Anna Pestova, Mikhail Mamonov Center for Macroeconomic Analysis and Short-term Forecasting and National Research University – Higher School of Economics
Tallinn, Estonia 16.02.2017
Epic Research is performing a basic role as a leading financial advisory firm by providing good recommendations for,KLSE Stocks, Comex and Forex and all other segments with the help of experts and it maintains high accuracy.
Epic Research is performing a basic role as a leading financial advisory firm by providing good recommendations for,KLSE Stocks, Comex and Forex and all other segments with the help of experts and it maintains high accuracy.
Epic Research is performing a basic role as a leading financial advisory firm by providing good recommendations for,KLSE Stocks, Comex and Forex and all other segments with the help of experts and it maintains high accuracy.
Epic Research is performing a basic role as a leading financial advisory firm by providing good recommendations for,KLSE Stocks, Comex and Forex and all other segments with the help of experts and it maintains high accuracy.
Epic Research is performing a basic role as a leading financial advisory firm by providing good recommendations for,KLSE Stocks, Comex and Forex and all other segments with the help of experts and it maintains high accuracy.
Epic Research is performing a basic role as a leading financial advisory firm by providing good recommendations for,KLSE Stocks, Comex and Forex and all other segments with the help of experts and it maintains high accuracy.
Väljaspool pankasid tegutsevad finantsvahendajad ja Eesti finantssektorit mõ...Eesti Pank
Eesti Panga ökonomistid Taavi Raudsaar ja Mari Tamm andsid keskpanga muuseumis ülevaate Eesti finantssektorit mõjutavatest peamistest riskidest ja väljaspool pangandust tegutsevatest finantsvahendajatest.
10.11.2016
“Os pais devem ser os únicos professores de seus filhos até atingirem oito ou dez anos de
idade. Não envieis vossos pequeninos muito cedo para a escola. A mãe deve ser cuidadosa com a
maneira em que confia a modelação da mente infantil a mãos alheias. Os pais devem ser os
melhores mestres dos filhos até que eles atinjam a idade de oito ou dez anos. Sua sala de aula
deveria ser o ar livre, entre as flores e os pássaros, e seu livro de estudo, o tesouro da Natureza.”
Fundamentos da Educação Cristã , 156,157.(Veja também Christian Temperance And Bible
Hygiene,60-72.)
Zemtsov S.P. Hazardous hydrological phenomena and social vulnerability in RussiaStepan Zemtsov
The relevance of our research:
Climate change increases number of natural hazards
More than 10 million people are exposed in Russia
The main gap is social vulnerability assessment
The purpose of the research: to estimate influence of hydrological phenomena on social and economic development in Russia
The hypothesis:
real social loses from hydrological events are similar to economic damage but the first one is underestimated because of low “value of life”
Eesti Panga majanduskommentaar 13.12.2016Eesti Pank
Keskpanga president Ardo Hansson ja asepresident Ülo Kaasik kommenteerivad Eesti majanduse viimase aja arengut ja tutvustavad keskpanga majandusprognoosi.
Financial Markets Signal Detection with Bayesian Networks - Phd DREAMT - Work...Alessandro Greppi
In order to model and explain the dynamics and the signals of the market, financial operators should take into account different kind and source of information.
Unfortunately, standard tools are not always able to summarize in a signal the big amount of information available. We propose to use Bayesian Networks as a quantitative financial tool for this aim. By exploiting the network, we can combine
in the model, both market variables and sentiment ones. Bayesian Networks can be used to show the relationship among the variables belonging to different areas, and to identify in a mouse-click time the configuration that provide an operative signal. An application to the analysis of S&P 500 in the periods 1994-2003 and 2004-2015
is presented.
Väljaspool pankasid tegutsevad finantsvahendajad ja Eesti finantssektorit mõ...Eesti Pank
Eesti Panga ökonomistid Taavi Raudsaar ja Mari Tamm andsid keskpanga muuseumis ülevaate Eesti finantssektorit mõjutavatest peamistest riskidest ja väljaspool pangandust tegutsevatest finantsvahendajatest.
10.11.2016
“Os pais devem ser os únicos professores de seus filhos até atingirem oito ou dez anos de
idade. Não envieis vossos pequeninos muito cedo para a escola. A mãe deve ser cuidadosa com a
maneira em que confia a modelação da mente infantil a mãos alheias. Os pais devem ser os
melhores mestres dos filhos até que eles atinjam a idade de oito ou dez anos. Sua sala de aula
deveria ser o ar livre, entre as flores e os pássaros, e seu livro de estudo, o tesouro da Natureza.”
