Proxy For Volatility Research Articles

Modelling the Chinese crude oil futures returns through a skew‐geometric Brownian motion correlated with the market volatility index process for pricing financial options

AbstractIn this paper we model the dynamics of the Chinese crude oil futures returns by using a skew‐geometric Brownian motion correlated with the market volatility, which is taken as a square‐root stochastic process. We use the OVX index data as proxy for market volatility. We validate the proposed model in terms of accuracy of its calibrations through an in‐sample simulation. Instead, out‐of‐sample simulations are used to show that a correlated skew‐geometric Brownian motion is more appropriate for modelling the Chinese returns compared to a single skew‐geometric Brownian motion in terms of forecasts. Furthermore, we price an American call option on the Chinese futures by using a recursively scheme based on a closed‐form formula, and an alternative Monte Carlo approach, for the related European call option. We show that our call price estimates are very close to market values and our model generally outperforms many benchmarks in literature, such as the Barone‐Adesi and Whaley formula and its generalizations.

The nonparametric GARCH model estimation using intraday high-frequency data

Most of nonparametric GARCH models typically employ daily frequency data to forecast the returns, correlations, and risk indicators of financial assets, without incorporating alternative frequency data. As a result, valuable financial market information may remain underutilized during the estimation process. To partially mitigate this issue, we introduce the intraday high-frequency data to enhance the estimation of the volatility function in a nonparametric GARCH model. To achieve this objective, we introduce a nonparametric proxy model for volatility. Under mild assumptions, we derive the asymptotic bias and variance of the estimator and further investigate the impact of various volatility proxies on estimation accuracy. Our findings from both simulations and empirical analysis indicate a considerable improvement in the estimation of the volatility function through the introduction of high-frequency data.

PERSISTENCE IN EUROPEAN STOCK MARKET RETURNS AND VOLATILITY

This paper applies fractional integration techniques to analyze persistence in stock market returns and volatility at different frequencies in the case of various European indices, specifically the French CAC, the Spanish IBEX 35, the German DAX, the British FTSE 100, and the Euro Stoxx 50, over the period from January 2018 to January 2023.Returns are calculated as the first differences of the logged prices, while absolute and squared returns are used as volatility proxies. The method used yields estimates of the differencing parameter d, which is allowed to take any real value, including fractional ones, and represents a measure of persistence. This parameter provides information about whether the series of interest exhibit either long memory or mean reversion. Specifically, a value of d greater than 0 indicates the presence of long memory in the series, while a value less than 1 implies mean reversion, with the effects of shocks being transitory. The results vary depending on the data frequency. More precisely, evidence of long memory is found for returns at the daily frequency only and for volatility proxies at both the weekly and daily frequencies but not at the monthly frequency. In most cases our findings imply that stock prices are I(1) and thus follow a random walk, which is consistent with the efficiency markets hypothesis (EMH).

Sentiment and energy price volatility: A nonlinear high frequency analysis

This study investigates the volatility dynamics of oil and gas prices in an environment characterized by post-coronavirus disease 2019 recovery, uncertainty, high inflation, and geopolitical tensions. Unlike previous studies, we examine a long-run series of high-frequency data on gas and oil prices from July 2007 to May 2022, which provides more than one million observations with which to analyze volatility. We compute realized volatility (RV) and decompose it into continuous volatility and jumps. We then investigate the relationship between uncertainty, investor sentiment, and RV, as well as its main components. Econometrically, we extend the heterogeneous autoregressive model of Corsi (2009) while considering not only disaggregate proxies for volatility (jumps and continuous volatility) and introducing uncertainty and heterogeneous investor sentiment, but also by allowing the model to include asymmetry, nonlinearity, and time variation according to the regime under consideration. Our results present three main findings. First, we find significant evidence of volatility decomposition, suggesting that both markets are characterized by significant jumps. Second, we show that trading volume, extra-financial news (uncertainty, investor sentiment), and jumps appear to drive commodity price volatility. Third, we find evidence of nonlinearity and threshold effects on energy price volatility. These findings are relevant for policymakers, regulators, investors, and portfolio managers, as they enable them to better characterize and forecast changes in commodity prices.

Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures

AbstractIn recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility in the models to verify whether high‐frequency information can improve the joint prediction ability of risk measures. To strengthen the persuasion of conclusions, four international stock indices (S&P500, Nikkei225, GDAXI, and DJIA) are applied to these models to estimate and forecast VaR and ES at different probability levels (1%, 2.5%, 5%, and 10%). Then, the predicted VaR and ES are backtested by several methods individually, and the popular score function FZ0 and MCS test are used to compare the effects of jointly predicting risk measures. Our results confirm that these semiparametric models containing intraday information outperform the benchmark models for four stocks and various probability levels, and medRV is the best volatility measure in improving the effects of models.

Forecasting volatility of stock indices: Improved GARCH-type models through combined weighted volatility measure and weighted volatility indicators

This paper proposes an unbiased combined weighted (CW) volatility measure and weighted volatility indicators (WVI) that integrates the return- and range-based volatility measures to model the dynamics volatility of stock returns. The main feature of the CW measure is that it is formulated based on the weighted inter- and intra-price information to quantify the volatility directly, while the WVI effectively identifies signals on the shift of volatility. Empirical analysis using five stock indices demonstrates that the CW measure, utilising squared returns in combination with range-based Garman-Klass volatility measure, exhibits the lowest losses based on root mean squared error and quasi-likelihood when compared to 5-minute realised volatility as a proxy for true volatility. Furthermore, we investigate the feasibility of incorporating the CW measure and WVI as the exogenous variable(s) in the generalised autoregressive conditional heteroscedasticity (GARCH)-type models to enhance the forecasting performance. The findings indicate that the GARCH-CW-WVI and EGARCH-CW-WVI models exhibit superior in-sample model fit based on the Akaike information criterion than the existing GARCH and EGARCH models. Moreover, our proposed models also offer the best out-of-sample forecasts evaluated using various loss functions and further tested using Hansen’s model confidence set based on the mean squared error loss. Different risk levels of value-at-risk (VaR) and expected shortfall (ES) forecasts based on GARCH-CW-WVI and EGARCH-CW-WVI models are computed and examined with various backtests to confirm the accuracies of VaR and ES forecasts.

Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective

Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this work, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from the true volatility. We then establish non-asymptotic deviation bounds for three robust volatility proxies, two of which are based on clipped data, and the third of which is based on exponentially weighted Huber loss minimization. In particular, in order for the Huber approach to adapt to non-stationary financial returns, we propose to solve a tuning-free weighted Huber loss minimization problem to jointly estimate the volatility and the optimal robustification parameter at each time point. We then inflate this robustification parameter and use it to update the volatility proxy to achieve optimal balance between the bias and variance of the global empirical loss. We also extend this Huber method to construct volatility predictors. Finally, we exploit the proposed robust volatility proxy to compare different volatility predictors on the Bitcoin market data and calibrated synthetic data. It turns out that when the sample size is limited, applying the robust volatility proxy gives more consistent and stable evaluation of volatility forecasts.

Does oil price volatility matter for the US transportation industry?

Although the US transport sector is one of the major users of fossil fuel (e.g., crude oil), the impact of energy price volatility on transport stock sector indexes remains under-researched. The present study addresses this research void by investigating the impact of energy implied volatility on transportation stock returns in the US. Using the crude oil volatility index (OVX), as a proxy of energy price volatility, and three Dow Jones indexes tracking the performance of the airlines, marine and trucking stock subsectors, we employ a GARCH-jump model. The main results show that the oil market sends volatility to the US transport subsector stock indexes, suggesting that oil implied volatility plays a role in pricing US transport stocks. The impact of OVX shocks is asymmetric, indicating that increases and decreases in oil implied volatility have a heterogeneous impact on the transport subsector stock markets. Jumps are significant in the three transport subsector stock indexes, and are time-dependent. Notably, the three transportation subsector stock indexes are more sensitive to OVX shocks than the S&P 500 index. These results have important implications for investors, policymakers, academics, and managers of the US transportation industry.

The effect of board of commissioners size on risk management trends

This study aims to obtain empirical evidence of the effect of board size on risk management tendencies. The data collection technique used was a purposive sampling method in order to obtain a sample of 531 manufacturing sector companies listed on the Indonesia Stock Exchange in 2014-2017. The data analysis used is Multiple Linear Regression Analysis. The results of this study are that a large number of boards has no impact on the volatility of company stock returns. However, it is positively related to cash flow volatility proxies.

