Long-run Volatility Components Research Articles

Fintech industry risk: does investor attention matter?

ABSTRACT The Fintech industry risk challenges the stability of China’s financial system. This paper makes the first attempt to capture the dynamics of the Fintech industry risk, represented by Fintech stock volatility, from the investor attention perspective. The novelty of this paper is that we fully consider the regime changes in the Fintech industry risk by adopting the AR-MS-GARCH-MIDAS with the Markov regime-switching process incorporated in the short-run volatility component, long-run volatility component, or both. We find that (1) Fintech industry risk is subject to structural breaks, (2) higher investor attention tends to alleviate the Fintech stock volatility, while the negative impact strengthens when the industry is in volatile condition, (3) investor attention contributes to improving the accuracy of the Fintech stock volatility forecasting, (4) considering the structural breaks of investor attention is important in volatility forecasting. We enrich the literature on the Fintech industry risk regulation, stock volatility forecasting, and the attention allocation theory.

Option Valuation with Nonmonotonic Pricing Kernel and Embedded Volatility Component Premiums

This article develops a nonmonotonic pricing kernel with long-run and short-run variance risk premiums for option valuation, with a proposed pricing kernel retaining a U-shaped pattern that significantly improves the fitting ability for index options pricing and implied volatility. The estimation results show that the long-run volatility component is critical in generating the negative risk premium. In the in-sample and out-of-sample tests, the model with the new pricing kernel has more accurate predictions, especially the year around the financial crisis, wherein there is a decrease of an average of 35% root mean square error relative to the benchmark. Considering the bull and bear market states, our model improves implied volatility root mean square error by 23% on average.

Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks

We investigate the role of geopolitical risks in forecasting stock market volatility at monthly horizons within a robust autoregressive Markov-switching GARCH mixed-data-sampling (AR-MSGARCH-MIDAS) framework. Our approach accounts for structural breaks through regime switching and allows us to disentangle short- and long-run volatility components. We conduct an empirical out-of-sample forecasting analysis using (i) daily Dow Jones Industrial Average returns, and (ii) monthly sampled geopolitical risks and macroeconomic variables over a time span of 122 years. We find that the impact of geopolitical risks as explanatory variables for stock market volatility forecasts at monthly horizons hinges crucially on the specific prediction model chosen by the forecaster. After capturing the non-stationarities in the data via an MSGARCH framework, we do not find significant forecast accuracy improvements through the inclusion of geopolitical risk indices.

Retracted] Analysis of Factors Influencing Stock Market Volatility Based on GARCH‐MIDAS Model

This paper further extends the existing GARCH‐MIDAS model to deal with the effect of microstructure noise in mixed frequency data. This paper has two highlights. First, according to the estimation of the long‐term volatility components of the GARCH‐MIDAS model, rAVGRV is adopted to substitute for the RV estimator. rAVGRV uses the rich data sources in tick‐by‐tick data and significantly corrects the impact of the microstructure noise on volatility estimation. Second, besides introducing macroeconomic variables (i.e., macroeconomic consistency index (MCI), deposits in financial institutions (DFI), industrial value‐added (IVA), and M2), Chinese Economic Policy Uncertainty (CEPU) index and Infectious Disease Equity Market Volatility Tracker (EMV) are introduced in the long‐run volatility component of the GARCH‐MIDAS model. As indicated by the results of this paper, the rAVGRV‐based GARCH‐MIDAS is slightly better than the RV model‐based GARCH‐MIDAS. In addition to the common macroeconomic variables significantly impacting stock market volatility, CEPU also substantially impacts stock market volatility. Nevertheless, the effect of EMV on the stock market is insignificant.

Long-Range Dependence in Financial Markets: A Moving Average Cluster Entropy Approach.

A perspective is taken on the intangible complexity of economic and social systems by investigating the dynamical processes producing, storing and transmitting information in financial time series. An extensive analysis based on the moving average cluster entropy approach has evidenced market and horizon dependence in highest-frequency data of real world financial assets. The behavior is scrutinized by applying the moving average cluster entropy approach to long-range correlated stochastic processes as the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Fractional Brownian motion (FBM). An extensive set of series is generated with a broad range of values of the Hurst exponent H and of the autoregressive, differencing and moving average parameters . A systematic relation between moving average cluster entropy and long-range correlation parameters H, d is observed. This study shows that the characteristic behaviour exhibited by the horizon dependence of the cluster entropy is related to long-range positive correlation in financial markets. Specifically, long range positively correlated ARFIMA processes with differencing parameter , and are consistent with moving average cluster entropy results obtained in time series of DJIA, S&P500 and NASDAQ. The findings clearly point to a variability of price returns, consistently with a price dynamics involving multiple temporal scales and, thus, short- and long-run volatility components. An important aspect of the proposed approach is the ability to capture detailed horizon dependence over relatively short horizons (one to twelve months) and thus its relevance to define risk analysis indices.

