The relationship between stock returns and volatility in international stock markets

This study examines the relationship between expected stock returns and volatility in the 12 largest international stock markets during January 1980 to December 2001. Consistent with most previous studies, we find a positive but insignificant relationship during the sample period for the majority of the markets based on parametric EGARCH-M models. However, using a flexible semiparametric specification of conditional variance, we find evidence of a significant negative relationship between expected returns and volatility in 6 out of the 12 markets. The results lend some support to the recent claim [Bekaert, G., Wu, G., 2000. Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1–42; Whitelaw, R., 2000. Stock market risk and return: an empirical equilibrium approach. Review of Financial Studies 13, 521–547] that stock market returns are negatively correlated with stock market volatility.     

Volatility Forecasting in the Hang Seng Index using the GARCH Approach

The aim of this paper is to add to the literature on volatility forecasting using data from the Hong Kong stock market to determine if forecasts from GARCH based models can outperform simple historical averaging models. Overall, unlike previous studies we find that the GARCH models with non-Normal distributions show a robust volatility forecasting performance in comparison to the historical models. The results indicate that although not all models outperform simple historical averaging, the EGARCH based models, with non-normal conditional volatility, tend to produce more accurate out-of-sample forecasts using both standard measures of forecast accuracy and financial loss functions. In addition we test for asymmetric adjustment in the Hang Seng, finding strong evidence of asymmetries due to the domination of financial and property firms in this market.     

基于CDM机制的国际碳排放权贸易价格波动性研究

运用规范分析和基于GARCH模型族的实证分析发现:清洁发展机制(CDM)下国际碳排放权核证减排单位(CERs)市场的价格波动是政治博弈、国际经济形势等多重因素共同冲击的结果。其价格波动呈现时变性、聚集性和持续性特征,市场"杠杆效应"明显。我国作为全球CDM项目的最大供给方,为了掌握定价权,必须寻求CERs市场价格波动规律、积极参与国际气候环境条款的谈判、选择恰当贸易时机和建立CDM项目风险管理体系。     

Do structural breaks in volatility cause spurious volatility transmission?

We show through extensive Monte Carlo simulations that structural breaks in volatility (volatility shifts) across two independently generated return series cause spurious volatility transmission when estimated with popular bivariate GARCH models. However, using a dummy variable for the induced volatility shift virtually eliminates this bias. We also show that structural breaks in volatility have a substantial impact on the estimated hedge ratios. We confirm our simulation findings using the US stock market data.     

Empirical analysis of real and financial volatilities on stock excess returns: evidence from Taiwan industrial data

This paper tests the relation between stock excess returns and risk factors measured by volatility. The sources of the volatility are based on the volatility of macroeconomic factors and time-series volatility. To model the macroeconomic fundamentals, we divide the risk into real and financial volatilities pertinent to Taiwan's economic environment. By examining the data of indusry excess returns and market excess returns, we find evidence to reject the hypothesis that the stock excess returns are independent of the real and financial volatilities.     

A multivariate stochastic volatility model with applications in the foreign exchange market

The main objective of this paper is to study the behavior of a daily calibration of a multivariate stochastic volatility model, namely the principal component stochastic volatility (PCSV) model, to market data of plain vanilla options on foreign exchange rates. To this end, a general setting describing a foreign exchange market is introduced. Two adequate models—PCSV and a simpler multivariate Heston model—are adjusted to suit the foreign exchange setting. For both models, characteristic functions are found which allow for an almost instantaneous calculation of option prices using Fourier techniques. After presenting the general calibration procedure, both the multivariate Heston and the PCSV models are calibrated to a time series of option data on three exchange rates—USD-SEK, EUR-SEK, and EUR-USD—spanning more than 11 years. Finally, the benefits of the PCSV model which we find to be superior to the multivariate extension of the Heston model in replicating the dynamics of these options are highlighted.     