Fundamentos da Educação Cristã , 156,157.(Veja também Christian Temperance And Bible
Hygiene,60-72.)
Zemtsov S.P. Hazardous hydrological phenomena and social vulnerability in RussiaStepan Zemtsov
The relevance of our research:
Climate change increases number of natural hazards
More than 10 million people are exposed in Russia
The main gap is social vulnerability assessment
The purpose of the research: to estimate influence of hydrological phenomena on social and economic development in Russia
The hypothesis:
real social loses from hydrological events are similar to economic damage but the first one is underestimated because of low “value of life”
Eesti Panga majanduskommentaar 13.12.2016Eesti Pank
Keskpanga president Ardo Hansson ja asepresident Ülo Kaasik kommenteerivad Eesti majanduse viimase aja arengut ja tutvustavad keskpanga majandusprognoosi.
Financial Markets Signal Detection with Bayesian Networks - Phd DREAMT - Work...Alessandro Greppi
In order to model and explain the dynamics and the signals of the market, financial operators should take into account different kind and source of information.
Unfortunately, standard tools are not always able to summarize in a signal the big amount of information available. We propose to use Bayesian Networks as a quantitative financial tool for this aim. By exploiting the network, we can combine
in the model, both market variables and sentiment ones. Bayesian Networks can be used to show the relationship among the variables belonging to different areas, and to identify in a mouse-click time the configuration that provide an operative signal. An application to the analysis of S&P 500 in the periods 1994-2003 and 2004-2015
is presented.
European Monetary Policy & Implications for US Markets, M.-O. Strauss-Kahn, N...Soledad Zignago
Presentation of Marc-Olivier Strauss-Kahn, Banque de France, at the Global Implications of Europe’s Redesign Conference, organized by SUERF, CGEG/COLUMBIA/SIPA, EIB and Société Générale, New York, October 5-6, 2016: "European Monetary Policy and Implications for US Markets" http://www.suerf.org/ny2016
http://www.eib.org/infocentre/events/all/global-implications-of-europes-redesign.htm
Susanne: World Recession Susceptibility Analysis
This presentation covers my journey in making Susanne, to be found here: http://susanne.bitballoon.com/.
This is a statistical computation and semantic web project, and I sort of went the extra mile. We find that export and banking economic variables best predict recession susceptibility, which can be gauged with up to 92% accuracy using SVM classifiers.
This is using World Bank data and R tooling. The website is made using D3.js and AngularJS, and does not use a backend.
This is a pretty broad exploration and tutorial of basic econometrics modeling techniques. It includes an introduction to quite a few multiple regression methods. It also includes an extensive coverage of model testing to ensure that your model is quantitatively sound and statistically robust using state of the art peer reviewing protocol.
Exchange rate pass-through: What has changed since the crisis?Eesti Pank
Martina Jašová, Princeton University
Richhild Moessner, Bank for International Settlements
Előd Takáts, Bank for International Settlements
Eesti Pank, Tallinn, Estonia
21 July 2017
Global credit risk cycles, lending standards, and limits to cross border risk...SYRTO Project
Global credit risk cycles, lending standards, and limits to cross border risk diversification. Bernd Schwaab, Siem Jan Koopman, André Lucas.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Macroeconometric analysis of Ecuador's inflation before and after dollarization, proposing a model to explain where Ecuador's inflation comes from nowadays
A Study on the Short Run Relationship b/w Major Economic Indicators of US Eco...aurkoiitk
The objective of this study
was to develop an economic indicator system for the US
economy that will help to forecast the turning points in the
aggregate level of economic activity. Our primary concern
is to study the short run relationship between the major
economic indicators of US economy (eg: GDP, Money
Supply, Unemployment Rate, Inflation rate, Federal Fund
Rate, Exchange Rate, Government Expenditure &
Receipt, Crude Oil Price, Net Import & Export).
Eesti Panga president Madis Müller ja finantsstabiilsuse osakonna juhataja Jaak Tõrs tutvustasid kõigile majandushuvilistele äsja valminud Eesti finantssektori ülevaadet.
Majanduse Rahastamise Ülevaade. Veebruar 2023Eesti Pank
22.02.2023 Eesti Panga ökonomistid Taavi Raudsaar ja Mari Tamm tutvustasid äsja valminud Majanduse Rahastamise Ülevaadet ehk millised on Eesti majapidamiste ja ettevõtete rahastamisvõimalused.
