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Journal articles on the topic 'Volatility predictability'

Author: Grafiati

Published: 8 May 2023

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1

Ghosh, Bikramaditya, Krishna M.C., Shrikanth Rao, Emira Kozarević, and Rahul Kumar Pandey. "Predictability and herding of bourse volatility: an econophysics analogue." Investment Management and Financial Innovations 15, no. 2 (June 25, 2018): 317–26. http://dx.doi.org/10.21511/imfi.15(2).2018.28.

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Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation.

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2

CAO, MELANIE. "EFFECTS OF RETURN PREDICTABILITY ON OPTION PRICES WITH STOCHASTIC VOLATILITY FOR THE MARKET PORTFOLIO." Annals of Financial Economics 01, no. 01 (June 2005): 0550005. http://dx.doi.org/10.1142/s2010495205500053.

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I examine the effects of return predictability on option prices for the market portfolio in the presence of stochastic volatility and/or stochastic interest rates. The analysis is implemented in an equilibrium framework where a consistent option pricing model is derived with the return predictability and stochastic volatility and the precise link between the actual and the risk neutral measures is endogenized. The equilibrium analysis indicates that the return predictability is induced by the mean-reverting and heteroskedastic features of aggregate dividends. It is shown that risk-neutral option pricing model with the stochastic volatility and/or stochastic interest rates can be consistent with return predictability. Numerical results suggest that (i) models with either perfect predictability or no predictability will significantly overprice long-term options across different strike prices when the return of the underlying exhibits modest predictability; (ii) the stochastic volatility does not affect option prices in a significant way when asset return predictability is properly reflected in the actual stock price process; (iii) when return predictability is correctly specified, the effects of stochastic interest rates are not uniform.

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3

Dichev, Ilia D., and Vicki Wei Tang. "Earnings volatility and earnings predictability." Journal of Accounting and Economics 47, no. 1-2 (March 2009): 160–81. http://dx.doi.org/10.1016/j.jacceco.2008.09.005.

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4

Dai, Zhifeng, Huiting Zhou, Xiaodi Dong, and Jie Kang. "Forecasting Stock Market Volatility: A Combination Approach." Discrete Dynamics in Nature and Society 2020 (June 5, 2020): 1–9. http://dx.doi.org/10.1155/2020/1428628.

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We find that combining two important predictors, stock market implied volatility and oil volatility, can improve the predictability of stock return volatility. We also document that the stock market implied volatility provides far more significant predictability than the oil volatility and other nonoil macroeconomic and financial variables. The empirical results show the “kitchen sink” combination approach that using two predictors jointly performs better than not only the univariate regression models which use oil volatility or stock market implied volatility separately but also convex combination of the individual forecasts. This improvement of predictability is also remarkable when we consider the business cycle. Furthermore, the robust test based on different lag lengths and different macroinformation shows that our forecasting strategy is efficient.

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Kim, Jungmu, and Yuen Jung Park. "Predictability of OTC Option Volatility for Future Stock Volatility." Sustainability 12, no. 12 (June 25, 2020): 5200. http://dx.doi.org/10.3390/su12125200.

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This study explores the information content of the implied volatility inferred from stock index options in the over-the-counter (OTC) market, which has rarely been studied in the literature. Using OTC calls, puts, and straddles on the KOSPI 200 index, we find that implied volatility generally outperforms historical volatility in predicting future realized volatility, although it is not an unbiased estimator. The results are more apparent for options with shorter maturity. However, while implied volatility has strong predictability during normal periods, historical volatility is superior to implied volatility during a period of crisis due to the liquidity contraction of the OTC options market. This finding suggests that the OTC options market can play a role in conveying important information to predict future volatility.

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Nguyen, Tristan, and Alexander Schüßler. "Anomalien auf Aktienmärkten." Der Betriebswirt: Volume 54, Issue 2 54, no. 2 (June 30, 2013): 26–30. http://dx.doi.org/10.3790/dbw.54.2.26.