Asian Pacific Stock Market Volatility Modeling and Value at Risk Analysis

The potential for stock market growth in Asian Pacific countries has attracted foreign investors. However, higher growth rates come with higher risk. We apply value at risk (VaR) analysis to measure and analyze stock market index risks in Asian Pacific countries, exposing and detailing both the unique risks and system risks embedded in those markets. To implement the VaR measure, it is necessary to perform "volatility modeling" by mixture switch, exponentially weighted moving average (EWMA), or generalized autoregressive conditional heteroskedasticity (GARCH) models. After estimating the volatility parameters, we can calibrate the VaR values of individual and system risks. Empirically, we find that, on average, Indonesia and Korea exhibit the highest VaRs and VaR sensitivity, and currently, Australia exhibits relatively low values. Taiwan is liable to be in high-state volatility. In addition, the Kupiec test indicates that the mixture switch VaR is superior to delta normal VaR; the quadratic probability score (QPS) shows that the EWMA is inclined to underestimate the VaR for a single series, and GARCH shows no difference from GARCH t and GARCH generalized error distribution (GED) for a multivariate VaR estimate with more assets.     

Volatility Clustering, Asymmetry and Hysteresis in Stock Returns: International Evidence

Encompassing a very broad family of ARCH-GARCH models, we show that the AT-GARCH (1,1) model, where volatility rises more in response to bad newsthan to good news, and where news are considered bad only below a certain level, is a remarkably robust representation of worldwide stock market returns. The residual structure is then captured by extending ATGARCH (1,1) to an hysteresis model, HGARCH, where we modelstructured memory effects from past innovations. Obviously, this feature relates to the psychology of the markets and the way traders process information. For the French stock market we show that votalitity is affected differently, depending on the recent past being characterized by returns all above or below a certain level. In the same way a longer term trend may also influence volatility. It is found that bad news are discounted very quickly in volatility, this effect being reinforced when it comes after a negative trend in the stock index. On the opposite, good news have a very small impact on volatility except when they are clustered over a few days, which in this case reduces volatility.     

The Effect of Futures Market Volume on Spot Market Volatility

There has been considerable interest, both academic and regulatory, in the hypothesis that the higher is the volume in the futures market, the greater is the destabilizing effect on the stock market. We show that conventional approaches, such as adding exogenous variables to GARCH models, may lead to false inferences in tests of this question. Using a stochastic volatility model, we show that, contrary to regulatory concern and the results of other papers, contemporaneous informationless futures market trading has no significant effect on spot market volatility.     

Global investigation on the country-level idiosyncratic volatility and its determinants

Adapting the Fama–French three-factor model to a global context, this paper investigates idiosyncratic volatility as a measure of country-specific risk, and explores its determinants by using the equity and risk data of 47 developed and emerging countries during the period 1995–2016. We find the stock market turnover to have a positive and significant impact on the country-level idiosyncratic volatility, while information disclosure and investor uncertainty avoidance degree are negatively associated with country-level idiosyncratic risk. Moreover, improvements in economic, financial, and political risks, as measured by GDP growth, FX stability, foreign debt health, and non-corruption degree decrease the country-level idiosyncratic volatility significantly. Among all sets of market structure, investor preference, and economic, financial, and political risk variables considered, we find financial risk factors, FX stability and foreign debt health, to have the highest explanatory power over the cross-sectional differences in country-level idiosyncratic risk.     

The Effects of Purchases of Mortgages and Securitization by Government Sponsored Enterprises on Mortgage Yield Spreads and Volatility

In this paper, we investigate the effects of GSE (government sponsored enterprise) activities on mortgage yield spreads and volatility. Using various regression procedures (i.e., vector error correction (VEC) and GARCH models) and controlling for default and prepayment risk, we find that securitizations and purchases of mortgages by GSEs reduce mortgage yield spreads and volatility. In particular, we find that the yield spread between conforming and 10-year constant maturity treasury (CMT) rates decreases by 8.0 bp per $1billion increase in the level of GSE securitizations. Similarly, if GSEs increase mortgage purchases, the yield spread decreases 10.5 bp per $1billion increase of purchases. In addition, we hypothesize and find that GSE activities have a spillover effect to the non-conforming mortgage market; via investor substitutions, GSE purchases and securitizations of conforming loans reduce non-conforming loan rates. Thus, the measured influence of GSE activities is biased downward when measured using the spread of non-conforming loans over conforming loan rates. We also find that purchases of mortgages by GSEs significantly reduce mortgage yield volatility. In sum, our findings show that GSE activities reduce and stabilize mortgage market rates.     