Fabio Canovaand Evi Pappa. Costly disasters, energy consumption, and the role...Eesti Pank
Neljapäeval, 20. oktoobril 2022 toimus Eesti Panga avatud seminar, kus rahvusvaheliselt tunnustatud majandusteadlane Fabio Canova tutvustas koos Evi Pappaga valminud uurimustööd „Kulukad looduskatastroofid, energiatarbimine ning eelarvepoliitika“ (Costly disasters, energy consumption, and the role of fiscal policy).
Romain Duval. IMF Regional Economic Outlook for EuropeEesti Pank
31. oktoobril 2022 toimus Eesti Panga avatud seminar, kus Rahvusvahelise Valuutafondi esindaja Romain A. Duval tutvustas IMFi Euroopa osakonnas vastvalminud regionaalset majandusväljavaadet.
Pressikonverents Eesti Pangas, kus keskpanga president Madis Müller ja finantsstabiilsuse osakonna juhataja Jaak Tõrs tutvustavad ülevaadet, mis analüüsib suuremaid riske Eesti finantssektoris.
Pressikonverentsil saab teada:
kuidas majanduse jahenemine, kiire hinnakasv ja intresside tõus mõjutavad inimeste ja ettevõtete võimet laene tagasi maksta
milline mõju saab majanduse jahenemisel olema uute laenude andmisel ettevõtetele ja inimestele
kuidas mõjutavad võlakirjaturgudel toimuvad muutused Eesti pangandussektori rahastamist
milliseid samme tuleb keskpanga hinnangul astuda finantssektori tugevuse kindlustamiseks.
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the telegram contact of my personal pi vendor
@Pi_vendor_247
Financial Assets: Debit vs Equity Securities.pptxWrito-Finance
financial assets represent claim for future benefit or cash. Financial assets are formed by establishing contracts between participants. These financial assets are used for collection of huge amounts of money for business purposes.
Two major Types: Debt Securities and Equity Securities.
Debt Securities are Also known as fixed-income securities or instruments. The type of assets is formed by establishing contracts between investor and issuer of the asset.
• The first type of Debit securities is BONDS. Bonds are issued by corporations and government (both local and national government).
• The second important type of Debit security is NOTES. Apart from similarities associated with notes and bonds, notes have shorter term maturity.
• The 3rd important type of Debit security is TRESURY BILLS. These securities have short-term ranging from three months, six months, and one year. Issuer of such securities are governments.
• Above discussed debit securities are mostly issued by governments and corporations. CERTIFICATE OF DEPOSITS CDs are issued by Banks and Financial Institutions. Risk factor associated with CDs gets reduced when issued by reputable institutions or Banks.
Following are the risk attached with debt securities: Credit risk, interest rate risk and currency risk
There are no fixed maturity dates in such securities, and asset’s value is determined by company’s performance. There are two major types of equity securities: common stock and preferred stock.
Common Stock: These are simple equity securities and bear no complexities which the preferred stock bears. Holders of such securities or instrument have the voting rights when it comes to select the company’s board of director or the business decisions to be made.
Preferred Stock: Preferred stocks are sometime referred to as hybrid securities, because it contains elements of both debit security and equity security. Preferred stock confers ownership rights to security holder that is why it is equity instrument
<a href="https://www.writofinance.com/equity-securities-features-types-risk/" >Equity securities </a> as a whole is used for capital funding for companies. Companies have multiple expenses to cover. Potential growth of company is required in competitive market. So, these securities are used for capital generation, and then uses it for company’s growth.
Concluding remarks
Both are employed in business. Businesses are often established through debit securities, then what is the need for equity securities. Companies have to cover multiple expenses and expansion of business. They can also use equity instruments for repayment of debits. So, there are multiple uses for securities. As an investor, you need tools for analysis. Investment decisions are made by carefully analyzing the market. For better analysis of the stock market, investors often employ financial analysis of companies.
Even tho Pi network is not listed on any exchange yet.
Buying/Selling or investing in pi network coins is highly possible through the help of vendors. You can buy from vendors[ buy directly from the pi network miners and resell it]. I will leave the telegram contact of my personal vendor.
@Pi_vendor_247
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
how to sell pi coins on Bitmart crypto exchangeDOT TECH
Yes. Pi network coins can be exchanged but not on bitmart exchange. Because pi network is still in the enclosed mainnet. The only way pioneers are able to trade pi coins is by reselling the pi coins to pi verified merchants.
A verified merchant is someone who buys pi network coins and resell it to exchanges looking forward to hold till mainnet launch.
