371 research outputs found
Forecasting multivariate volatility in larger dimensions: some practical issues
The importance of covariance modelling has long been recognised in the field of portfolio management and large dimensional multivariate problems are increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating whether simpler moving average based correlation forecasting methods have equal predictive accuracy as their more complex multivariate GARCH counterparts for large dimensional problems. We find simpler forecasting techniques do provide equal (and often superior) predictive accuracy in a minimum variance sense. A portfolio allocation problem is used to compare forecasting methods. The global minimum variance portfolio and Model Confidence Set (Hansen, Lunde, and Nason (2003)) are used to compare methods, whilst portfolio weight stability and computational time are also considered.Volatility, multivariate GARCH, portfolio allocation
Sukupuolen ja organisaatioaseman yhteys tyΓΆssΓ€ oppimiseen ja luottamukseen :Β tarkastelussa vesihuoltolaitosten henkilΓΆstΓΆ
Recommended from our members
Effect of Inhaled Xenon on Cerebral White Matter Damage in Comatose Survivors of Out-of-Hospital Cardiac Arrest: A Randomized Clinical Trial
IMPORTANCE: Evidence from preclinical models indicates that xenon gas can prevent the development of cerebral damage after acute global hypoxic-ischemic brain injury but, thus far, these putative neuroprotective properties have not been reported in human studies. OBJECTIVE: To determine the effect of inhaled xenon on ischemic white matter damage assessed with magnetic resonance imaging (MRI). DESIGN, SETTING, AND PARTICIPANTS: A randomized single-blind phase 2 clinical drug trial conducted between August 2009 and March 2015 at 2 multipurpose intensive care units in Finland. One hundred ten comatose patients (aged 24-76 years) who had experienced out-of-hospital cardiac arrest were randomized. INTERVENTIONS: Patients were randomly assigned to receive either inhaled xenon combined with hypothermia (33Β°C) for 24 hours (nβ=β55 in the xenon group) or hypothermia treatment alone (nβ=β55 in the control group). MAIN OUTCOMES AND MEASURES: The primary end point was cerebral white matter damage as evaluated by fractional anisotropy from diffusion tensor MRI scheduled to be performed between 36 and 52 hours after cardiac arrest. Secondary end points included neurological outcome assessed using the modified Rankin Scale (score 0 [no symptoms] through 6 [death]) and mortality at 6 months. RESULTS: Among the 110 randomized patients (mean age, 61.5 years; 80 men [72.7%]), all completed the study. There were MRI data from 97 patients (88.2%) a median of 53 hours (interquartile range [IQR], 47-64 hours) after cardiac arrest. The mean global fractional anisotropy values were 0.433 (SD, 0.028) in the xenon group and 0.419 (SD, 0.033) in the control group. The age-, sex-, and site-adjusted mean global fractional anisotropy value was 3.8% higher (95% CI, 1.1%-6.4%) in the xenon group (adjusted mean difference, 0.016 [95% CI, 0.005-0.027], Pβ=β.006). At 6 months, 75 patients (68.2%) were alive. Secondary end points at 6 months did not reveal statistically significant differences between the groups. In ordinal analysis of the modified Rankin Scale, the median (IQR) value was 1 (1-6) in the xenon group and 1 (0-6) in the control group (median difference, 0 [95% CI, 0-0]; Pβ=β.68). The 6-month mortality rate was 27.3% (15/55) in the xenon group and 34.5% (19/55) in the control group (adjusted hazard ratio, 0.49 [95% CI, 0.23-1.01]; Pβ=β.053). CONCLUSIONS AND RELEVANCE: Among comatose survivors of out-of-hospital cardiac arrest, inhaled xenon combined with hypothermia compared with hypothermia alone resulted in less white matter damage as measured by fractional anisotropy of diffusion tensor MRI. However, there was no statistically significant difference in neurological outcomes or mortality at 6 months. These preliminary findings require further evaluation in an adequately powered clinical trial designed to assess clinical outcomes associated with inhaled xenon among survivors of out-of-hospital cardiac arrest. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00879892
