1. Do industries predict stock market volatility? Evidence from machine learning models. (2024). Demirer, Riza ; Niu, Zibo ; Zhu, Xuehong ; Suleman, Muhammad Tahir ; Zhang, Hongwei.
   In: Journal of International Financial Markets, Institutions and Money.
   RePEc:eee:intfin:v:90:y:2024:i:c:s1042443123001713.

   Full description at Econpapers || Download paper

1. Ang, A., & Chen, J. (2001). Asymmetric correlations of equity portfolios. Journal of Financial Economics, 63(3), 637– 654.
   Paper not yet in RePEc: Add citation now
   

2. Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH-MIDAS approach. Journal of Forecasting, 32, 600–612.

3. Ayub, U., Shah, S. Z. A., & Abbas, Q. (2015). Robust analysis for downside risk in portfolio management for a volatile stock market. Economic Modelling, 44, 86–96.

4. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645-1680.

5. Baker, S., Bloom, N., Davis, S., 2016. Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131, 1593–1636.

6. Bee, M., Dupuis, D. J., & Trapin, L. (2016). Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective. Journal of Empirical Finance, 36, 86–99.

7. Bekaert, G., Engstrom, E., Xu. N. (2022). The Time Variation in Risk Appetite and Uncertainty. Management Science, Vol. 68, No. 6, 3975–4004.

8. Bollerslev, T., Patton, A. J., & Quaedvlieg, R. (2018). Modeling and forecasting (un) reliable realized covariances for more reliable financial decisions. Journal of Econometrics, 207(1), 71-91.

9. Bolton, P. and Kacperczyk, M. 2021. Do investors care about carbon risk? Journal of Financial Economics 142, 517–549.

10. Brown, D. B., and Smith, J. E. (2011). Dynamic portfolio optimization with transaction costs: Heuristics and dual bounds. Management Science, 57(10), 1752-1770.

11. Callot, L.A., Kock, A. B., and Medeiros, M. C. (2017). Modeling and forecasting large realized covariance matrices and portfolio choice. Journal of Applied Econometrics, 32(1), 140-158.

12. Cederburg, S., O’Doherty, M. S., Wang, F., & Yan, X. (2020). On the performance of volatilitymanaged portfolios. Journal of Financial Economics, 138, 95–117.

13. Cepni, O., Demirer, R., Rognone, L. 2022. Hedging Climate Risks with Green Assets. Economics Letters 212, 110312.

14. Chen, E. T. J., and Clements, A. (2007). S&P 500 implied volatility and monetary policy announcements. Finance Research Letters, 4(4), 227-232.

15. Choi, D., Gao, Z., & Jiang, W. (2020). Attention to global warming. The Review of Financial Studies, 33(3), 1112-1145.

16. Christensen, K., Siggaard, M., & Veliyev, B. (2021). A machine learning approach to volatility forecasting. Available at SSRN.

17. Clements, A., & Liao, Y. (2017). Forecasting the variance of stock index returns using jumps and cojumps. International Journal of Forecasting, 33, 729–742.

18. Clements, A., & Preve, D. P. (2021). A practical guide to harnessing the har volatility model. Journal of Banking & Finance, 133, 106285.

19. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196. 24 DeMiguel, V., Nogales, F. J., and Uppal, R. (2014). Stock return serial dependence and out-ofsample portfolio performance.The Review of Financial Studies, 27(4), 1031-1073.

20. Demirer, R., Gupta, R. and Pierdzioch, C. (2020) Forecasting Realized Stock-Market Volatility: Do Industry Returns Have Predictive Value? (December 8, 2020). Available at SSRN: https://ssrn.com/abstract=3744537 Engle, R.F., and Rangel, J.G. (2008). The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes. Review of Financial Studies 21(3), 1187-1222.

21. Engle, R.F., Ghysels, E., and Sohn, B. (2013). Stock Market Volatility and Macroeconomic Fundamentals. The Review of Economics and Statistics 95(3), 776-797.

22. Faccini, R., Matin, R. and Skiadopoulos, G. 2021. Are Climate Change Risks Priced in the US Stock Market? Danmarks Nationalbank Working Paper, No. 169, February.
    Paper not yet in RePEc: Add citation now
    

23. Golosnoy, V., & Gribisch, B. (2022). Modeling and forecasting realized portfolio weights. Journal of Banking & Finance, 138, 106404.

24. Graham, J. R., & Harvey, C. R. (1996). Market timing ability and volatility implied in investment newsletters’ asset allocation recommendations. Journal of Financial Economics, 42, 397–421.