I will leave the telegram contact of my personal pi merchant to trade with.
@Pi_vendor_247
Poonawalla Fincorp and IndusInd Bank Introduce New Co-Branded Credit Cardnickysharmasucks
The unveiling of the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card marks a notable milestone in the Indian financial landscape, showcasing a successful partnership between two leading institutions, Poonawalla Fincorp and IndusInd Bank. This co-branded credit card not only offers users a plethora of benefits but also reflects a commitment to innovation and adaptation. With a focus on providing value-driven and customer-centric solutions, this launch represents more than just a new product—it signifies a step towards redefining the banking experience for millions. Promising convenience, rewards, and a touch of luxury in everyday financial transactions, this collaboration aims to cater to the evolving needs of customers and set new standards in the industry.
The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
Empowering the Unbanked: The Vital Role of NBFCs in Promoting Financial Inclu...Vighnesh Shashtri
In India, financial inclusion remains a critical challenge, with a significant portion of the population still unbanked. Non-Banking Financial Companies (NBFCs) have emerged as key players in bridging this gap by providing financial services to those often overlooked by traditional banking institutions. This article delves into how NBFCs are fostering financial inclusion and empowering the unbanked.
how can I sell pi coins after successfully completing KYCDOT TECH
Pi coins is not launched yet in any exchange 💱 this means it's not swappable, the current pi displaying on coin market cap is the iou version of pi. And you can learn all about that on my previous post.
RIGHT NOW THE ONLY WAY you can sell pi coins is through verified pi merchants. A pi merchant is someone who buys pi coins and resell them to exchanges and crypto whales. Looking forward to hold massive quantities of pi coins before the mainnet launch.
This is because pi network is not doing any pre-sale or ico offerings, the only way to get my coins is from buying from miners. So a merchant facilitates the transactions between the miners and these exchanges holding pi.
I and my friends has sold more than 6000 pi coins successfully with this method. I will be happy to share the contact of my personal pi merchant. The one i trade with, if you have your own merchant you can trade with them. For those who are new.
Message: @Pi_vendor_247 on telegram.
I wouldn't advise you selling all percentage of the pi coins. Leave at least a before so its a win win during open mainnet. Have a nice day pioneers ♥️
#kyc #mainnet #picoins #pi #sellpi #piwallet
#pinetwork
Exploring Abhay Bhutada’s Views After Poonawalla Fincorp’s Collaboration With...beulahfernandes8
The financial landscape in India has witnessed a significant development with the recent collaboration between Poonawalla Fincorp and IndusInd Bank.
The launch of the co-branded credit card, the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card, marks a major milestone for both entities.
This strategic move aims to redefine and elevate the banking experience for customers.
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
The Evolution of Non-Banking Financial Companies (NBFCs) in India: Challenges...beulahfernandes8
Role in Financial System
NBFCs are critical in bridging the financial inclusion gap.
They provide specialized financial services that cater to segments often neglected by traditional banks.
Economic Impact
NBFCs contribute significantly to India's GDP.
They support sectors like micro, small, and medium enterprises (MSMEs), housing finance, and personal loans.
The Evolution of Non-Banking Financial Companies (NBFCs) in India: Challenges...
On the economic effect of Western sanctions on Russia
1. On the economic effect of Western
sanctions on Russia
Anna Pestova, Mikhail Mamonov
Center for Macroeconomic Analysis and Short-term Forecasting and
National Research University – Higher School of Economics
Tallinn, Estonia
16.02.2017
2. Motivation
As a result of Crimea accession to Russia in 2014, EU and US has
imposed financial sanctions on the Russian companies
Russia is an emerging economy with underdeveloped domestic financial
sector. Russian companies are highly dependent on the external finance
(corporate external debt to GDP ratio exceeds 30%)
Western sanctions terminated new debt issuance abroad
Russian corporate external debt shrunk by ¼ in 2014-2015
Russia has been experiencing economic crisis since the end of
2014. However, not only sanction are responsible for that
Oil price dropped by half (average 2015 to 2014). Being an oil and gas
exporting country (70% of merchandise export), Russia is very sensitive
to commodity prices
Following these external shocks, GDP shrunk by 3.6% in 2015,
households income decreased by more than 10%, Russian
currency (ruble) has depreciated by 90% (2014-2015)
Are economic sanctions painful for the Russian economy?