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In diesem Beitrag werden Anomalien (Puzzles) vorgestellt, die sich auf den gesamten Aktienmarkt beziehen. Equity Premium Puzzle steht für die zu hohe empirisch beobachtete Marktrisikoprämie. Sie kann nicht mit den Präferenzen der Erwartungsnutzentheorie erklärt werden. Volatility Puzzle bezeichnet die erhöhte Volatilität von Aktien. Diese schwanken zu stark, als dass sie den von rationalen Investoren diskontierten Wert erwarteter Dividenden widerspiegeln könnten. Predictability Puzzle beschreibt, dass gewisse Indikatoren die Preisentwicklung auf Marktebene vorhersagen. Für diese Anomalien werden verhaltenswissenschaftliche Erklärungen angeführt. This paper presents aggregate market anomalies. Equity Premium Puzzle means that the historical equity premium is too high to be explained by preferences of expected ultility theory. According to Volatility Puzzle, stocks move too much to be justified by dividend movements. Predictibility Puzzle describes that there are several ratios that predict aggregate market performance. We give behavioral explanations for those anomalies. Keywords: volatility puzzle, prospect theory, predictability puzzle, equity premium puzzle

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7

Christopherson, Jon A., and Andrew L. Turner. "Volatility and predictability of manager alpha." Journal of Portfolio Management 18, no. 1 (October 31, 1991): 5–12. http://dx.doi.org/10.3905/jpm.1991.409388.

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8

Raunig, Burkhard. "The predictability of exchange rate volatility." Economics Letters 98, no. 2 (February 2008): 220–28. http://dx.doi.org/10.1016/j.econlet.2007.04.035.

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9

Mavrides, Marios. "Predictability and volatility of stock returns." Managerial Finance 29, no. 8 (September 2003): 46–56. http://dx.doi.org/10.1108/03074350310768427.

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10

Li, Xingyi, and Valeriy Zakamulin. "The term structure of volatility predictability." International Journal of Forecasting 36, no. 2 (April 2020): 723–37. http://dx.doi.org/10.1016/j.ijforecast.2019.08.010.

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11

Pathak, Rajesh, and Amarnath Mitra. "PREDICTABILITY AND PREDICTORS OF VOLATILITY SMIRK: A STUDY ON INDEX OPTIONS." Business: Theory and Practice 18 (May 3, 2017): 64–70. http://dx.doi.org/10.3846/btp.2017.007.

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The purpose of this study is to examine the presence of volatility smirk anomaly in index options and its predictability for future returns. The study tests the temporal properties of volatility smirk and further explores the factors determining the anomaly. The daily volatility smirk is computedfor the period 2004–2014 and the first lag of smirk is used in generalized least square (GLS) estimation framework, with set of control variables in two different specifications, to test the predictability as well as the determinants of volatility smirk. The study reports significant presence of volatility smirk in index options with an auto–regressive structure. Smirk predicts marginal returns and the predictability is robust to the control of major risk factors. It is also found that open interest of calls and puts, along with market risk premium and momentum premium, act as significant predictor of volatility smirk. The results are helpful in enhancing returns on investment in Index based funds and designing options strategies from the perspective of volatility risk. The study is first of its kind in the Indian market examining the reasons and consequences of existence of volatility smirk in index options.

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12

Xiao, Jihong, and Yudong Wang. "Good oil volatility, bad oil volatility, and stock return predictability." International Review of Economics & Finance 80 (July 2022): 953–66. http://dx.doi.org/10.1016/j.iref.2022.03.013.

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13

Catania, Leopoldo, and Mads Sandholdt. "Bitcoin at High Frequency." Journal of Risk and Financial Management 12, no. 1 (February 15, 2019): 36. http://dx.doi.org/10.3390/jrfm12010036.

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This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon.

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Arfianto, Erman Denny, and Ivan Irawan. "Short Horizon Return Predictability di Pasar Modal Indonesia." Jurnal Pasar Modal dan Bisnis 1, no. 1 (September 2, 2019): 41–54. http://dx.doi.org/10.37194/jpmb.v1i1.7.

