Matches in SemOpenAlex for { <https://semopenalex.org/work/W3201775449> ?p ?o ?g. }
- W3201775449 endingPage "2484" @default.
- W3201775449 startingPage "2484" @default.
- W3201775449 abstract "This study analyzes the spillover effects of volatility in the Russian stock market. The paper applies the Diebold–Yilmaz connectedness methodology to characterize volatility spillovers between Russian assets. The spectral representation of the forecast variance decomposition proposed by Baruník and Křehlik is used to describe the connectivity in short-term (up to 5 days), medium-term (6–20 days) and long-term (more than 20 days) time frequencies. Additionally, two new augmented models are developed and applied to evaluate conditional spillover effects in different sectors of the Russian economy for the period from January 2012 to June 2021. It is shown that spillover effects increase significantly during political and economic crises and decrease during periods of relative stability. The rising of the overall level of spillovers in the Russian stock market coincides in time with the political crisis of 2014, the intensification of anti-Russian sanctions in 2018 and the fall in oil prices and the start of the pandemic in 2020. With the consideration of the augmented models it can be argued that a significant part of the long-term spillover effects on the Russian stock market may be caused by the influence of external economic and political factors. However, volatility spillovers generated by internal Russian idiosyncratic shocks are short-term. Thus, the proposed approach provides new information on the impact of external factors on volatility spillovers in the Russian stock market." @default.
- W3201775449 created "2021-10-11" @default.
- W3201775449 creator A5025183007 @default.
- W3201775449 creator A5027353798 @default.
- W3201775449 creator A5070253523 @default.
- W3201775449 creator A5075886960 @default.
- W3201775449 date "2021-10-04" @default.
- W3201775449 modified "2023-10-13" @default.
- W3201775449 title "Conditional Time-Varying General Dynamic Factor Models and Its Application to the Measurement of Volatility Spillovers across Russian Assets" @default.
- W3201775449 cites W1875396108 @default.
- W3201775449 cites W1969610664 @default.
- W3201775449 cites W1992278941 @default.
- W3201775449 cites W2030223245 @default.
- W3201775449 cites W2035903161 @default.
- W3201775449 cites W2039573460 @default.
- W3201775449 cites W2039734130 @default.
- W3201775449 cites W2050815921 @default.
- W3201775449 cites W2058479869 @default.
- W3201775449 cites W2062490455 @default.
- W3201775449 cites W2066580768 @default.
- W3201775449 cites W2071869259 @default.
- W3201775449 cites W2085712123 @default.
- W3201775449 cites W2094287117 @default.
- W3201775449 cites W2107712848 @default.
- W3201775449 cites W2123963758 @default.
- W3201775449 cites W2131053200 @default.
- W3201775449 cites W2144055462 @default.
- W3201775449 cites W2145942793 @default.
- W3201775449 cites W2167693791 @default.
- W3201775449 cites W2419479474 @default.
- W3201775449 cites W2592411895 @default.
- W3201775449 cites W2787827843 @default.
- W3201775449 cites W2789457967 @default.
- W3201775449 cites W2799519329 @default.
- W3201775449 cites W2803805892 @default.
- W3201775449 cites W2888832810 @default.
- W3201775449 cites W2889410933 @default.
- W3201775449 cites W2889880961 @default.
- W3201775449 cites W2891247890 @default.
- W3201775449 cites W2906393807 @default.
- W3201775449 cites W2912547876 @default.
- W3201775449 cites W2940802932 @default.
- W3201775449 cites W2941675756 @default.
- W3201775449 cites W2941853946 @default.
- W3201775449 cites W2946737604 @default.
- W3201775449 cites W2954258605 @default.
- W3201775449 cites W2964340579 @default.
- W3201775449 cites W2988667421 @default.
- W3201775449 cites W3006348689 @default.
- W3201775449 cites W3008190017 @default.
- W3201775449 cites W3015133521 @default.
- W3201775449 cites W3016752284 @default.
- W3201775449 cites W3019724901 @default.
- W3201775449 cites W3020982786 @default.
- W3201775449 cites W3026479760 @default.
- W3201775449 cites W3035484674 @default.
- W3201775449 cites W3039053275 @default.
- W3201775449 cites W3044693676 @default.
- W3201775449 cites W3047943986 @default.
- W3201775449 cites W3072540861 @default.
- W3201775449 cites W3081612375 @default.
- W3201775449 cites W3105322001 @default.
- W3201775449 cites W3111862822 @default.
- W3201775449 cites W3120162374 @default.
- W3201775449 cites W3121195099 @default.
- W3201775449 cites W3121899577 @default.
- W3201775449 cites W3122186884 @default.
- W3201775449 cites W3122847540 @default.
- W3201775449 cites W3124094741 @default.
- W3201775449 cites W3125023246 @default.
- W3201775449 cites W3125237652 @default.
- W3201775449 cites W3125467025 @default.
- W3201775449 cites W3128190207 @default.
- W3201775449 cites W3134090484 @default.
- W3201775449 cites W3149240433 @default.
- W3201775449 cites W3152029731 @default.
- W3201775449 cites W3163363740 @default.
- W3201775449 cites W3179324197 @default.
- W3201775449 cites W4230327943 @default.
- W3201775449 cites W4231125268 @default.
- W3201775449 cites W4246715751 @default.
- W3201775449 cites W4253163276 @default.
- W3201775449 cites W871040644 @default.
- W3201775449 doi "https://doi.org/10.3390/math9192484" @default.
- W3201775449 hasPublicationYear "2021" @default.
- W3201775449 type Work @default.
- W3201775449 sameAs 3201775449 @default.
- W3201775449 citedByCount "2" @default.
- W3201775449 countsByYear W32017754492022 @default.
- W3201775449 crossrefType "journal-article" @default.
- W3201775449 hasAuthorship W3201775449A5025183007 @default.
- W3201775449 hasAuthorship W3201775449A5027353798 @default.
- W3201775449 hasAuthorship W3201775449A5070253523 @default.
- W3201775449 hasAuthorship W3201775449A5075886960 @default.
- W3201775449 hasBestOaLocation W32017754491 @default.
- W3201775449 hasConcept C106159729 @default.
- W3201775449 hasConcept C127413603 @default.
- W3201775449 hasConcept C139719470 @default.