2
3. External debt of the large emerging countries:
the short-term effect of sanctions on Russia
3
Source: World Bank
0.0
100.0
200.0
300.0
400.0
500.0
600.0 1.2004
3.2004
1.2005
3.2005
1.2006
3.2006
1.2007
3.2007
1.2008
3.2008
1.2009
3.2009
1.201
3.201
1.2011
3.2011
1.2012
3.2012
1.2013
3.2013
1.2014
3.2014
1.2015
3.2015
Russian Federation
Turkey
South Africa
Mexico
Malaysia
Kazakhstan
Indonesia
India
Chile
Brazil
Argentina
Crimea accession,
sanctions against
Russia
BnUS$
4. Empirical design
4
On the basis of Bayesian VAR forecasting model we estimate
the impact of deteriorated external conditions on the Russian
economy in 2014-2015, in particular, the effect of the
sanctions
Make counterfactual simulations: if a researcher estimated
the BVAR model in the end of 2013 and knew what will
happen with the external conditions in 2014-2015, could he
or she predict the scope and the depth of the current crisis in
Russia?
Estimate model parameters over the period 2000-2013
Calculate out-of-sample forecasts for 2014-2015 conditioning on the
realized actual paths of external conditions
(1) Oil price and (2) Oil price and Corporate external debt – proxy for short-
term effect of economic sanctions
5. This paper and the literature
5
This is an empirical forecasting exercise based on the Bayesian VAR
methodology
In Russia (and in others emerging countries), the literature on
theoretically based forecasting models (DSGE) is scarce, VAR models
provide viable and flexible alternative
Bayesian VAR is a recognized benchmark model for the (non-
structural) macroeconomic forecasting
Proposed by Doan, Litterman and Sims (1984)
Unconditional forecasting: Banbura, Giannone and Reichlin (2010), Koop
(2013), Carriero, Clark and Marcellino (2013)
Сonditional forecasting: Bloor and Matheson (2011), Banbura, Giannone
and Lenza (2015)
This paper is close to the Banbura, Giannone and Lenza (2015) paper.
However, Banbura et al. (2015) make projections conditioned on the realized
paths of key domestic macro variables (Y, P, i). In contrast, we follow more close
to what policymakers are used to do: condition on external conditions
(“scenario forecasting”)
6. Model specification
6
VAR(P): N endogenous variables and P lags
𝑌𝑡 = 𝑐 + 𝐵1 𝑌𝑡−1 + 𝐵2 𝑌𝑡−2 +… +𝐵 𝑃 𝑌𝑡−𝑃 + 𝑢 𝑡
where 𝑌𝑡 = 𝑌1𝑡, 𝑌2𝑡, … , 𝑌𝑁𝑡 ′ is a 𝑇 × 𝑁 matrix
(number of obs. × number of endogenous variables)
In a compact form:
𝑌𝑡 = 𝑋𝑡 𝐵 + 𝑢 𝑡
where 𝑋𝑡 = 𝑐𝑖, 𝑌𝑖𝑡−1, 𝑌𝑖𝑡−2, … , 𝑌𝑖𝑡−𝑃 is a 𝑇 × (1 + 𝑁 ∗ 𝑃) matrix
𝐸 𝑢 𝑡
′
𝑢 𝑠 = Σ, if 𝑡 = 𝑠; 𝐸 𝑢 𝑡
′
𝑢 𝑠 = 0, if 𝑡 ≠ 𝑠; 𝐸 𝑢 𝑡 =0.
Bayes law:
𝑝 𝐵, Σ 𝑌𝑡 ∝ 𝑝 𝑌𝑡 𝐵, Σ ∙ 𝑝 𝐵, Σ
Likelihood
(data)
Prior
distribution
of parameters
Posterior
distribution
of parameters
7. Variables
7
14 variables divided into 3 groups
External sector variables (exogenous): global financial volatility (VIX
index), Urals oil price, value of Russian exports (constant 2007 prices)
Domestic non-financial variables: GDP, wage, retail sails, and
investment (all in constant 2007 prices), value of Russian imports
(current prices); CPI inflation
Domestic financial and monetary variables: corporate external debt,
outstanding bank loans to the private sector, monetary policy interest
rate (key rate), monetary base, exchange market pressure index
(weighted average of nominal exchange rate and international reserves,
later on this)
All variables are taken in logs (except interest rate). Seasonal adjustment
procedure was applied to export, import, GDP, wage, retail sales, investment,
CPI, and monetary base; X12 in EViews
Monthly data starting from January 2000 up to September 2015 (189 obs.).