Market beta coefficient and enterprise risk management: A literature review

One of the significant factors in the valuation of publicly listed firms is their market beta coefficient, commonly utilised in the capital asset pricing model (CAPM) as a proxy for stock volatility directly affecting market value. This article’s primary purpose is to explore the theoretical basis for future empirical research into the relationship between the market beta coefficient and enterprise risk management (ERM). The author explores academic literature about various researched variables affecting the market beta coefficient in the context of the neoclassical capital asset pricing model, which was founded on the premise of an efficient market hypothesis.

A Real-Time GARCH-MIDAS model

This paper proposes the Real-Time GARCH-MIDAS model to model and forecast volatility. An empirical application to the Shanghai Stock Exchange Composite Index (SSEC) and Shenzhen Stock Exchange Component Index (SZSEC) of China shows that the Real-Time GARCH-MIDAS model outperforms competing models in terms of both empirical return fitting and out-of-sample volatility forecasting. Moreover, the superior forecasting performance of the Real-Time GARCH-MIDAS model is robust to alternative rolling windows, alternative benchmark models, alternative MIDAS lags and alternative volatility proxy. Further discussion illustrates the flexibility of the Real-Time GARCH-MIDAS model.

Daily Semiparametric GARCH Model Estimation Using Intraday High-Frequency Data

The GARCH model is one of the most influential models for characterizing and predicting fluctuations in economic and financial studies. However, most traditional GARCH models commonly use daily frequency data to predict the return, correlation, and risk indicator of financial assets, without taking data with other frequencies into account. Hence, financial market information may not be sufficiently applied to the estimation of GARCH-type models. To partially solve this problem, this paper introduces intraday high-frequency data to improve estimation of the volatility function of a semiparametric GARCH model. To achieve this objective, a semiparametric volatility proxy model was proposed, which includes both symmetric and asymmetric cases. Under mild conditions, the asymptotic normality of estimators was established. Furthermore, we also discuss the impact of different volatility proxies on estimation precision. Both the simulation and empirical results showed that estimation of the volatility function could be improved by the introduction of high-frequency data.

IS DEMUTUALIZATION A PANACEA TO SPUR INVESTORS’ PERCEPTION?

This study considers demutualization of Pakistan Stock Exchange (PSX)and how it has influenced investors perception. The investors perception and response to demutualization is assessed under two heads namely: volume of trade as proxy for liquidity, andmarket priceas proxy for volatility. The study is layered into: country level and firm level. Pakistan used to have three stock exchanges namely: Karachi Stock Exchange, Lahore Stock Exchange and Islamabad Stock Exchange. Following passing of the Demutualization Act in the year 2012, these three stock exchanges merged to form a single stock exchange named as Pakistan Stock Exchange abbreviated as PSX. The investors perception as reflected in the number of trades is assessed against country’s gross domestic product to measure demutualization impact on investment climate that prevailed in the country during the period 2014 to 2018 which is two years before and two years after demutualization. The year 2016 is the year when the process of demutualization was given final shape and being transitory period is ignored for the study. The explanatory variables used for the study include: turnover ratio, market capitalization, value of trades as percentage of GDP, and the control variables include size and age of firms included in the study. The study has revealed thatDemutualization of PSX has had statistically significant impact on liquidity and volatility.

국제 유가 변동성이 국내 휘발유 가격 비대칭성에 미치는 영향

Purpose - This study examines the effects of Dubai oil price and the volatility on the asymmetry of domestic gasoline price adjustment. Additionally, the study investigates the effects of “Altteul” gas-station and tax-cut policies on asymmetry.
 Design/methodology/approach - Firstly, the study calculates proxies for asymmetry and volatility of each window(every 3-month) by error-correction model and GARCH(1, 1) using daily domestic gas price and Dubai oil price from 2008/04/15 to 2022/12/31. Secondly, the study investigates the effects of the increasing rate of Dubai oil price, volatility, “Altteul” gas-station and tax-cut policies on asymmetry. The autoregressive distributed lag regression model is employed for estimations.
 Findings - The study finds that changes in the increasing rate of Dubai oil price and both types of volatility of Dubai oil price increase asymmetry. While “Altteul” gas-station and tax-cut policies decrease asymmetry. Additionally, the study fails to find that asymmetry in the Korean gasoline market in the estimation with total observations.
 Research implications or Originality - An increase in Dubai oil price volatility means an increase in cost uncertainty for gas-station owners. Since cost uncertainty is a kind of financial risk, the increase in volatility reinforces the asymmetry. The study provides supporting evidence for the idea.