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Purpose- This study aims to examine the effect of effective spread, price impact, trading volume, stock prices, and volatility of returns on the predictability of short-term returns (short horizon return predictability). Methods- This research offers a new approach perspective which is a market microstructure with intraday data to measure short horizon return predictability as an efficient market inversion. The sample in this study was 64 non-financial companies listed on the KOMPAS100 Index during October 2017-March 2018. Intraday data used using the 5-minute frequency obtained from Bloomberg. This study uses multiple linear regression analysis. Finding- This study found that price impact, trading volume, stock prices, and volatility have a positive impact on the predictability of long-term returns. This study also found that effective spread does not have a significant impact on the predictability of short-term returns.

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15

Marks, Joseph M., and David P. Simon. "Sector Option Implied Volatility Dynamics and Predictability." Journal of Derivatives 25, no. 2 (November 27, 2017): 22–42. http://dx.doi.org/10.3905/jod.2017.25.2.022.

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16

Mateus, Cesario, and Worawuth Konsilp. "Implied Idiosyncratic Volatility and Stock Return Predictability." Journal of Mathematical Finance 04, no. 05 (2014): 338–52. http://dx.doi.org/10.4236/jmf.2014.45032.

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17

Della Corte, Pasquale, Tarun Ramadorai, and Lucio Sarno. "Volatility risk premia and exchange rate predictability." Journal of Financial Economics 120, no. 1 (April 2016): 21–40. http://dx.doi.org/10.1016/j.jfineco.2016.02.015.

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18

Andrei, Daniel, and Michael Hasler. "Dynamic Attention Behavior Under Return Predictability." Management Science 66, no. 7 (July 2020): 2906–28. http://dx.doi.org/10.1287/mnsc.2019.3328.

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We investigate the dynamic problem of how much attention an investor should pay to news in order to learn about stock-return predictability and maximize expected lifetime utility. We show that the optimal amount of attention is U-shaped in the return predictor, increasing with both uncertainty and the magnitude of the predictive coefficient and decreasing with stock-return volatility. The optimal risky asset position exhibits a negative hedging demand that is hump shaped in the return predictor. Its magnitude is larger when uncertainty increases but smaller when stock-return volatility increases. We test and find empirical support for these theoretical predictions. This paper was accepted by Gustavo Manso, finance.

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19

Raggad, Bechir, and Elie Bouri. "Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests." Mathematics 11, no. 3 (January 18, 2023): 528. http://dx.doi.org/10.3390/math11030528.

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We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants.

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20

Kongsilp, Worawuth, and Cesario Mateus. "Volatility risk and stock return predictability on global financial crises." China Finance Review International 7, no. 1 (February 20, 2017): 33–66. http://dx.doi.org/10.1108/cfri-04-2016-0021.

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Purpose The purpose of this paper is to investigate the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis. Design/methodology/approach Using a broad sample of stock options traded on the American Stock Exchange and the Chicago Board Options Exchange from January 2001 to December 2010, the effect of different idiosyncratic volatility forecasting measures are examined on future stock returns in four different periods (Bear and Bull markets). Findings First, the authors find clear and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub-period both in Bear and Bull markets. Second, the cross-section firm-specific characteristics are important when it comes to stock returns forecasts, as the latter have mixed positive and negative effects on Bear and Bull markets. Third, the authors provide evidence that short selling constraints impact negatively on stock returns for only a Bull market and that liquidity is meaningless for both Bear and Bull markets after the recent financial crisis. Practical implications These results would be helpful to disclose more information on the best idiosyncratic volatility measure to be implemented in global financial crises. Originality/value This study empirically analyses the effect of different idiosyncratic volatility measures for a period that involves both the dotcom bubble and the recent financial crisis in four different periods (Bear and Bull markets) and contributes the existing literature on volatility measures, volatility risk and stock return predictability in global financial crises.