Maximum time lag k = 13 month (to capture residual seasonality, if any)
Number of estimated coefficients = N * (NP + 1) > 2500
8. Shift from the managed floating to the flexible
exchange rate regime
8
Exchange market pressure (EMP) index
Takes into account exchange rate policy shift from fixed (managed floating) to
flexible exchange rate regime
If either ruble depreciates or the Bank of Russia sells international reserves →
external pressure on exchange market increases
𝐸𝑀𝑃 =
1
𝜎𝜀
∆𝜀
𝜀
−
1
𝜎𝐼𝑛𝑡𝑅𝑒𝑠
∆𝐼𝑛𝑡𝑅𝑒𝑠
𝐼𝑛𝑡𝑅𝑒𝑠
0
100
200
300
400
500
600
700
0.0
50.0
100.0
150.0
200.0
250.0
1.2000
7.2000
1.2001
7.2001
1.2002
7.2002
1.2003
7.2003
1.2004
7.2004
1.2005
7.2005
1.2006
7.2006
1.2007
7.2007
1.2008
7.2008
1.2009
7.2009
1.2010
7.2010
1.2011
7.2011
1.2012
7.2012
1.2013
7.2013
1.2014
7.2014
1.2015
7.2015
International reserves ($ bn.), right scale
EMP (2007=100)
Ruble to bi-currency basket exchange rate (period average), rub.
9. Prior specification
9
Modern Gibbs-sampler version of Minnesota prior
Independent normal inverted Wishart prior distribution of VAR coefficients B
and innovations covariance matrix 𝛴
Small open economy prior: external variables are not influenced by the
domestic ones, so that we need to shrink coefficients in different equations
differently. This is achieved at the expense of getting non-conjugate prior
The analytical representation of the posterior for B is no longer available.
Thus, we need to employ MCMC-methods (time consuming Gibbs sampling
algorithm)
VAR coefficients 𝑩 in equation for 𝑌𝑖𝑡:
of 𝑌𝑖𝑡−1: b equals 1 (if 𝑌𝑖𝑡 is non-stationary or OLS estimate from respective
AR(1) representation of 𝑌𝑖𝑡);
of 𝑌𝑖𝑡−𝑝 for all 𝑝 = 2 … 𝑃: equal to 0;
of 𝑌𝑗𝑡−𝑝 for all 𝑗 ≠ 𝑖 and 𝑝 = 1 … 𝑃: equal to 0;
Covariance matrix of 𝑩 is built to shrink the coefficients of other variables and
of deeper lags towards zero more tightly: rule of thumb: 𝜆1= 0.1 (general
tightness); 𝜆2= 0.5 (significance of other variables); 𝜆3 = 1 (own lags decay)
10. Optimization of the prior hyperparameters
10
Notes: minimal obtained values of RMSFE are marked with red color (by columns)
Rule of thumb: 𝜆1= 0.1 (general tightness); 𝜆2= 0.5 (tightness on other variables); 𝜆3 = 1
(tightness on own lags decay)
The choice of optimal values for 𝜆 is based on the minimization of root mean squared forecast
errors (RMSFE, as a % of average). Estimation period: Jan. 2000 - Dec. 2011. Out-of-sample
forecasting period: Jan. 2012 - Sep. 2015, forecasting horizon = 3 months
λ1 λ2 λ3 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
0.1 0.5 1 5.95 2.2 1.15 0.049 0.16 0.09 0.30 1.86 0.12 0.56 0.17 9.13 0.24 1.39
0.1 0.1 2 5.88 1.9 1.32 0.057 0.18 0.10 0.30 1.90 0.13 0.43 0.15 8.86 0.21 1.36
0.1 0.1 1 5.86 2.0 1.22 0.058 0.17 0.10 0.30 1.86 0.13 0.45 0.15 8.25 0.23 1.37
0.1 0.5 2 6.05 1.6 1.19 0.053 0.15 0.10 0.28 1.51 0.14 0.43 0.12 6.83 0.21 0.98
0.05 0.1 1 5.86 1.9 1.24 0.055 0.18 0.11 0.32 1.88 0.13 0.44 0.14 6.97 0.21 1.36
0.05 0.5 2 5.91 2.0 1.17 0.054 0.18 0.09 0.31 1.80 0.13 0.45 0.16 8.19 0.22 1.37
0.05 0.1 2 5.98 2.0 1.37 0.057 0.19 0.11 0.32 1.86 0.13 0.45 0.14 8.00 0.19 1.34
0.05 0.5 1 5.86 2.0 1.16 0.051 0.18 0.09 0.31 1.84 0.13 0.48 0.16 6.69 0.23 1.39
11. Optimization of the prior hyperparameters
11
Notes: minimal obtained values of RMSFE are marked with red color (by columns)
λ1 λ2 λ3 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
0.1 0.5 1 6.71 3.0 1.30 0.064 0.22 0.15 0.36 2.90 0.24 0.99 0.25 13.57 0.36 2.20
0.1 0.1 2 6.99 2.6 1.60 0.080 0.23 0.17 0.36 2.85 0.26 0.79 0.21 13.94 0.31 2.14
0.1 0.1 1 6.93 2.7 1.42 0.083 0.23 0.18 0.36 2.82 0.26 0.83 0.20 12.44 0.34 2.13
0.1 0.5 2 6.68 2.3 1.25 0.058 0.17 0.18 0.33 2.23 0.22 0.85 0.20 9.76 0.30 1.60