Regulation Change and Volatility Spillovers: Evidence from China's Stock Markets

This paper investigates the structural changes of volatility spillovers between Chinese A-share and B-share markets induced by a regulation change on February 19, 2001, that allowed Chinese domestic investors to trade in the B-share market. The empirical results of the study, using high-frequency intraday data collected from a sample of seventy-eight firms issuing both A-shares and B-shares and employing a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model, show that after the regulation change, the volatility in A-shares increases the volatility in B-shares, thus increasing the risk of the whole market, whereas the latter reduces the former, thus reducing the risk of the whole market. A further investigation of the determinants influencing these structural changes shows that the following factors can encourage structural changes that reduce overall market risk: government ownership, institutional ownership, firm size, B-share proportion, and market-to-book ratio. Conversely, the following factors can encourage structural changes that increase overall market risk: dual roles of chief executive officer and chairman and the joint effect of firm size and B-share proportion.     

Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach

Volatility is an important element for various financial instruments owing to its ability to measure the risk and reward value of a given financial asset. Owing to its importance, forecasting volatility has become a critical task in financial forecasting. In this paper, we propose a suite of hybrid models for forecasting volatility of crude oil under different forecasting horizons. Specifically, we combine the parameters of generalized autoregressive conditional heteroscedasticity (GARCH) and Glosten–Jagannathan–Runkle (GJR)-GARCH with long short-term memory (LSTM) to create three new forecasting models named GARCH–LSTM, GJR-LSTM, and GARCH-GJRGARCH LSTM in order to forecast crude oil volatility of West Texas Intermediate on different forecasting horizons and compare their performance with the classical volatility forecasting models. Specifically, we compare the performances against existing methodologies of forecasting volatility such as GARCH and found that the proposed hybrid models improve upon the forecasting accuracy of Crude Oil: West Texas Intermediate under various forecasting horizons and perform better than GARCH and GJR-GARCH, with GG–LSTM performing the best of the three proposed models at 7-, 14-, and 21-day-ahead forecasts in terms of heteroscedasticity-adjusted mean square error and heteroscedasticity-adjusted mean absolute error, with significance testing conducted through the model confidence set showing that GG–LSTM is a strong contender for forecasting crude oil volatility under different forecasting regimes and rolling-window schemes. The contribution of the paper is that it enhances the forecasting ability of crude oil futures volatility, which is essential for trading, hedging, and purposes of arbitrage, and that the proposed model dwells upon existing literature and enhances the forecasting accuracy of crude oil volatility by fusing a neural network model with multiple econometric models.     

Volatility Transmission in the Real Estate Spot and Forward Markets

How shocks in one market influence the returns and volatility of other markets has been an important question for portfolio managers. In the finance literature, many studies found evidence of volatility spillovers across international markets, as well as between spot and futures markets. Although real estate is often regarded as a good vehicle for diversification, the dynamics of its volatility transmission have been largely ignored. This paper provides the first study to examine volatility spillovers between the spot and forward (pre-sale) index returns of the Hong Kong real estate market through a bivariate GARCH model. Transaction-based indices were used so that our volatility modelling was free from any smoothing problem. Our results showed that real estate returns exhibited volatility clustering, and the volatility of the forward market was more sensitive to shocks than the spot market. Moreover, volatility was mainly transmitted from the forward market to the spot market, but not vice versa.

Evaluation of Black-Scholes and GARCH Models Using Currency Call Options Data

This paper empirically examines the performance of Black-Scholes and Garch-M call option pricing models using call options data for British Pounds, Swiss Francs and Japanese Yen. The daily exchange rates exhibit an overwhelming presence of volatility clustering, suggesting that a richer model with ARCH/GARCH effects might have a better fit with actual prices. We perform dominant tests and calculate average percent mean squared errors of model prices. Our findings indicate that the Black-Scholes model outperforms the GARCH models. An implication of this result is that participants in the currency call options market do not seem to price volatility clusters in the underlying process.     