25. Gupta, R.,Hammoudeh, S., Modise,M. P.,&Nguyen, D. K. (2014). Can economic uncertainty, financial stress and consumer sentiments predict U.S. equity premium? Journal of International Financial Markets, Institutions and Money, 33, 367–378.

26. Hong, H., Torous, W., Valkanov, R. (2007). Do industries lead stock markets? Journal of Financial Economics, 83: 367-396.

27. Horpestad, J. B., Lyócsa, Š., Molnár, P., & Olsen, T. B. (2019). Asymmetric volatility in equity markets around the world. The North American Journal of Economics and Finance, 48, 540554.

28. Ji, Q., Zhang, D., & Zhao, Y. (2022). Intra-day co-movements of crude oil futures: China and the international benchmarks. Annals of Operations Research 313, 77–103.

29. Jin, Z., Guo, K., Sun, Y., Lai, L., & Liao, Z. (2020). The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR.

30. Kempf, A., & Memmel, C. (2006). Estimating the global minimum variance portfolio. Schmalenbach Business Review, 58(4), 332-348.

31. Koopman, S. J., Jungbacker, B., & Hol, E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance, 12(3), 445-475.

32. Li, X., &Wei, Y. (2018). The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method. Energy Economics, 74, 565–581.

33. Li, X., Wei, Y., Chen, X., Ma, F., Liang, C., & Chen, W. (2022). Which uncertainty is powerful to forecast crude oil market volatility? New evidence. International Journal of Finance and Economics 27 (4), 4279-4297.

34. Liang, C., Tang, L., Li, Y., & Wei, Y. (2020). Which sentiment index is more informative to forecast stock market volatility? Evidence from China. International Review of Financial 25 Analysis, 71, 101552.

35. Lyócsa, Š., Molnár, P., & Výrost, T. (2021). Stock market volatility forecasting: Do we need high-frequency data?. International Journal of Forecasting, 37(3), 1092-1110.
    Paper not yet in RePEc: Add citation now
    

36. Nonejad, N. (2017). Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why? Journal of Empirical Finance, 42, 131–154.

37. Paye, B. S. (2012). ‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables. Journal of Financial Economics, 106, 527–546.
    Paper not yet in RePEc: Add citation now
    

38. Poon, S-H, and Granger, C. W. J. (2003). Forecasting Volatility in Financial Markets: AReview. Journal of Economic Literature, 41(2), 478-539.

39. Rangel, J.G., and Engle, R.F. (2011). The Factor-Spline-GARCH Model for High and Low Frequency Correlations. Journal of Business & Economic Statistics 30(1), 109-124.

40. Rapach, D.E., Strauss, J.K., and Wohar, M.E. (2008). Forecasting stock return volatility in the presence of structural breaks, in Forecasting in the Presence of Structural Breaks and Model Uncertainty, in David E. Rapach and Mark E. Wohar (Eds.), Vol. 3 of Frontiers of Economics and Globalization, Bingley, United Kingdom: Emerald (May 2008), 381-416.
    Paper not yet in RePEc: Add citation now
    
41. Singh, A. (2016). On the linkages between India VIX and US financial stress index. Theoretical Economics Letters, 06, 68–74.
    Paper not yet in RePEc: Add citation now
    

42. Sum, V. (2014). Dynamic effects of financial stress on the U.S. real estate market performance. Journal of Economics and Business, 75, 80–92.

43. Symitsi, E., Symeonidis, L., Kourtis, A., & Markellos, R. (2018). Covariance forecasting in equity markets. Journal of Banking & Finance, 96, 153-168.

44. Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.

45. Vahamaa, S., and Aijo, J. (2011). The Fed's policy decisions and implied volatility. The Journal of Futures Markets, 31(10), 995-1009.
    Paper not yet in RePEc: Add citation now
    

46. Wang, J., Lu, X., He, F.,& Ma, F. (2020). Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU? International Review of Financial Analysis, 72, 101596.

47. Wang, X.,Wu, C., & Xu,W. (2015). Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects. International Journal of Forecasting, 31, 609–619.

48. Wang, Y., Ma, F., Wei, Y., & Wu, C. (2016). Forecasting realized volatility in a changing world: A dynamic model averaging approach. Journal of Banking & Finance, 64, 136-149.

49. Zhang, H., He, Q., Jacobsen, B., & Jiang, F. (2020). Forecasting stock returns with model uncertainty and parameter instability. Journal of Applied Econometrics, 35(5), 629-644. 26 Table 1: Descriptive statistics of realized volatility estimates.

