as volatility receivers, with Bitcoin and gold showing evident safe-haven effects, while the remaining markets primarily function as volatility transmitters. Particularly post-COVID-19, developed stock markets emerge as significant transmitters of volatility, largely directed towards the crude oil market. By evaluating and comparing risk transmission patterns among financial markets before and during the COVID-19 pandemic, our findings offer valuable insights that can assist policymakers and other stakeholders in managing financial uncertainty in times of global crises.

Nominal exchange rates and heterogeneous beliefs

We propose a two-country model with heterogeneous beliefs to understand dynamics of nominal exchange rate. Facing a shock to monetary policy, disagreement between domestic and foreign investors shifts the relative wealth of investors – an essential part of the stochastic discount factor in our model – which then moves the foreign exchange rate. Calibrated to U.S. and U.K. data, our model reveals that dispersion in beliefs predicts the future spot exchange rate, associates with the cross-section of currency risk premia, and comoves with the time-varying volatility in currency returns. Furthermore, our model suggests that domestic investors would hold fewer foreign currency-denominated bonds in countries with greater disagreements on monetary policy.

A greater crisis? Investigating MSA-level housing markets during the COVID-19 pandemic

This study analyzes the impact of uncertainties on ten major metropolitan statistical area (MSA)-level housing markets in the US from 2000 to 2022, elucidating the distinctive characteristics of crisis-like phenomena triggered by COVID-19. The study assesses crisis and noncrisis periods and finds that time-varying volatility in uncertainty is evident during crises, and economic policy uncertainty and macroeconomic uncertainty shocks show heightened volatility. The results suggest that the COVID-19 pandemic represents a more substantial crisis than the housing market crisis of 2007–2009. The findings indicate that uncertainty elevated MSA-level housing returns in 2000–2022 and offer supporting evidence for a flight-to-quality pattern as investors tended to allocate capital to safer real estate assets. The transmission mechanism from uncertainty to metropolitan housing markets is phase-dependent, with estimated responses to uncertainty shocks during crisis phases being more robust than those observed in normal times. This study provides valuable insights for monetary policymakers and offers guidance for investors’ risk management strategies.

Equilibrium bifurcation and extreme risk in the EU carbon futures market

Considering the long-term memory and volatility clustering of the European Union (EU) Carbon Emission Allowances (EUA) futures returns, based on the economy–energy–environment system perspective and the assumption of investors' heterogeneity, this study proposes a joint modeling approach combining the fractionally integrated generalized autoregressive conditional heteroscedasticity model (FIGARCH) and the stochastic cusp catastrophe model (SCC) to examine the equilibrium bifurcations and extreme risks in the EU carbon futures market. The relevant results are threefold. (1) The SCC model has good fitting effect and interpretability, and is an effective method for investigating catastrophe reactions under time-varying volatility conditions. (2) In the EUA futures market, chartists are mainly affected by short-term price and trading volume changes, which leads to the emergence of equilibrium bifurcations, while fundamentalists make investment decisions based on the economy, the energy market, and market supply–demand, which affects the asymmetry of equilibrium bifurcations. (3) Using the catastrophe criterion (i.e., Cardan's discriminant of the equilibrium surface equation), we identify148 equilibrium bifurcation time points in the EUA futures market from December 3, 2009 to September 16, 2020, most of which are concentrated in two upward periods with an average scale of extreme risks is about 32.51 %. Our analysis provides theoretical support for regulatory authorities to stabilize the carbon futures market and build a collaborative extreme risk management framework covering energy and macroeconomics, also proposing suggestions for traders to effectively prevent extreme risks.

An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition

In this paper, we address the problem of probabilistic forecasting using an adaptive volatility method rooted in classical time-varying volatility models and leveraging online stochastic optimization algorithms. These principles were successfully applied in the M6 forecasting competition under the team named AdaGaussMC. Our approach takes a unique path by embracing the Efficient Market Hypothesis (EMH) instead of trying to beat the market directly. We focus on evaluating the efficient market and emphasize the importance of online forecasting in adapting to the dynamic nature of financial markets. The three key points of our approach are: (a) apply the univariate time-varying volatility model AdaVol, (b) obtain probabilistic forecasts of future returns, and (c) optimize the competition metrics using stochastic gradient-based algorithms. We contend that the simplicity of our approach contributes to its robustness and consistency. Our performance in the M6 competition resulted in an overall 7th place, with a 5th place in the forecasting task. Considering our approach’s perceived simplicity, this achievement underscores the efficacy of our adaptive volatility method in probabilistic forecasting.

Time series clustering based on latent volatility mixture modeling with applications in finance

Modeling financial time series data poses a significant challenge in the realm of time series analysis. The Autoregressive Conditional Heteroskedasticity (ARCH) model stands out as a potent tool for capturing time-varying volatility and heteroskedasticity in financial data. However, conventional ARCH models display sensitivity to departures from normality, leading to the development of extensions employing more flexible distributions. In this context, we propose a robust enhancement to the mixture of ARCH (MoARCH) model by integrating normal mean–variance mixture (NMVM) distributions to model component errors. The stochastic representation of the proposed model allows for a straightforward implementation of an Expectation Conditional Maximization Either (ECME) algorithm for obtaining Maximum Penalized Likelihood estimates (MPL). To thoroughly evaluate the model, we conduct four simulation studies to explore finite-sample properties, assess MPL estimators, scrutinize model robustness, and evaluate the accuracy of our proposal in fitting, clustering, and forecasting. Practical applications further highlight the effectiveness of our methodology, showcasing successful implementations across diverse real datasets.