Non-Affine Stochastic Volatility With Seasonal Trends

We propose a new stochastic volatility model for pricing options on assets that exhibit seasonal trends in volatility. Such assets are prevalent among commodities, with futures on grains and energy being an example. The model is based on the 3/2 stochastic volatility model, but includes a cyclical long-run volatility component. The model yields a closed-form characteristic function which can be used to rapidly calibrate the model to benchmark options. We test the model on market data and show that the model proposed here allows for significantly better empirical fit to option prices on corn and wheat futures than the unmodified 3/2 model.

The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets

We examine the macroeconomic determinants of the volatility of commodity futures (including agricultural commodity futures, metal futures and oil futures) in two emerging commodity markets – China and India. The macroeconomic variables used include both domestic and international macroeconomic variables that gauge economic environment, monetary policy and financial market information. We use a recently proposed GARCH-MIDAS model which jointly incorporates the daily price volatility and low-frequency macroeconomic variables. The model decomposes the volatility series into short- and long-run components, thereby enabling us to test whether the macroeconomic variables can determine the long-run variance. We find that there exists a long-run volatility component in the commodity futures, and most of the tested low-frequency macroeconomic variables are positively related to the long-run variance of commodity futures. Our results suggest that both domestic and international macroeconomic information plays an important role in determining the price volatility of the emerging commodity futures.

Do BRIC countries stock market volatility move together? An empirical analysis of using multivariate GARCH models

This study investigates the stock return volatility of BRIC countries for 15 years from January 2000 to September 2015 by using GARCH, CHARMA, APARCH and CGARCH models. The study finds the presence of asymmetric and leverage effects in all BRIC countries stock market return. The analysis also reports that the time varying long-run volatility component is more persistent in Chinese and Russian stock markets. The temporary effect of short-run volatility persistence is found in Indian and Brazilian stock markets. The empirical result further exhibits that volatility shocks are quite persistent in all BRIC countries stock markets revealing the changing pattern of volatility over time. The CUSUM of square test also confirms that volatility is changing over time in all BRIC countries stock markets. It is suggested that an investor can leverage expected amount of return by investing in Indian and Brazilian stock markets. The study also proposes that investors are advised to be more sensible towards volatility dynamics of BRIC countries stock markets for favourable portfolio selection and managing financial risks before making investment decisions to leverage real return on the market.

A Comparative Study of the Taiwan and Japan Equity and Foreign Exchange Markets: Modeling, Estimation and Application of the Component Garch-in-Mean Model

The main purpose of this paper is to verify the effectiveness of the bivariate Component GARCH-in-mean (GARCH-M) model and analyze the interactions and risk premium of equity markets by exploring the short- and long-run volatility components on both the Taiwanese and Japanese equity markets. We show that unexpected shocks of volatility will in general influence the fluctuations of both equity and foreign exchange markets. Persistence on the long-run volatility components of both markets is also found. The results also reveal that the positive risk-return relation on equity markets can be further verified when the impacts of short and long-run volatility components are decomposed by the Component GARCH-M model. The decomposition can also facilitate reflecting the transitory and permanent volatility impacts of foreign exchange exposure on the returns of equity markets.

Dissecting Volatility Risks in Currency Markets

We investigate the pricing of volatility risks in currency markets. First, we show that pricing ability of volatility risk is concentrated in some of its components. Diffusive volatility dominates jump volatility in pricing carry trade returns, while jump volatility is important in jointly explaining carry trade and momentum returns. Both short-run and long-run volatility components are priced, and the short-run component is more important in general. Second, we suggest that factors similar to volatility in identifying bad states, i.e. volatility of volatility and cross sectional volatility are also priced and are not fully subsumed by conventional volatility measures.

Risk and Return Trade-Off in the U.S. Treasury Market

This paper characterizes the risk-return trade-off in the U.S. Treasury market. We propose a discrete-time no-arbitrage term structure model, in which bond prices are solved in closed form and the conditional variances of bond yields are decomposed into a short-run component and a long-run component, each of which follows a GARCH-type process. Estimated using Treasury yields data from January 1962 to August 2007, our model simultaneously matches the conditional volatility dynamics and the deviation from the expectations hypothesis in the data. We find that a higher short-run volatility component of bond yields significantly predicts a higher future excess return, above and beyond the predictive power of the yields. The long-run volatility component does not predict bond excess returns.