Conservative Approach to Unilateral Condylar Fracture in a Growing Patient: A 2.5-Year Follow Up
Condylar fractures in children are especially important because of the risk of a mandibular growth-center being affected in the condylar head, which can lead to growth retardation and facial asymmetry. The purpose of this article is to follow up the two and half year clinical and radiological evaluation of the conservative treatment of a 10 year-old patient, who had a unilateral green-stick type fracture. The patient presented with painful facial swelling localized over the left condylar region, limited mouth-opening and mandibular deviation to the left. Panoramic radiography and computed tomography confirmed the diagnosis of incomplete fracture on the left condyle with one side of the bone fractured and the other bent. Closed reduction was chosen to allow for initial fibrous union of the fracture segments and remodeling with a normal functional stimulus. A non-rigid mandibular splint was applied in order to remove the direct pressure on the fracture side of the mandible. Clinical and radiologic examination after 30 months revealed uneventful healing with reduction of the condylar head and remodeling of the condylar process following conservative treatment
Recommended from our members
Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts
This study investigates whether different specifications of univariate GARCH models can usefully forecast volatility in the foreign exchange market. The study compares in-sample forecasts from symmetric and asymmetric GARCH models with the implied volatility derived from currency options for four dollar parities. The data set covers the period 2002 to 2012. We divide the data into two periods one for the period 2002 to 2007 which is characterised by low volatility and the other for the period 2008 to 2012 characterised by high volatility. The results of this paper reveal that the implied volatility forecasts significantly outperforms the three GARCH models in both low and high volatility periods. The results strongly suggest that the foreign exchange market efficiently prices in future volatility
Widespread genomic influences on phenotype in Dravet syndrome, a 'monogenic' condition
Dravet syndrome is an archetypal rare severe epilepsy, considered "monogenic", typically caused by loss-of-function SCN1A variants. Despite a recognisable core phenotype, its marked phenotypic heterogeneity is incompletely explained by differences in the causal SCN1A variant or clinical factors. In 34 adults with SCN1A-related Dravet syndrome, we show additional genomic variation beyond SCN1A contributes to phenotype and its diversity, with an excess of rare variants in epilepsy-related genes as a set and examples of blended phenotypes, including one individual with an ultra-rare DEPDC5 variant and focal cortical dysplasia. Polygenic risk scores for intelligence are lower, and for longevity, higher, in Dravet syndrome than in epilepsy controls. The causal, major-effect, SCN1A variant may need to act against a broadly compromised genomic background to generate the full Dravet syndrome phenotype, whilst genomic resilience may help to ameliorate the risk of premature mortality in adult Dravet syndrome survivors
Supporting the Development of Digitally Competent VET Teachers in Serbia and Russia
Introduction. In the modern educational space, an intensive digital transformation is currently taking place, which imposes new requirements for teacher competencies. This determines the relevance of setting goals and solving problems in order to develop up-to-date models for improving the qualifications of teachers of vocational education and training (VET). The paper discusses the current state of the development of digital competencies of teachers and teachers of Serbia and Russia in line with the European Digital Competence Framework (DigComp) and the European Digital Competence Framework for Educators (DigΠ‘ompΠdu). The paper includes an analysis