0.05 0.1 1 6.90 2.7 1.50 0.077 0.23 0.17 0.37 2.81 0.25 0.79 0.19 11.45 0.29 2.15
0.05 0.5 2 6.76 2.8 1.35 0.071 0.22 0.15 0.34 2.72 0.26 0.82 0.25 11.67 0.30 2.13
0.05 0.1 2 7.00 2.7 1.72 0.080 0.24 0.18 0.38 2.79 0.25 0.77 0.17 12.15 0.25 2.10
0.05 0.5 1 6.85 2.8 1.32 0.065 0.23 0.15 0.34 2.76 0.25 0.87 0.24 11.71 0.32 2.15
Rule of thumb: 𝜆1= 0.1 (general tightness); 𝜆2= 0.5 (tightness on other variables); 𝜆3 = 1
(tightness on own lags decay)
The choice of optimal values for 𝜆 is based on the minimization of root mean squared forecast
errors (RMSFE, as a % of average). Estimation period: Jan. 2000 - Dec. 2011. Out-of-sample
forecasting period: Jan. 2012 - Sep. 2015, forecasting horizon = 6 months
12. 12
Notes: minimal obtained values of RMSFE are marked with red color (by columns)
λ1 λ2 λ3 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Y11 Y12 Y13 Y14
0.1 0.5 1 7.98 4.6 1.43 0.139 0.34 0.33 0.51 5.18 0.47 1.79 0.37 20.51 0.58 3.50
0.1 0.1 2 8.61 4.0 2.03 0.158 0.36 0.32 0.53 4.86 0.50 1.45 0.30 21.52 0.51 3.36
0.1 0.1 1 8.41 4.2 1.70 0.166 0.36 0.33 0.53 5.06 0.51 1.51 0.28 17.90 0.58 3.36
0.1 0.5 2 8.01 4.6 1.35 0.131 0.31 0.50 0.46 4.33 0.49 1.92 0.35 16.29 0.49 2.89
0.05 0.1 1 8.50 4.2 1.90 0.158 0.36 0.34 0.56 4.94 0.48 1.45 0.23 18.13 0.49 3.34
0.05 0.5 2 8.29 4.3 1.60 0.132 0.34 0.30 0.43 4.71 0.49 1.46 0.39 17.90 0.49 3.31
0.05 0.1 2 8.62 4.2 2.26 0.162 0.37 0.34 0.58 4.87 0.47 1.44 0.22 17.63 0.42 3.22
0.05 0.5 1 8.38 4.3 1.53 0.131 0.34 0.30 0.45 4.81 0.48 1.52 0.37 18.74 0.51 3.35
The lowest values of RMSFE are achieved under the following configuration of hyper parameters:
λ1=0.1 , λ2=0.5 and λ3=2. It holds for all forecasting horizon considered (3, 6 and 12 months)
Rule of thumb: 𝜆1= 0.1 (general tightness); 𝜆2= 0.5 (tightness on other variables); 𝜆3 = 1
(tightness on own lags decay)
The choice of optimal values for 𝜆 is based on the minimization of root mean squared forecast
errors (RMSFE, as a % of average). Estimation period: Jan. 2000 - Dec. 2011. Out-of-sample
forecasting period: Jan. 2012 - Sep. 2015, forecasting horizon = 12 months
Optimization of the prior hyperparameters
13. Conditional forecasting: setup
13
We test the empirical performance of estimated BVAR
model by making pseudo out-of-sample scenario forecasts
built on the basis of known (realized) external conditions
for the crisis period of 2014-2015
Conditional forecast = restrictions on future paths of some
variables = restrictions on future shocks (these shocks put
variables away from its respective unconditional forecasts)
Methodology: we apply the Gibbs sampling algorithm of
Waggoner and Zha (1999) to compute density forecasts
with BVAR
14. Conditional forecasts: results for 2014-2015 (1)
14
Restricted access to external debt market, together with the oil price drop, is able to explain current GDP
decline. Due to economic sanctions Russian GDP growth rates was 0,4 p.p. lower in 2014 and 0.7 in 2015
(1) Conditioned on Urals oil price (and global
volatility VIX )
(2) Conditioned on Urals oil price and
external debt stock (and global volatility VIX )
GDP growth rates, %,
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
8.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median upper 84
unconditional actual
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
8.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median upper 84
unconditional actual
15. Conditional forecasts: results for 2014-2015 (2)
15
External factors are not capable to predict the scope of inflation acceleration in 2014-2015. Possible reason –
underestimation of exchange rate depreciation due to unaccounted negative speculative expectations.