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21

Bonato, Matteo, Konstantinos Gkillas, Rangan Gupta, and Christian Pierdzioch. "Investor Happiness and Predictability of the Realized Volatility of Oil Price." Sustainability 12, no. 10 (May 25, 2020): 4309. http://dx.doi.org/10.3390/su12104309.

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We use the the heterogeneous autoregressive realized volatility (HAR-RV) model to analyze both in sample and out-of-sample whether a measure of investor happiness predicts the daily realized volatility of oil-price returns, where we use high-frequency intraday data to measure realized volatility. Full-sample estimates reveal that realized volatility is significantly negatively linked to investor happiness at a short forecast horizon. Similarly, out-of-sample results indicate that investor happiness significantly improves the accuracy of forecasts of realized volatility at a short forecast horizon. Results for a medium and a long forecast horizon are insignificant. We argue that our results shed light on the role played by speculation in oil products and the potential function of oil-related products as a hedge against risks in traditional financial assets.

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22

Feng, Jiabao, Yudong Wang, and Libo Yin. "Oil volatility risk and stock market volatility predictability: Evidence from G7 countries." Energy Economics 68 (October 2017): 240–54. http://dx.doi.org/10.1016/j.eneco.2017.09.023.

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23

Jha, Kislay Kumar, and Dirk G. Baur. "Regime-Dependent Good and Bad Volatility of Bitcoin." Journal of Risk and Financial Management 13, no. 12 (December 7, 2020): 312. http://dx.doi.org/10.3390/jrfm13120312.

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This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.

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24

Cakici, Nusret, Kudret Topyan, and Chia-Jane Wang. "Cross-Sectional Return Predictability in Taiwan Stock Exchange: An Empirical Investigation." Review of Pacific Basin Financial Markets and Policies 17, no. 02 (June 2014): 1450010. http://dx.doi.org/10.1142/s0219091514500106.

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This paper provides an analysis of the effectiveness of certain return predictors in Taiwan Stock Exchange (TWSE) from January 1990 to December 2011 by employing both portfolio method and cross-sectional regressions. While we found no statistically significant predictive power of beta, total volatility, and idiosyncratic volatility the two cheapness variables, book-to-market (BKMT) and cash-flow-to-price (FPR) ratios showed strong consistent economically and statistically significant predictive powers. In addition, our multiple regressions found predictive power in total volatility, short-term reversal (STREV), and market capitalization in the set of small stocks, while our all stock set showed predictive power only in total volatility and STREV.

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25

Kumar, Rakesh. "Risk, uncertainty and stock returns predictability – a case of emerging equity markets." Journal of Financial Economic Policy 10, no. 4 (November 5, 2018): 438–55. http://dx.doi.org/10.1108/jfep-08-2017-0075.

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Purpose This paper aims to investigate the predictability of stock returns under risk and uncertainty of a set of 11 emerging equity markets (EEMs) during the pre- and post-crash periods. Design/methodology/approach Listed indices are considered to serve the proxy of stock markets with a structural break in data for the period: 2000-2014. As preliminary results highlight the significant autocorrelations in stock returns, Threshold-GARCH (1,1) model is used to estimate the conditional volatility, which is further decomposed into expected and unexpected volatility. Findings Results highlight that the volatility has symmetric impact on stock returns during the pre-crash period and asymmetric impact during the post-crash period. While testing the relationship of stock returns, a significant positive (negative) relationship is found with expected volatility during the pre-crash (post-crash) periods. The stock returns are found positively related to unexpected volatility. Research limitations/implications Business, political and other market conditions of sample stock markets are fundamentally different. These economies were liberalized in different years, which may affect the degree of integration with international equity markets. Practical implications The findings highlight that investors consider the impact of expected volatility in forecasting of stock returns during the growth period. They realize returns in commensurate to risk of their portfolios. However, they significantly reduce their investments in response to expected volatility during the recession period. The positive relationship between stock returns and unexpected volatility highlights the fact that investors realize extra returns for exposing their portfolios to unexpected volatility. Originality/value Pioneer efforts are made by using T-GARCH (1,1) procedure to analyse the problem. Given the emergence of emerging equity markets, new insight in dynamics of stock returns provide interesting findings for portfolio diversification under risk and uncertainty.