Forecasting Intraday Volatility and Value-at-Risk with High-Frequency Data

In this paper, we develop modeling tools to forecast Value-at-Risk and volatility with investment horizons of less than one day. We quantify the market risk based on the study at a 30-min time horizon using modified GARCH models. The evaluation of intraday market risk can be useful to market participants (day traders and market makers) involved in frequent trading. As expected, the volatility features a significant intraday seasonality, which motivates us to include the intraday seasonal indexes in the GARCH models. We also incorporate realized variance (RV) and time-varying degrees of freedom in the GARCH models to capture more intraday information on the volatile market. The intrinsic tail risk index is introduced to assist with understanding the inherent risk level in each trading time interval. The proposed models are evaluated based on their forecasting performance of one-period-ahead volatility and Intraday Value-at-Risk (IVaR) with application to the 30 constituent stocks. We find that models with seasonal indexes generally outperform those without; RV can improve the out-of-sample forecasts of IVaR; student GARCH models with time-varying degrees of freedom perform best at 0.5 and 1 % IVaR, while normal GARCH models excel for 2.5 and 5 % IVaR. The results show that RV and seasonal indexes are useful to forecasting intraday volatility and Intraday VaR.     

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

We explore the cross‐sectional pricing of volatility risk by decomposing equity market volatility into short‐ and long‐run components. Our finding that prices of risk are negative and significant for both volatility components implies that investors pay for insurance against increases in volatility, even if those increases have little persistence. The short‐run component captures market skewness risk, which we interpret as a measure of the tightness of financial constraints. The long‐run component relates to business cycle risk. Furthermore, a three‐factor pricing model with the market return and the two volatility components compares favorably to benchmark models.     

基于不同分布假设下波动模型估计效果比较——以上海股票市场为例

本文基于不同分布假设,即正态分布、Student-t分布以及EGB2分布,使用2005年1月4日至2011年6月30日上证综指日收益率数据对GARCH模型和GJR GARCH模型估计效果进行实证比较。实证结果显示：（1）基于非对称EGB2分布的GJR GARCH模型更适合中国证券市场;（2）中国股票市场存在波动不对称性,且好消息引发的波动大于坏消息引发的波动,这可能与中国股票市场特有的市场结构和交易制度有关;（3）波动的不对称特性可能部分来自于对分布偏度特性考虑的欠缺,验证了合理的分布假设在波动行为分析过程中的重要性。     

The impact of FIFA’s official announcements on the stock market of Qatar: The case of the 2022 World Cup

This study examines the impact of the FIFA’s official announcements on Doha Stock Exchange (DSE) of Qatar with respect to the 2022 World Cup. Using the abnormal unsystematic volatility method of Hilliard and Savickas (2002), our empirical findings reveal that the DSE market is sensitive to FIFA’s announcements about the 2022 World Cup. We find that four out of six FIFA announcements act as primary drivers to the DSE market volatility. The significant reactions of the DSE market to these announcements unveil the investors’ sentiments about the fate of the governmental and private expenditures on medium- and long-term projects undertaken in anticipation of hosting the 2022 World Cup. The results have some implications to investors in this newly emerging market related to this global sporting event. Any future announcements, good or bad, are likely to impact share prices in DSE market and trigger portfolio reallocation by local and international investors, leading to increased volatility.     

Algorithmic market making for options

In this article, we tackle the problem of a market maker in charge of a book of options on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic optimal control problem of an option market maker is in fact tractable. More precisely, when volatility is modeled using a classical stochastic volatility model—e.g. the Heston model—the problem faced by an option market maker is characterized by a low-dimensional functional equation that can be solved numerically using a Euler scheme along with interpolation techniques, even for large portfolios. In order to illustrate our findings, numerical examples are provided.