Additionally, exchange rate pass-though is underestimated (recall 𝜆3=2)
CPI Inflation, %,
0.0
5.0
10.0
15.0
20.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median
upper 84 unconditional
actual
0.0
5.0
10.0
15.0
20.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median
upper 84 unconditional
actual
(1) Conditioned on Urals oil price (and global
volatility VIX )
(2) Conditioned on Urals oil price and
external debt stock (and global volatility VIX )
16. Conditional forecasts: results for 2014-2015 (3)
16
Exchange market pressure index, 2007=100
90.0
100.0
110.0
120.0
130.0
140.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median
upper 84 actual
90.0
100.0
110.0
120.0
130.0
140.0
150.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median
upper 84 actual
The specification with EMP delivers more precise forecasts of exchange rate than the model with nominal
exchange rate (previously studied, not shown here). Exchange rate shock is caused by double shock from the
balance of payment (current account and financial account). Difference between scenarios: 5% in 2014, 9% in
2015 (due to sanctions )
(1) Conditioned on Urals oil price (and global
volatility VIX ) (2) Conditioned on Urals oil price and
external debt stock (and global volatility VIX )
𝐸𝑀𝑃 =
1
𝜎𝜀
∆𝜀
𝜀
−
1
𝜎𝐼𝑛𝑡𝑅𝑒𝑠
∆𝐼𝑛𝑡𝑅𝑒𝑠
𝐼𝑛𝑡𝑅𝑒𝑠
17. Conditional forecasts: results for 2014-2015 (4)
17
Investment growth rates, %
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median
upper 84 unconditional
actual
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median
upper 84 unconditional
actual
Investment dynamics is well described. Possible explanation - macroeconomic downturn is captured and in the
second scenario the deficit of sources of investment financing is taken into account (reduction of external debt).
(1) Conditioned on Urals oil price (and global
volatility VIX )
(2) Conditioned on Urals oil price and
external debt stock (and global volatility VIX )
18. Conditional forecasts: results for 2014-2015 (5)
18
Import growth rates, %
-60.0
-40.0
-20.0
0.0
20.0
40.0
60.0
80.0
1.2010
5.2010
9.2010
1.2011
5.2011
9.2011
1.2012
5.2012
9.2012
1.2013
5.2013
9.2013
1.2014
5.2014
9.2014
1.2015
5.2015
9.2015
lower 16 median
upper 84 unconditional
actual
-60.0
-40.0
-20.0
0.0
20.0
40.0
60.0
80.0
1.2010
6.2010
11.2010
4.2011
9.2011
2.2012
7.2012
12.2012
5.2013
10.2013
3.2014
8.2014
1.2015
6.2015
11.2015
lower 16 median
upper 84 unconditional
actual
(1) Conditioned on Urals oil price (and global
volatility VIX )
(2) Conditioned on Urals oil price and
external debt stock (and global volatility VIX )
Note that ~50% of Russian imports comes from EU countries. Due to sanctions:-3% in 2014; -11% in 2015
19. Results
19
The model captures the influence of external shocks on the
Russian economy. By making counterfactual simulations on the
different set of external conditions we are able to disentangle
their effect
The fall in oil prices alone can not explain the depth of the
current recession in the Russian economy which is partly driven
by the limited access of Russian companies to international
financial markets as a result of sanctions (-1.2 p.p. cumulative
decline in GDP growth rates in 2014-2015 is attributed to the
sanctions)
The restricted access of the Russian corporates to the new debt
issuance led to the amplification of domestic economic and
financial crisis and through external trade channel (less demand
for import) influenced the EU economies as well