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Fu, Xi, Y. Eser Arisoy, Mark B. Shackleton, and Mehmet Umutlu. "Option-Implied Volatility Measures and Stock Return Predictability." Journal of Derivatives 24, no. 1 (August 31, 2016): 58–78. http://dx.doi.org/10.3905/jod.2016.24.1.058.

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27

Do Nguyet, Anh. "The Impact of Earnings Volatility on Earnings Predictability." GLOBAL BUSINESS FINANCE REVIEW 22, no. 2 (June 30, 2017): 82–89. http://dx.doi.org/10.17549/gbfr.2017.22.2.82.

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Li, Xingyi, and Valeriy Zakamulin. "Stock volatility predictability in bull and bear markets." Quantitative Finance 20, no. 7 (April 7, 2020): 1149–67. http://dx.doi.org/10.1080/14697688.2020.1725101.

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29

Dakka, Yadollah. "The Relationship Between Volatility and Predictability of Profit." Journal of Finance and Accounting 3, no. 4 (2015): 69. http://dx.doi.org/10.11648/j.jfa.20150304.12.

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Chen, Jian, Fuwei Jiang, Yangshu Liu, and Jun Tu. "International volatility risk and Chinese stock return predictability." Journal of International Money and Finance 70 (February 2017): 183–203. http://dx.doi.org/10.1016/j.jimonfin.2016.08.007.

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31

Ma, Yao, Baochen Yang, and Yunpeng Su. "Technical trading index, return predictability and idiosyncratic volatility." International Review of Economics & Finance 69 (September 2020): 879–900. http://dx.doi.org/10.1016/j.iref.2020.07.006.

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32

Ornelas, José Renato Haas, and Roberto Baltieri Mauad. "Implied volatility term structure and exchange rate predictability." International Journal of Forecasting 35, no. 4 (October 2019): 1800–1813. http://dx.doi.org/10.1016/j.ijforecast.2019.03.016.

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33

Makina, Daniel. "Mean reversion and predictability of remittances." International Journal of Social Economics 41, no. 12 (November 25, 2014): 1209–19. http://dx.doi.org/10.1108/ijse-02-2014-0038.

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Purpose – The purpose of this paper is to examine the predictability of remittances in individual developing countries. It achieves this objective by testing for mean reversion (i.e. stationarity) in the monthly remittance series reported to the World Bank by 21 developing countries. Design/methodology/approach – Unit root tests on remittance time series are undertaken using three tests – the augmented Dickey-Fuller test, the Phillip-Peron test and the Kwiatkowshi, Phillips, Schmidt and Shin test. Stationarity of series in levels would indicate mean reversion and predictability of remittances. Findings – The paper finds significant evidence of mean reversion and hence predictability in remittance inflows in 17 developing countries. Practical implications – Remittance inflows, which have become an important source of external finance for many developing countries, are not random flows but a stable and predictable stream of financial flows. Originality/value – Prior research has focused on volatility of remittances in comparison with other capital flows and then inferred stability from them having lower volatility. Using available monthly data, this paper is the first to directly test for mean reversion and hence predictability of remittances.

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Chang, Chia-Lin, Jukka Ilomäki, Hannu Laurila, and Michael McAleer. "Long Run Returns Predictability and Volatility with Moving Averages." Risks 6, no. 4 (September 22, 2018): 105. http://dx.doi.org/10.3390/risks6040105.

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This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.

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Dai, Zhifeng, Huiting Zhou, Fenghua Wen, and Shaoyi He. "Efficient predictability of stock return volatility: The role of stock market implied volatility." North American Journal of Economics and Finance 52 (April 2020): 101174. http://dx.doi.org/10.1016/j.najef.2020.101174.