Study on the Extreme Risk Spillover between China and World Stock Market after China’s Share Structure Reform

With the rising importance of China’s role in the world economy, the Chinese economic fluctuation has become a more and more significant factor that influences the world economy. Therefore, it is an interesting issue for all circles as well as academicians that whether the real economic inter-connection leads to volatility spillover between China’s and international stock markets. In this paper, CGARCH (Combine Generalized Auto Regressive Conditional Heteroskedasticity) model and Granger causality test are applied to examine the relationship between China’s A share index and world’s major indices with respect to the extreme risk spillover effect. The results show that the extreme risk of A share market’s long-run volatility component has strong risk spillover effect on foreign markets, while the short term volatility is vulnerable to the risks from overseas. Since long-run volatility component is consistent with real economic cycle, our results support that China’s economy has deep impact on world economy.

Volatility Trading: What Is the Role of the Long-Run Volatility Component?

AbstractWe study an investor’s asset allocation problem with a recursive utility and with tradable volatility that follows a 2-factor stochastic volatility model. Consistent with previous findings under the additive utility, we show that the investor can benefit substantially from volatility trading due to hedging demand. Unlike existing studies, we find that the impact of elasticity of intertemporal substitution (EIS) on investment decisions is of 1st-order importance. Moreover, the investor can incur significant economic losses due to model and/or parameter misspecifications where the EIS better captures the investor’s attitude toward risk than the risk aversion parameter.

What Does Financial Volatility Tell Us About Macroeconomic Fluctuations?

This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of aggregated and industry-level stock volatility, and bond market volatility from daily returns. We model log financial volatility as composed of a long-run component that is common across all series, and a short-run component. If volatility has components, volatility proxies are characterized by large measurement error, which veils analysis of their fundamental information and relationship with the economy. We find that there are substantial gains from using the long term component of the volatility measures for linearly projecting future economic activity, as well as for forecasting business cycle turning points. When we allow for asymmetry in the long-run volatility component, we find that it provides early signals of upcoming recessions. In a real-time out-of-sample analysis of the last recession, we find that these signals are concomitant with the first signs of distress in the financial markets due to problems in the housing sector around mid-2007 and the implied chronology is consistent with the crisis timeline.

Volatility Trading: What is the Role of the Long-Run Volatility Component?

In this paper, we study an investor's asset allocation problem with a recursive utility and with tradable volatility that follows a two-factor stochastic volatility model. Consistent with Liu and Pan (2003) and Egloff, Leippold, and Wu's (2009) finding under the additive utility, we show that volatility trading generates substantial hedging demand, and so the investor can benefit substantially from volatility trading. However, unlike existing studies, we find that the impact of elasticity of intertemporal substitution on investment decisions is of first-order importance in the two-factor stochastic volatility model when the investor has access to the derivatives market to optimally hedge the persistent component of the volatility shocks. Moreover, we study the economic impact of model and parameter misspecifications and find that an investor can incur substantial economic losses if he uses an incorrect one-factor model instead of the two-factor model or if he incorrectly estimates one of the key parameters in the two-factor model. In addition, we find that the elasticity of intertemporal substitution is a more sensible description of an investor's attitude toward model and parameter misspecifications than the risk aversion parameter.

Long-memory and heterogeneous components in high frequency Pacific-Basin exchange rate volatility

Recent research examining high-frequency financial data has suggested that volatility dynamics may be confounded by the existence of an intra-day periodic pattern and multiple sources of volatility. This paper examines whether these dynamics are present in the US Dollar exchange rates of five Pacific Basin economies. Using 30-min sampled returns, evidence of a ‘U’-shape intra-day pattern in volatility for regional markets is reported and controlled for using a Flexible Fourier transform. Supportive evidence for the existence of multiple volatility components is offered by semi-parametric fractional difference estimates of the long-memory properties of absolute exchange rate returns at various intra-day data sampling frequencies. Further parametric evidence of an explicit component structure in such high frequency exchange rate volatility is offered by the estimates of a component-GARCH model which comprises both a long-run volatility component exhibiting slow shock decay and a short-run volatility component exhibiting far more rapid decay, and provides a generally superior fit to the data. Further application of these C-GARCH models in the analysis of high frequency volatility spillovers between the currencies considered also reveals that such spillovers are predominantly transitory rather than highly persistent in nature, but that where volatility spillovers do impact on the long-run component of exchange rate volatility the Australian Dollar plays a pivotal role in the localised causality transmission mechanism.

Stock Returns and Volatility: Pricing the Short-Run and Long-Run Components of Market Risk

We decompose the time series of equity market risk into short- and long-run volatility components. Both components have negative and highly significant prices of risk in the cross section of equity returns. A three-factor model with the market return and the two volatility components compares favorably to benchmark models. We show that the short-run component captures market skewness risk, while the long-run component captures business cycle risk. Furthermore, short-run volatility is the more important cross-sectional risk factor, even though its average risk premium is smaller than the premium of the long-run component.