Risk transference between climate variability and financial derivatives: Implications for global food security

We investigate the impacts of the El Niño-Southern Oscillation (ENSO) on global food security by using information embedded in financial derivatives. Using a state-of-the-art general circulation model (GCM) climate forecasting system that generates observations on the phase and magnitude of ENSO, we create novel indices that track its uncertainty throughout time. Together with the option implied volatilities of core food inputs (wheat, maize, rice, and soybean), we model the time-varying volatility spillover from ENSO to each commodity, capturing the direction and intensity of risk transference. Simulating Gaussian and non-Gaussian impulse responses that mimic an increase in climate variability show a persistent impact on the price uncertainty of the commodities lasting up to three months. We also show that shocks are contingent on the phase of ENSO, with warmer conditions in the Eastern Pacific (i.e., the El Niño phase) increasing risk transference for soybean and rice. In contrast, the La Niña phase has a material influence on the uncertainty of wheat and maize. In all, we reveal a volatility transmission channel of climate variability that affects financial markets, suggesting valuable inferences about the impact of climate shocks can be derived from the rich information embedded in commodity-linked derivatives.

Monetary Policy, Investor Sentiment, and the Asymmetric Jump Risk of Chinese Stock Market

To investigate the impacts of monetary policies on the jump risk of Chinese stock market, we introduce them into an exponential generalized autoregressive conditional heteroskedasticity with autoregressive jump intensity (EGARCH-ARJI) model. A new jump model, i.e., the EGARCH-ARJI model with monetary policy (EGARCH-AM), is constructed. Moreover, investor sentiment is considered to investigate the interaction effect of a monetary policy and investor sentiment on the jump intensity. Results show that the announcement of an interest rate policy has significantly positive effect on it, while the effects of the announcement and implementation of a required reserve ratio policy are not significant. In addition, the interaction effect of an interest rate policy and investor sentiment on the jump intensity is positive. The interaction effect of the announcement of a required reserve ratio policy and investor sentiment is negative. The interaction effect of the implementation of a required reserve ratio policy and investor sentiment does not exist. The research results are of guiding significance for policy makers and investors to fully learn the time-varying volatility and jump risk of stock markets.

Real-time forecast of DSGE models with time-varying volatility in GARCH form

Recent research shows that time-varying volatility plays a crucial role in non-linear modeling. Contributing to this literature, we suggest an approach that allows for straightforward computation of DSGE models with time-varying volatility, where the volatility component is formulated as a GARCH process. As an application of our approach, we examine the forecasting performance of this DSGE-GARCH model using euro area real-time data. Our findings suggest that the DSGE-GARCH approach is superior in out-of-sample forecasting performance in comparison to various other benchmarks for the forecast of inflation rates, output growth and interest rates, especially in the short term. Comparing our approach to the widely used stochastic volatility specification using in-sample forecasts, we also show that the DSGE-GARCH is superior in in-sample forecast quality and computational efficiency. In addition to these results, our approach reveals interesting properties and dynamics of time-varying correlations (conditional correlations).

Markov-switching multifractal volatility spillovers among European stock markets during crisis periods

ABSTRACT This research investigates time-varying volatility spillovers and connectedness among European stock markets during the COVID-19 pandemic and the Russia – Ukraine war, two events that destabilized global markets. With data from 20 European stock markets spanning 17 December 2019, to 17 March 2022, we employ the TVP-VAR model and estimate volatility using the Markov-switching multifractal volatility technique. Findings from log-volatility estimates suggest that markets are highly connected, with price movement driven mainly by spillover effects from other markets in the same region. Most emerging markets are net receivers of volatility, with most of Europe’s major markets being net transmitters of shocks. The COVID-19 pandemic appears to have impacted European stock markets more than the Russia – Ukraine war. Shifting to the results obtained based on MSM volatility estimates, we find that markets strongly correlate for both high and low volatility. In the case of a high volatility regime, we document the dominance of Finland, Denmark, and Iceland over major European markets. In contrast, under a low volatility regime, we note the dominance of major markets, including the UK and France, over emerging markets in Europe. The findings reveal the diversification potential of emerging European stock markets.

Flexible markov-switching models with evolving regime-specific parameters: an application to Brazilian business cycles

ABSTRACT This article develops a flexible class of Markov-switching models in which the GDP growth rate is decomposed into a long-run growth trend and evolving regime-dependent means. The models can account for multiple regimes, breaks in the long-run trend, stochastic volatility, and time-varying transition probabilities. They can also handle data outliers that may arise from rare events, such as the COVID-19 crisis. We illustrate our methodology by modelling Brazilian GDP growth, which has exhibited complicated dynamics over the past four decades. Our results suggest two regimes, one long-run trend break, significant time variation in volatility, and the presence of outliers. Moreover, the selected model features time-varying transition probabilities driven by domestic variables (fiscal stance, reserves, and the real interest rate). Significantly, our findings indicate a marked decline in Brazil’s long-run growth in recent years.