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Rangvid, Jesper, Maik Schmeling, and Andreas Schrimpf. "Dividend Predictability Around the World." Journal of Financial and Quantitative Analysis 49, no. 5-6 (August 22, 2014): 1255–77. http://dx.doi.org/10.1017/s0022109014000477.

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AbstractWe show that dividend-growth predictability by the dividend yield is the rule rather than the exception in global equity markets. Dividend predictability is weaker, however, in large and developed markets where dividends are smoothed more, the typical firm is large, and volatility is lower. Our findings suggest that the apparent lack of dividend predictability in the United States does not uniformly extend to other countries. Rather, cross-country patterns in dividend predictability are driven by differences in firm characteristics and the extent to which dividends are smoothed.

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FONG, WAI MUN, and WING-KEUNG WONG. "THE STOCHASTIC COMPONENT OF REALIZED VOLATILITY." Annals of Financial Economics 02, no. 01 (June 2006): 0650004. http://dx.doi.org/10.1142/s2010495206500047.

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Volatility–volume regressions provide a convenient framework to study sources of volatility predictability. We apply this approach to the daily realized volatility of common stocks. We find that unexpected volume plays a more significant role in explaining realized volatility than expected volume, and accounts for about one-third of the non-persistent component in the volatility process. Contrary to the findings of Lamoureux and Lastrapes (1990), the ARCH effect is robust even in the presence of volume. However, this component explains only about half of the variations in realized volatility. Thus, large portion of realized volatility is clearly stochastic. This presents a significant challenge to the goal of achieving precise realized volatility forecasts.

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Solibakke, Per Bjarte. "Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities." Journal of Risk and Financial Management 14, no. 11 (October 22, 2021): 510. http://dx.doi.org/10.3390/jrfm14110510.

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This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011–2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion for the market (differences) and observed data dependence important for market participants, implying predictability. The paper estimates multifactor stochastic volatility models for both contracts giving access to a long-simulated realization of the state vector with associated contract movements. The realization establishes a functional form of the conditional distributions, which are evaluated on observed data giving the conditional mean function for the volatility factors at the data points (nonlinear Kalman filter). For both Brent and WTI oil contracts, the first factor is a slow-moving persistent factor while the second factor is a fast-moving immediate mean reverting factor. The negative correlation between the mean and volatility suggests higher volatilities from negative price movements. The results indicate that holding volatility as an asset of its own is insurance against market crashes as well as being an excellent diversification instrument. Furthermore, the volatility data dependence is strong, indicating predictability. Hence, using the Kalman filter from a realization of an optimal multifactor SV model visualizes the latent step ahead volatility paths, and the data dependence gives access to accurate static forecasts. The results extend market transparency and make it easier to implement risk management including derivative trading (including swaps).

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Liu, Zhaohua, Susheng Wang, Siyi Liu, Haixu Yu, and He Wang. "Volatility Risk Premium, Return Predictability, and ESG Sentiment: Evidence from China’s Spots and Options’ Markets." Complexity 2022 (October 3, 2022): 1–14. http://dx.doi.org/10.1155/2022/6813797.

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This study investigates the volatility risk premium on the emerging financial market. We also consider the expected return and ESG sentiment. Based on the SSE 50 ETF 5-minute high-frequency spots and daily options data from 2016 to 2021, we adopt nonparametric model-free approaches to calculate realized and implied volatilities. And the volatility risk premium is constructed by subtracting these volatility series. We examine the relations between the volatility risk premium and future excess returns as well as ESG sentiment through multifactor specifications. We find that the volatility risk premium also exists in the Chinese market and is significantly negative. In addition, the statistically positive correlation between the volatility risk premium and aggregate returns is an outlier compared to the empirically negative pattern in developed markets. At last, ESG sentiment is positively associated with the volatility risk premium, especially the impact of environmental and social. This evidence supports the agency theory, which indicates that investors perceive ESG investments as waste resources in a short term and become potentially risky.