of the peculiarities of vocational education and training systems, as well as the directions of further training of teachers, conducted by participants in the international project βProfessional Development of Vocation Education Teachers with European Practices (Pro-VET)β. In order to better understand national contexts, the content of the reports of the participating countries of the project was analysed in the context of the EU policy and strategy for the development of digital competency of VET teachers. In this article, the authors focus on exploring digital competencies required of VET teachers within the European Digital Competence Framework for Educators (DigΠ‘ompΠdu) to identify digital competencies and development needs of Serbian and Russian VET teachers when working in online learning environments.The aims of the research are the following: 1) to compare the educational needs of Russian and Serbian VET teachers in the development of their digital pedagogical competencies; 2) to identify the theoretical and practical base for VET teachersβ digitally competent development programme design in the context of online learning according to the best European practices in the field of VET. Methodology and research methods. The development of the model was based on learning theories, didactics and practical approaches to soft skills development in online learning environments. The research has been conducted by the means of document analysis, theoretical analysis and synthesis methods, comparative method, modelling method and expert estimation method. Results and scientific novelty. Key aspects of VET teacher training systems in Russia and Serbia are compared and needs in development of digital pedagogical skills of Russian and Serbian VET teachers are identified. A developed model of VET teachersβ digitally competent development programme design in the context of online learning according to best European practices in this fields is represented by two components: structural and functional. The structural component of VET teachersβ digitally competent development model contains: learning theories and didactics, adult learning theories, soft skills development approaches in online learning, learning outcomes development approaches. The functional component of the model contains: national and European educational policy, strategies in the field of digitalisation of education and the development of digital competencies of teachers, European Union policies related to online learning; pedagogical, psychological and didactical design parameters of the content of advanced training programmes in the context of e-learning. Practical significance. The demonstrated model is being tested in the framework of the implementation of the international Pro-VET project supported by ERASMUS+. Methodological approaches, procedure and tools of VET teachersβ digitally competent development are being developed and tested. The application of digitally competent development programmes ensures the transparency of training and allows for the correlation of national and international training programmes as well as the development of academic and professional mobility of VET teachers. The process of designing such educational training programmes in online environment for VET teachers has begun at some universities in Russia and Serbia (participants of the project). The developed online training programmes can be used as a basis to design more quality online courses beyond the Pro-VET project in the sphere of professional development for VET teachers.ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅. Π ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΌ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½Π°Ρ ΡΠΈΡΡΠΎΠ²Π°Ρ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΡ, ΠΊΠΎΡΠΎΡΠ°Ρ ΠΏΡΠ΅Π΄ΡΡΠ²Π»ΡΠ΅Ρ Π½ΠΎΠ²ΡΠ΅ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡ ΠΊ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Ρ. ΠΡΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΏΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΠΈ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ (ΠΠΠ). Π ΡΡΠ°ΡΡΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΡΡΡ ΡΠ΅ΠΊΡΡΠ΅Π΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ Π‘Π΅ΡΠ±ΠΈΠΈ ΠΈ Π ΠΎΡΡΠΈΠΈ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ Π΅Π²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΈΠΌΠΈ ΡΠ°ΠΌΠΊΠ°ΠΌΠΈ ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ (DigComp) ΠΈ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ (DigCompEdu); ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ Π°Π½Π°Π»ΠΈΠ· ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΡΠΈΡΡΠ΅ΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΉ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΎΠ², ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΠΉ ΡΡΠ°ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° Β«ΠΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΡΠ΅ ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΏΠΎ Π΅Π²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΈΠΌ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ°ΠΌ (Pro-VET)Β». Π‘ ΡΠ΅Π»ΡΡ Π»ΡΡΡΠ΅Π³ΠΎ ΠΏΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΡ Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠΎΠ² ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½ΠΎ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ Π΄ΠΎΠΊΠ»Π°Π΄ΠΎΠ² ΡΡΡΠ°Π½ β ΡΡΠ°ΡΡΠ½ΠΈΠΊΠΎΠ² Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ ΠΈ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΈ ΠΠ‘ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ. ΠΡΡΠ²Π»Π΅Π½Ρ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠΈ ΡΠ΅ΡΠ±ΡΠΊΠΈΡ
ΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ ΠΈ ΡΠΈΡΡΠΎΠ²ΡΠ΅ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅, ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ Π΅Π²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠΉ ΡΠ°ΠΌΠΊΠ΅ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ (DigCompEdu), Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³Π°ΠΌ ΠΠΠ Π΄Π»Ρ ΡΠ°Π±ΠΎΡΡ Π² ΠΎΠ½Π»Π°ΠΉΠ½-ΡΡΠ΅Π΄Π΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. Π¦Π΅Π»ΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: 1) ΡΡΠ°Π²Π½ΠΈΡΡ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΈ ΡΠ΅ΡΠ±ΡΠΊΠΈΡ
ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ Π² ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΠΈΡ
ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ; 2) ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΡΡ ΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΡΡ ΠΎΡΠ½ΠΎΠ²Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΎΠ² Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΠΎΠ½Π»Π°ΠΉΠ½-ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ Π»ΡΡΡΠΈΠΌΠΈ Π΅Π²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΈΠΌΠΈ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ°ΠΌΠΈ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΠΠ. ΠΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ. Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠΊΠ°Π·Π°Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π±Π°Π·ΠΈΡΠΎΠ²Π°Π»Π°ΡΡ Π½Π° ΡΠ΅ΠΎΡΠΈΡΡ
ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, Π΄ΠΈΠ΄Π°ΠΊΡΠΈΠΊΠΈ ΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°Ρ
ΠΊ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³ΠΈΠ±ΠΊΠΈΡ
Π½Π°Π²ΡΠΊΠΎΠ² Π² ΠΎΠ½Π»Π°ΠΉΠ½-ΡΡΠ΅Π΄Π΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π°Π½Π°Π»ΠΈΠ·Π° Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ², ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π°, ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠΈΠ½ΡΠ΅Π·Π°, ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈ Π½Π°ΡΡΠ½Π°Ρ Π½ΠΎΠ²ΠΈΠ·Π½Π°. Π‘ΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½Ρ Π°ΡΠΏΠ΅ΠΊΡΡ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ Π² Π ΠΎΡΡΠΈΠΈ ΠΈ Π‘Π΅ΡΠ±ΠΈΠΈ, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΠΈΡ
ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠΈ Π² ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠ°Ρ Π΄Π²Π° ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ°: ΡΡΡΡΠΊΡΡΡΠ½ΡΠΉ ΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΉ. Π‘ΡΡΡΠΊΡΡΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΠΉ ΡΠ΅ΠΎΡΠΈΡΠΌΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ Π΄ΠΈΠ΄Π°ΠΊΡΠΈΠΊΠΈ, ΡΠ΅ΠΎΡΠΈΡΠΌΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π²Π·ΡΠΎΡΠ»ΡΡ
, ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΊ Π²ΡΡΠ°Π±Π°ΡΡΠ²Π°Π½ΠΈΡ ΠΌΡΠ³ΠΊΠΈΡ
Π½Π°Π²ΡΠΊΠΎΠ² Π² ΠΎΠ½Π»Π°ΠΉΠ½-ΠΎΠ±ΡΡΠ΅Π½ΠΈΠΈ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π΅Π³ΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ². Π€ΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ, ΠΊΠΎΡΠΎΡΡΠΉ ΠΎΡΠ½ΠΎΠ²ΡΠ²Π°Π΅ΡΡΡ Π½Π° Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΠΈ Π΅Π²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠΉ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ΅, ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΡ
Π² ΡΡΠ΅ΡΠ΅ ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΠΉ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΎΠ², ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ΅ ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΠ·Π° Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΎΠ½Π»Π°ΠΉΠ½-ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅, ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ Π΄ΠΈΠ΄Π°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ Π΄ΠΈΠ·Π°ΠΉΠ½Π° ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ. Π£ΠΊΠ°Π·Π°Π½Π½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠ΅ΡΡΠΈΡΡΠ΅ΡΡΡ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° Pro-VET. Π Π°Π·ΡΠ°Π±Π°ΡΡΠ²Π°ΡΡΡΡ ΠΈ ΡΠ΅ΡΡΠΈΡΡΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ, ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΈ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ. ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π² ΡΡΠ΅ΡΠ΅ ΡΠΈΡΡΠΎΠ²ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅Ρ ΠΏΡΠΎΠ·ΡΠ°ΡΠ½ΠΎΡΡΡ ΠΈ ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΠΌΠΎΡΡΡ Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΠΈ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ. Π ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ°Ρ
Π ΠΎΡΡΠΈΠΈ ΠΈ Π‘Π΅ΡΠ±ΠΈΠΈ (ΡΡΠ°ΡΡΠ½ΠΈΠΊΠ°Ρ
ΠΏΡΠΎΠ΅ΠΊΡΠ°) ΡΠΎΠ·Π΄Π°ΡΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΎΠ½Π»Π°ΠΉΠ½-ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ ΠΠΠ Π½Π° Π±Π°Π·Π΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ. ΠΠ°Π½Π½ΡΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΌΠΎΠ³ΡΡ ΡΡΠ°ΡΡ ΠΎΡΠ½ΠΎΠ²ΠΎΠΉ Π΄Π»Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΠΎΠ½Π»Π°ΠΉΠ½-ΠΊΡΡΡΠΎΠ² Π·Π° ΠΏΡΠ΅Π΄Π΅Π»Π°ΠΌΠΈ ΠΏΡΠΎΠ΅ΠΊΡΠ° Pro-VET Π² ΡΡΠ΅ΡΠ΅ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΎΠ² ΠΠΠ.The research in Pro-VET project was funded by Education, Audio-visual and Culture Executive Agency, Erasmus+, ref. 598698-EPP- 1-2018-1-FI-EPPKA2-CBHE-JP. The authors are also grateful to all partners of Pro-VET team with their valuable contributions to discussions and verification of the developed procedures. The European Commissionβs support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΎΠ΅ΠΊΡΠ° ProVET ΡΠΈΠ½Π°Π½ΡΠΈΡΠΎΠ²Π°Π»ΠΈΡΡ ΠΡΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΌ Π°Π³Π΅Π½ΡΡΡΠ²ΠΎΠΌ ΠΏΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ, Π°ΡΠ΄ΠΈΠΎΠ²ΠΈΠ·ΡΠ°Π»ΡΠ½ΡΠΌ ΡΡΠ΅Π΄ΡΡΠ²Π°ΠΌ ΠΈ ΠΊΡΠ»ΡΡΡΡΠ΅, Erasmus+ (Π½ΠΎΠΌΠ΅Ρ ΠΏΡΠΎΠ΅ΠΊΡΠ° 598698-EPP-1- 2018-1-FI-EPPKA2-CBHE-JP). ΠΠ²ΡΠΎΡΡ ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠΈΠ·Π½Π°ΡΠ΅Π»ΡΠ½Ρ Π²ΡΠ΅ΠΌ ΠΏΠ°ΡΡΠ½Π΅ΡΠ°ΠΌ ΠΊΠΎΠΌΠ°Π½Π΄Ρ Pro-VET Π·Π° ΠΈΡ
ΡΠ΅Π½Π½ΡΠΉ Π²ΠΊΠ»Π°Π΄ Π² ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΈ ΠΏΡΠΎΠ²Π΅ΡΠΊΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΡ
ΠΏΡΠΎΡΠ΅Π΄ΡΡ. ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠ°Ρ ΠΊΠΎΠΌΠΈΡΡΠΈΡ, ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠ°Π²ΡΠ°Ρ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΡ ΡΡΠΎΠΉ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΈ, Π½Π΅ Π½Π΅ΡΠ΅Ρ ΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ Π·Π° Π΅Π΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ Π² Π½Π΅ΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ
Volatility Patterns of CDs, Bond and Stock Markets Before and During the Financial Crisis: Evidence from Major Financial Institutions
- β¦