50. Zhu, K., & Ling, S. (2015). Model-based pricing for financial derivatives. Journal of Econometrics, 187, 447–457.

1. Are industry‐level indicators more helpful to forecast industrial stock volatility? Evidence from Chinese manufacturing purchasing managers index. (2021). Wei, YU ; Bai, Lan ; Yang, Kun.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:40:y:2021:i:1:p:17-39.

   Full description at Econpapers || Download paper

2. A realized EGARCH-MIDAS model with higher moments. (2021). Wu, Xinyu ; Xie, Haibin.
   In: Finance Research Letters.
   RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319308505.

   Full description at Econpapers || Download paper

3. Macroeconomic forecasts and commodity futures volatility. (2021). Jiang, Ying ; Liu, Xiaoquan ; Ye, Wuyi ; Guo, Ranran ; Deschamps, Bruno.
   In: Economic Modelling.
   RePEc:eee:ecmode:v:94:y:2021:i:c:p:981-994.

   Full description at Econpapers || Download paper

4. Is implied volatility more informative for forecasting realized volatility: An international perspective. (2020). Zhang, Yaojie ; Wei, YU ; Liang, Chao.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:39:y:2020:i:8:p:1253-1276.

   Full description at Econpapers || Download paper

5. Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects. (2020). Wang, LU ; Liu, Guoshan ; Ma, Feng.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:39:y:2020:i:5:p:797-810.

   Full description at Econpapers || Download paper

6. Predicting the Long-term Stock Market Volatility: A GARCH-MIDAS Model with Variable Selection. (2020). Lee, Tae Hwy ; Su, Zhi ; Fang, Tong.
   In: Working Papers.
   RePEc:ucr:wpaper:202009.

   Full description at Econpapers || Download paper

7. Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach. (2020). You, Yu ; Liu, Xiaochun.
   In: Journal of Banking & Finance.
   RePEc:eee:jbfina:v:116:y:2020:i:c:s0378426620301151.

   Full description at Econpapers || Download paper

8. Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. (2020). Wang, LU ; Yang, Lin ; Liu, Jing ; Ma, Feng.
   In: International Journal of Forecasting.
   RePEc:eee:intfor:v:36:y:2020:i:2:p:684-694.

   Full description at Econpapers || Download paper

9. Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model. (2020). Zhou, Zhongbao ; Fu, Zhangyan ; Jiang, Yong ; Zeng, Ximei ; Lin, Ling.
   In: Finance Research Letters.
   RePEc:eee:finlet:v:34:y:2020:i:c:s1544612319304982.

   Full description at Econpapers || Download paper

10. Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection. (2020). Lee, Tae Hwy ; Su, Zhi ; Fang, Tong.
    In: Journal of Empirical Finance.
    RePEc:eee:empfin:v:58:y:2020:i:c:p:36-49.

    Full description at Econpapers || Download paper

11. Economic policy uncertainty and the Chinese stock market volatility: Novel evidence. (2020). Zhang, Yaojie ; Li, Tao ; Ma, Feng.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:87:y:2020:i:c:p:24-33.

    Full description at Econpapers || Download paper

12. The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model. (2020). Zhou, Wei-Xing ; Xiong, Xiong ; Dai, Peng-Fei.
    In: Papers.
    RePEc:arx:papers:2007.12838.

    Full description at Econpapers || Download paper

13. Testing for an omitted multiplicative long-term component in GARCH models. (2019). Schienle, Melanie ; Conrad, Christian.
    In: Working Paper Series in Economics.
    RePEc:zbw:kitwps:121.

    Full description at Econpapers || Download paper

14. The economic sources of Chinas CSI 300 spot and futures volatilities before and after the 2015 stock market crisis. (2019). Gong, Yuting ; Chen, Qiang.
    In: International Review of Economics & Finance.
    RePEc:eee:reveco:v:64:y:2019:i:c:p:102-121.

    Full description at Econpapers || Download paper

15. Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?. (2019). Wei, YU ; Wang, Yan ; Zhu, Sha ; Liu, Qiuhong.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119314645.

    Full description at Econpapers || Download paper

16. Uncertainty and oil volatility: New evidence. (2019). Diao, Xiaohua ; Cao, Xiang ; Zeng, Qing ; Mei, Dexiang.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:525:y:2019:i:c:p:155-163.

    Full description at Econpapers || Download paper

17. U.S., European, Chinese economic policy uncertainty and Moroccan stock market volatility. (2019). El Ghini, Ahmed ; Belcaid, Karim.
    In: The Journal of Economic Asymmetries.
    RePEc:eee:joecas:v:20:y:2019:i:c:s1703494919300672.