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Hofer, Peter, Christoph Eisl, and Albert Mayr. "Forecasting in Austrian companies." Journal of Applied Accounting Research 16, no. 3 (November 9, 2015): 359–82. http://dx.doi.org/10.1108/jaar-10-2014-0113.

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Purpose – The purpose of this paper is a comparison of forecasting behaviour of small and large Austrian firms, analysing their forecast practices in a volatile business environment. Design/methodology/approach – The empirical analysis of the paper, deductive by nature, was conducted by means of a quantitative online-survey (199 data sets). The relationship of perceived volatility and forecast predictability was evaluated by correlation analysis. t-Test and analysis of variances were used to examine significant differences in the forecast characteristics between small and large Austrian companies and different industries. Findings – The study provides evidence that the surveyed companies have been hit by volatility, showing that Austrian SMEs are significantly more severely affected than large companies. The increasing volatility correlates with a reduced forecast predictability of sales quantities and commodity prices. Large Austrian companies primarily use a broad spectrum of qualitative forecasting methods. In contrast, Austrian SMEs utilize simple quantitative and qualitative forecast techniques, like the forward projection of historical data. Research limitations/implications – Relevant for the forecasting of small and large companies. Practical implications – Although management requests a broad spectrum of forecast qualities, the current usage of less sophisticated methods reveals a gap between intention and reality. Companies that supplement their qualitative techniques by sophisticated quantitative ones should expect less forecast bias. Originality/value – This paper initially compares forecast methods in large and small Austrian firms and additionally provides the impact of volatility on the forecast predictability.

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Borochkin, A. A. "Volatility and predictability of the Russian ruble exchange rate." Финансы и кредит 23, no. 5 (February 15, 2017): 274–91. http://dx.doi.org/10.24891/fc.23.5.274.

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Ma, Feng, Yangli Guo, Julien Chevallier, and Dengshi Huang. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability." International Review of Financial Analysis 84 (November 2022): 102339. http://dx.doi.org/10.1016/j.irfa.2022.102339.

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Lu, Fei, Feng Ma, Pan Li, and Dengshi Huang. "Natural gas volatility predictability in a data-rich world." International Review of Financial Analysis 83 (October 2022): 102218. http://dx.doi.org/10.1016/j.irfa.2022.102218.

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Liu, Zhichao, Jing Liu, Qing Zeng, and Lan Wu. "VIX and stock market volatility predictability: A new approach." Finance Research Letters 48 (August 2022): 102887. http://dx.doi.org/10.1016/j.frl.2022.102887.

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Campbell, Sean D. "Macroeconomic Volatility, Predictability, and Uncertainty in the Great Moderation." Journal of Business & Economic Statistics 25, no. 2 (April 2007): 191–200. http://dx.doi.org/10.1198/073500106000000558.

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D’Amico, Guglielmo, Fulvio Gismondi, Filippo Petroni, and Flavio Prattico. "Stock market daily volatility and information measures of predictability." Physica A: Statistical Mechanics and its Applications 518 (March 2019): 22–29. http://dx.doi.org/10.1016/j.physa.2018.11.049.

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Raggi, Davide, and Silvano Bordignon. "Volatility, Jumps, and Predictability of Returns: A Sequential Analysis." Econometric Reviews 30, no. 6 (November 2011): 669–95. http://dx.doi.org/10.1080/07474938.2011.553570.

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Bentes, Sonia R., and Rui Menezes. "On the predictability of realized volatility using feasible GLS." Journal of Asian Economics 28 (October 2013): 58–66. http://dx.doi.org/10.1016/j.asieco.2013.08.002.

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Raunig, Burkhard. "The longer-horizon predictability of German stock market volatility." International Journal of Forecasting 22, no. 2 (April 2006): 363–72. http://dx.doi.org/10.1016/j.ijforecast.2005.11.003.

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Kearney, Fearghal, Han Lin Shang, and Lisa Sheenan. "Implied volatility surface predictability: The case of commodity markets." Journal of Banking & Finance 108 (November 2019): 105657. http://dx.doi.org/10.1016/j.jbankfin.2019.105657.

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