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on Confederation of Independent States |
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Issue of 2020‒06‒15
six papers chosen by |
| By: | Raymond Fisman (Boston University); April Knill (Florida State University); Sergey Mityakov (Florida State University); Margarita Portnykh (Carnegie Mellon University) |
| Abstract: | Using a framework akin to portfolio theory in asset pricing, we introduce the concept of “political beta†to model firm-level export diversification in response to global political risk. The main implication of our model is that a firm is less responsive to changes in political relations with a destination market when that country contributes less to (has lower political beta) or even hedges against (has negative political beta) the firm’s total political risk. This result follows the diversification logic of portfolio theory, in which an investor values a given asset depending on the asset’s comovement with his/her overall investment portfolio. We find patterns consistent with our model using disaggregated Russian firm-by-destination-country data during 1999-2011: trade is positively correlated with political relations, though the effect is far weaker for trading partners whose political relations with Russia are relatively uncorrelated with those of other partners in a firm’s export portfolio. Our results highlight the importance of viewing firms’ political relations as an undiversifiable source of risk, and more generally points to the value of modeling firms’ treatment of risks as a portfolio diversification problem. |
| Keywords: | Political Risk, Asset Pricing Theory; Portfolio Theory; Exports; Diversification |
| JEL: | F14 F23 F51 G11 G32 |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:bos:iedwpr:dp-342&r=all |
| By: | International Monetary Fund |
| Abstract: | As part of the IMF’s Data for Decisions (D4D) Trust Fund Project, a technical assistance (TA) mission was conducted by John Joisce and Dario Florey, IMF Experts, during September 23–October 4, 2019, to help the authorities of the Republic of Moldova in developing sectoral financial accounts and financial balance sheet statistics (FABS). The mission was the follow up to the mission held in December 2018, and evaluated the progress made since the last mission against the objectives set out in the action plan. The mission worked principally with the National Bank of Moldova (NBM), but also had meetings with officials from the Ministry of Finance (MOF), the Central Securities Depository (CSD), the National Commission for Financial Markets (NCFM) and the National Bureau of Statistics of the Republic of Moldova (NBS). |
| Date: | 2020–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:2020/174&r=all |
| By: | Maake, Witness; Van Zyl, Terence |
| Abstract: | The research aims to investigate the role of hidden orders on the structure of the average market impact curves in the five BRICS financial markets. The concept of market impact is central to the implementation of cost-effective trading strategies during financial order executions. The literature of Lillo et al. (2003) is replicated using the data of visible orders from the five BRICS financial markets. We repeat the implementation of Lillo et al. (2003) to investigate the effect of hidden orders. We subsequently study the dynamics of hidden orders. The research applies machine learning to estimate the sizes of hidden orders. We revisit the methodology of Lillo et al. (2003) to compare the average market impact curves in which true hidden orders are added to visible orders to the average market impact curves in which hidden orders sizes are estimated via machine learning. The study discovers that : (1) hidden orders sizes could be uncovered via machine learning techniques such as Generalized Linear Models (GLM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF); and (2) there exist no set of market features that are consistently predictive of the sizes of hidden orders across different stocks. Artificial Neural Networks produce large R^2 and small MSE on the prediction of hidden orders of individual stocks across the five studied markets. Random Forests produce the ˆ most appropriate average price impact curves of visible and estimated hidden orders that are closest to the average market impact curves of visible and true hidden orders. In some markets, hidden orders produce a convex power-law far-right tail in contrast to visible orders which produce a concave power-law far-right tail. Hidden orders may affect the average price impact curves for orders of size less than the average order size; meanwhile, hidden orders may not affect the structure of the average price impact curves in other markets. The research implies ANN and RF as the recommended tools to uncover hidden orders. |
| Keywords: | Hidden Orders; Market Features; GLM; ANN; SVM; RF; Hidden Order Sizes; Market Impact; BRICS(Brazil, Russia, India, China, and South Africa) |
| JEL: | C4 C8 D4 |
| Date: | 2020–02–28 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:99075&r=all |
| By: | Michał Brzeziński (Faculty of Economic Sciences, University of Warsaw); Katarzyna Sałach (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | We provide the first attempt to understand how differences in households’ socio-demographic and economic characteristics account for disparities in wealth inequality between five post-socialist countries of Central and Eastern Europe. We use 2013/2014 data from the second wave of the Household Finance and Consumption Survey (HFCS) and the reweighted Oaxaca-Blinder-like decompositions based on recentered influence function (RIF) regressions. Our results show that the differences in homeownership rates account for up to 42% of the difference in wealth inequality measured with the Gini index and for as much as 63-109% in case of the P50/P25 percentile ratio. Differences in homeownership rates are related to alternative designs of housing tax policies but could be also driven by other factors. We correct for the problem of the ‘missing rich’ in household surveys by calibrating the HFCS survey weights to top wealth shares adjusted using wealth data from national rich lists. Empirically, the correction procedure strengthens the importance of homeownership rates in accounting for cross-country wealth inequality differences, which suggests that our results are not sensitive to the significant underestimation of top wealth observations in the HFCS. |
| Keywords: | wealth inequality, decomposition, recentered influence function (RIF) regressions, survey weight calibration, Household Finance and Consumption Survey (HFCS), post-socialist transition, Central and Eastern Europe (CEE), housing, homeownership, missing rich |
| JEL: | D31 D63 P36 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2020-14&r=all |
| By: | International Monetary Fund |
| Abstract: | The National Bank of the Republic of Belarus (NBRB) is reforming its monetary policy framework in line with recommendations of past IMF TA missions and its Road Map for Transitioning to Inflation Targeting with the aim of eventually adopting inflation targeting (IT). Transitioning to IT would require, among other strengthening the monetary policy forecasting and analysis system (FPAS) and better integrating the core quarterly projection model (QPM) into the decision-making process. This mission was the seventh in a planned series of quarterly FPAS TA missions. It was mainly aimed at helping with reviewing the initial conditions and compiling a QPM-based forecast as a part of the NBRB’s September forecasting round. The mission, in addition, worked on strengthening processes within the FPAS. |
| Date: | 2020–05–15 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:2020/162&r=all |
| By: | International Monetary Fund |
| Abstract: | The purpose of the mission was to assist the National Statistics Office of Georgia (Geostat) with expanding the coverage of the producer price index (PPI) to additional services activities. This was the fourth price statistics mission to Georgia conducted under the auspices of the three-year Project to Improve National Accounts and Price Statistics in Eastern and Southeastern Europe. This project is funded by the Government of the Netherlands. |
| Date: | 2020–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:2020/173&r=all |