Volatility Forecasting with Machine Learning and Intraday Commonality

Abstract We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting 1-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.

The impact of the Russian-Ukrainian war on global financial markets

On February 24, 2022, Russia invaded the Ukraine. In this paper, we analyze the response of European and global stock markets alongside a representative sample of commodities. We compare the war response against the recent Covid-19 pandemic and the not-too-distant 2008 global financial crisis. Applying a Markov-switching HAR model on volatility proxies, estimates are made of synchronization, duration and intensity measures for each event. In broad terms, stock markets and commodities respond most rapidly to the Russian invasion; and post-invasion crisis intensity is noticeably smaller compared to both the Covid-19 and the GFC. Wheat and nickel are the most affected commodities due to the prominent exporter status of the two countries.

Cash flow volatility and debt maturity structure: Evidence from the Gulf Cooperation Council countries

This study aims to investigate the impact of cash flow volatility on the debt maturity structure choices of corporations in the Gulf Cooperation Council (GCC) countries, a region with large gross domestic products (GDPs), negligible corporate taxes, and bank-based economies. The study uses a four-year rolling standard deviation of cash flows as a proxy for volatility and examines its impact on the use of long-term debt by applying the two-stage least square estimator. In addition, the study constructs a categorical debt maturity variable and applies the ordered probit regression to analyze the impact of volatility on the probability of having long-term debt. The findings of this study show that both the proportion of long-term debt relative to total debt and the probability of having long-term debt decrease significantly with volatility. These findings suggest that volatility limits GCC firms’ use of long-term borrowing which has implications for their private investments. Other findings indicate that firm size, asset tangibility, asset maturity, and leverage have a positive impact on debt maturity while growth opportunities have a negative impact, which suggests that GCC firms use short-term debt to reduce agency and liquidity costs.

Forecasting the High-Frequency Exchange Rate Volatility with Smooth Transition Exponential Smoothing

Smooth Transition Exponential Smoothing (STES) is a popular exponential smoothing method for volatility forecasting; whereby the success of the STES model lies in the choice of the transition variable. In this paper, three realized variance (RV), daily, weekly and monthly RV were used as the transition variables in STES methods to evaluate the performance of intraday data. While daily squared return is a noisy series, squared residual and daily RV were employed as the proxy for actual volatilities in this study. With five series of exchange rates, a comparative analysis was conducted for Ad Hoc methods, Generalised Autoregressive Conditional Heteroscedastic (GARCH) models, and STES methods using various RV combinations. The empirical results showed that when daily RV was used as proxy for actual volatility, the traditional STES models and STES models with RV as the transition variables outperformed Ad Hoc methods and GARCH models under the RMSE evaluation criteria. Similar promising results were also observed for traditional STES models and STES models with RV as the transition variables under MAE evaluation. The MCS results generally reaffirmed the results from both the MAE and RMSE evaluation criteria.

Impact of COVID-19 on financial returns: a spatial dynamic panel data model with random effects

Using a dataset including financial market returns and volatility proxies for several countries, we analyzed the impact of Covid-19 deaths on the financial economy. From the modeling perspective, we consider a spatial panel data model for returns and a spatial dynamic panel data for volatilities. Proper marginal effects are calculated to exploit information on short- and long-term effects. A Chow test is used to identify the existence of a structural break in each series. Our empirical evidence shows that in the first weeks of the Covid-19 outbreak, until mid-March 2020, the identified break date, the spatial effect of Covid-19 deaths was statistically significant, leading to a contraction in returns and an increase in risk. Moreover, the effects disappeared in the remaining months as the financial markets moved back to pre-crisis levels, causing a decrease in the overall risk. Our evidence supports the behavioral impact of the pandemic on financial markets.

The VolCo index: a measure of the transition from pandemic to equity market

ABSTRACT The COVID-19 has serious impacts on the economy and contaminates the equity market because its spread leads to enormous uncertainty, which can be measured by various volatilities from backward and forward information contents. We find that the pandemic-related data are significant for regressing various volatilities. Hence, we construct the index termed the Volatility of COVID-19 pandemic (VolCo) as a proxy of equity market volatility, including GJR-GARCH volatility, instantaneous volatility, VIX index and their combination. This VolCo index provides an explanation for the volatility of equity market in the period of COVID-19 spreads.