    Full description at Econpapers || Download paper

18. International spillovers of U.S. financial volatility. (2019). Vu, Nam ; Berg, Kimberly.
    In: Journal of International Money and Finance.
    RePEc:eee:jimfin:v:97:y:2019:i:c:p:19-34.

    Full description at Econpapers || Download paper

19. Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin?. (2019). Roubaud, David ; GUPTA, RANGAN ; Bouri, Elie ; Fang, Libing.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:61:y:2019:i:c:p:29-36.

    Full description at Econpapers || Download paper

20. Geopolitical risk and oil volatility: A new insight. (2019). Zhang, Yaojie ; Tang, Yingkai ; Liu, Jing ; Ma, Feng.
    In: Energy Economics.
    RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319303433.

    Full description at Econpapers || Download paper

21. Estimating MIDAS regressions via OLS with polynomial parameter profiling. (2019). Qian, Hang ; Ghysels, Eric.
    In: Econometrics and Statistics.
    RePEc:eee:ecosta:v:9:y:2019:i:c:p:1-16.

    Full description at Econpapers || Download paper

22. Understanding stock market volatility: What is the role of U.S. uncertainty?. (2019). Yin, Libo ; Su, Zhi ; Fang, Tong.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:48:y:2019:i:c:p:582-590.

    Full description at Econpapers || Download paper

23. On the asymmetric impact of macro–variables on volatility. (2019). Gallo, Giampiero ; Amendola, Alessandra ; Candila, Vincenzo.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:76:y:2019:i:c:p:135-152.

    Full description at Econpapers || Download paper

24. Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility. (2019). Caporale, Guglielmo Maria ; Yfanti, Stavroula ; Karanasos, Menelaos.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8000.

    Full description at Econpapers || Download paper

25. The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach. (2018). Qian, Yichuo ; Fang, Libing ; Yu, Honghai ; Chen, Baizhu.
    In: Journal of Futures Markets.
    RePEc:wly:jfutmk:v:38:y:2018:i:3:p:413-422.

    Full description at Econpapers || Download paper

26. Does Global Economic Uncertainty Matter for the Volatility and Hedging Effectiveness of Bitcoin?. (2018). Roubaud, David ; GUPTA, RANGAN ; Bouri, Elie ; Fang, Libing.
    In: Working Papers.
    RePEc:pre:wpaper:201858.

    Full description at Econpapers || Download paper

27. Modeling and forecasting commodity market volatility with long-term economic and financial variables. (2018). Walther, Thomas ; Nguyen, Duc Khuong.
    In: MPRA Paper.
    RePEc:pra:mprapa:84464.

    Full description at Econpapers || Download paper

28. Models with Multiplicative Decomposition of Conditional Variances and Correlations. (2018). Teräsvirta, Timo ; Silvennoinen, Annastiina ; Amado, Cristina ; Terasvirta, Timo.
    In: NIPE Working Papers.
    RePEc:nip:nipewp:07/2018.

    Full description at Econpapers || Download paper

29. A Comparative GARCH Analysis of Macroeconomic Variables and Returns on Modelling the Kurtosis of FTSE 100 Implied Volatility Index. (2018). giouvris, evangelos ; Alsheikhmubarak, Abdulilah Ibrahim.
    In: Multinational Finance Journal.
    RePEc:mfj:journl:v:22:y:2018:i:3-4:p:119-172.

    Full description at Econpapers || Download paper

30. The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data. (2018). Zhou, Xiaoyang ; Chen, Xiaofeng ; Su, Siping ; Cao, Puju ; Lai, Kin Keung ; Chai, Jian.
    In: Energies.
    RePEc:gam:jeners:v:11:y:2018:i:6:p:1372-:d:149403.

    Full description at Econpapers || Download paper

31. Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market. (2018). Sun, Wencong ; Fang, Libing ; Yu, Honghai.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:505:y:2018:i:c:p:931-940.

    Full description at Econpapers || Download paper

32. How does stock market volatility react to NVIX? Evidence from developed countries. (2018). Chen, Ying ; Qian, Yichuo ; Fang, Libing ; Yu, Honghai.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:505:y:2018:i:c:p:490-499.

    Full description at Econpapers || Download paper

33. Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach. (2018). Xu, Qifa ; Chen, LU ; Yuan, Jing ; Jiang, Cuixia.
    In: Pacific-Basin Finance Journal.
    RePEc:eee:pacfin:v:51:y:2018:i:c:p:13-31.

    Full description at Econpapers || Download paper

34. Oil and the short-term predictability of stock return volatility. (2018). Yin, Libo ; Wang, Yudong ; Wei, YU ; Wu, Chongfeng.
    In: Journal of Empirical Finance.
    RePEc:eee:empfin:v:47:y:2018:i:c:p:90-104.

    Full description at Econpapers || Download paper

35. Models with Multiplicative Decomposition of Conditional Variances and Correlations. (2018). Teräsvirta, Timo ; Silvennoinen, Annastiina ; Amado, Cristina ; Terasvirta, Timo.
    In: CREATES Research Papers.
    RePEc:aah:create:2018-14.

    Full description at Econpapers || Download paper

36. On the influence of US monetary policy on crude oil price volatility. (2017). Scognamillo, Antonio ; Amendola, Alessandra ; Candila, Vincenzo.
    In: Empirical Economics.
    RePEc:spr:empeco:v:52:y:2017:i:1:d:10.1007_s00181-016-1069-5.

    Full description at Econpapers || Download paper

37. Forecasting realized volatility: a review. (2017). Bucci, Andrea.
    In: MPRA Paper.
    RePEc:pra:mprapa:83232.

    Full description at Econpapers || Download paper

38. Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility. (2017). Lindblad, Annika.
    In: MPRA Paper.
    RePEc:pra:mprapa:80266.

    Full description at Econpapers || Download paper

39. European equity market integration and joint relationship of conditional volatility and correlations. (2017). Virk, Nader ; Javed, Farrukh.
    In: Journal of International Money and Finance.
    RePEc:eee:jimfin:v:71:y:2017:i:c:p:53-77.

    Full description at Econpapers || Download paper

40. Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?. (2017). Wei, YU ; Hu, Yang ; Lai, Xiaodong ; Liu, Jing.
    In: Energy Economics.
    RePEc:eee:eneeco:v:68:y:2017:i:c:p:141-150.

    Full description at Econpapers || Download paper

41. Macroeconomic Variables, Leverage, Stock Returns and Stock Return Volatility. (2017). Magwedere, Margaret Rutendo.
    In: Acta Universitatis Danubius. OEconomica.
    RePEc:dug:actaec:y:2017:i:4:p:264-288.

    Full description at Econpapers || Download paper

42. Forecasting the return volatility of energy prices: A GARCH MIDAS approach. (2017). Salisu, Afees ; Swaray, Raymond.
    In: Working Papers.
    RePEc:cui:wpaper:0029.

    Full description at Econpapers || Download paper

43. MIDAS models in banking sector – systemic risk comparison. (2017). Mestel, Roland ; Gurgul, Henryk ; Syrek, Robert.
    In: Managerial Economics.
    RePEc:agh:journl:v:18:y:2017:i:2:p:165-181.

    Full description at Econpapers || Download paper

44. What drives long-term oil market volatility? Fundamentals versus Speculation. (2016). Yin, Libo ; Zhou, Yimin.
    In: Economics Discussion Papers.
    RePEc:zbw:ifwedp:20162.

    Full description at Econpapers || Download paper

45. Effects of macroeconomic uncertainty on the stock and bond markets. (2015). Christiansen, Charlotte ; Asgharian, Hossein ; Hou, Ai Jun.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:13:y:2015:i:c:p:10-16.

    Full description at Econpapers || Download paper

46. Are the KOSPI 200 implied volatilities useful in value-at-risk models?. (2015). Kim, Junsik ; Ryu, Doojin.
    In: Emerging Markets Review.
    RePEc:eee:ememar:v:22:y:2015:i:c:p:43-64.

    Full description at Econpapers || Download paper

47. Macro-Driven VaR Forecasts: From Very High to Very Low Frequency Data. (2015). Vander Elst, Harry ; Dominicy, Yves.
    In: Working Papers ECARES.
    RePEc:eca:wpaper:2013/220550.

    Full description at Econpapers || Download paper

48. Misspecification Testing in GARCH-MIDAS Models. (2015). Schienle, Melanie ; Conrad, Christian.
    In: Working Papers.
    RePEc:awi:wpaper:0597.

    Full description at Econpapers || Download paper

49. On the influence of the U.S. monetary policy on the crude oil price volatility. (2015). Scognamillo, Antonio ; Amendola, Alessandra ; Candila, Vincenzo.
    In: 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy.
    RePEc:ags:aiea15:207860.

    Full description at Econpapers || Download paper

50. Effects of Macroeconomic Uncertainty upon the Stock and Bond Markets. (2015). Christiansen, Charlotte ; Asgharian, Hossein ; Hou, Ai Jun.
    In: CREATES Research Papers.
    RePEc:aah:create:2015-15.

    Full description at Econpapers || Download paper

Report date: 2025-09-23 16:00:32 || Missing content? Let us know