Predicting the economic public opinions in EuropeSYRTO Project
Predicting the economic public opinions in Europe
Maurizio Carpita, Enrico Ciavolino, Mariangela Nitti
University of Brescia & University of Salento
SYRTO Project Final Conference, Paris – February 19, 2016
Results of the SYRTO Project
Roberto Savona - Primary Coordinator of the SYRTO Project
University of Brescia
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Comment on:Risk Dynamics in the Eurozone: A New Factor Model forSovereign C...SYRTO Project
Comment on:Risk Dynamics in the Eurozone: A New Factor Model forSovereign CDS and Equity Returnsby Dellaportas, Meligkotsidou, Savona, Vrontos. Andre Lucas. Amsterda, June, 25 2015. Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models. Andre Lucas. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
Entropy and systemic risk measures
M. Billio, R. Casarin, M. Costola, A. Pasqualini
Ca’ Foscari Venice University
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Predicting the economic public opinions in EuropeSYRTO Project
Predicting the economic public opinions in Europe
Maurizio Carpita, Enrico Ciavolino, Mariangela Nitti
University of Brescia & University of Salento
SYRTO Project Final Conference, Paris – February 19, 2016
Results of the SYRTO Project
Roberto Savona - Primary Coordinator of the SYRTO Project
University of Brescia
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Comment on:Risk Dynamics in the Eurozone: A New Factor Model forSovereign C...SYRTO Project
Comment on:Risk Dynamics in the Eurozone: A New Factor Model forSovereign CDS and Equity Returnsby Dellaportas, Meligkotsidou, Savona, Vrontos. Andre Lucas. Amsterda, June, 25 2015. Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models. Andre Lucas. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
Entropy and systemic risk measures
M. Billio, R. Casarin, M. Costola, A. Pasqualini
Ca’ Foscari Venice University
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Clustering in dynamic causal networks as a measure of systemic risk on the eu...SYRTO Project
Clustering in dynamic causal networks as a measure of systemic risk on the euro zone
M. Billio, H. Gatfaoui, L. Frattarolo, P. de Peretti
IESEG/ Universitè Paris1 Panthèon-Sorbonne/ University Ca' Foscari
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
A Dynamic Factor Model: Inference and Empirical Application. Ioannis Vrontos SYRTO Project
The document describes a dynamic factor model to analyze how financial risks are interconnected within the Eurozone. It uses the model to examine risk dynamics using sovereign CDS and equity returns from 2007-2009 covering the US financial crisis and pre-sovereign crisis in Europe. The model relates asset returns to latent sector factors, macro factors, and covariates. Bayesian inference is applied using MCMC to estimate the time-varying parameters and latent factors.
Network and risk spillovers: a multivariate GARCH perspectiveSYRTO Project
M. Billio, M. Caporin, L. Frattarolo, L. Pelizzon: “Network and risk spillovers: a multivariate GARCH perspective”.
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Scalable inference for a full multivariate stochastic volatilitySYRTO Project
Scalable inference for a full multivariate stochastic volatility
P. Dellaportas, A. Plataniotis and M. Titsias UCL(London), AUEB(Athens), AUEB(Athens)
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time...SYRTO Project
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models. Andre Lucas. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of N...SYRTO Project
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of Network Connectivity on Factor Exposures, Asset pricing and Portfolio Diversification” by Billio, Caporin, Panzica and Pelizzon. Arjen Siegmann. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
This document discusses the relevance and implications of forecasting retail deposits. Forecasting retail deposits involves analyzing macroeconomic data to build models that can accurately predict future deposit levels given economic conditions. Accurately forecasting deposits is important for banks to inform strategic planning and decisions around operations, technology, and infrastructure needs. The implications of deposit forecasting are discussed from social and philosophical perspectives, including how forecasting stems from humans' innate desire to understand and prepare for an uncertain future.
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based ...SYRTO Project
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based indicators for systemic risk of Euro area sovereigns and for Euro area financial firms” by Lucas et al. - Carsten Detken.
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
The document summarizes a study on modeling risk aggregation and sensitivity analysis for economic capital at banks. It finds that different risk aggregation methodologies, such as historical bootstrap, normal approximation, and copula models, produce significantly different economic capital estimates ranging from 10% to 60% differences. The empirical copula approach tends to be the most conservative while normal approximation is the least conservative. The results indicate banks should take a conservative approach to quantify integrated risk and consider the impact of methodology choice and parameter uncertainty on economic capital estimates.
Scenarios - approaches for exploring urban futures Ian Miles
This document summarizes a presentation on developing scenarios for urban futures. It discusses different types of scenario analysis including departures, destinations, and success scenarios. Departures analyze the consequences of uncertain events, destinations examine how futures could be realized given drivers, and success scenarios envision a desirable future. Methods for developing scenarios like expert groups, surveys, and workshops are presented. The document concludes by summarizing a scenario planning workshop for Greater Manchester that identified drivers, current issues, visions of success, and potential actions across sectors like environment, economy and governance.
Systemic risk signaling using scores - Andre Lucas, Bernd Schwaab, Xin Zhang,...SYRTO Project
Systemic risk signaling using scores - Andre Lucas, Bernd Schwaab, Xin Zhang, Francisco Blasques, Siem Jan Koopman, Julia Schaumburg.
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
The document describes a new credit risk modeling technique using a Bayesian network with a latent variable. It introduces a discrete Bayesian network model containing a latent variable that represents different classes of probability distributions for credit risk. The model allows evaluating credit risk and clustering loan subscribers. The document then provides details of the Bayesian network model and proposes a customized Expectation Maximization algorithm to learn the model parameters from data. The model and learning approach are applied to a real loan data set to classify loans and analyze credit risk profiles.
This document summarizes a working paper that investigates systematic default risk for firms in different countries and industries from 1980-2014. The main findings are:
1) There is evidence of a distinct world default risk cycle related to but different from macroeconomic cycles. Approximately 18-26% of global default risk variation is systematic.
2) Shared exposure to global and regional macroeconomic factors explains only 2-4% of total default risk variation, while global and regional "frailty" factors account for 7-18% and 1-11% respectively.
3) Industry-specific factors represent an additional significant source (17-31%) of default clustering, particularly for transportation/energy, consumer goods, and retail
2011 NIJ Crime Mapping Conference - Data Mining and Risk Forecasting in Web-b...Azavea
- Azavea is a software company based in Philadelphia that develops web-based crime analysis, early warning, and risk forecasting tools to help police departments like analyzing crime data.
- Their HunchLab software aims to automate time-intensive data analysis tasks and provide predictive analytics capabilities to help forecast crime patterns and risks.
- Two key features of HunchLab are near repeat pattern analysis, which quantifies the increased risk of similar crimes nearby in space and time after an initial crime, and load forecasting, which predicts expected crime counts based on historical seasonal and trend patterns.
Data Matrix Of Cpi Data Distribution After Transformation...Kimberly Jones
Here is a draft essay on default risk and ways of identifying it:
Introduction
Default risk refers to the possibility that a borrower will fail to make timely payments on their debt obligations. For lenders and investors, understanding and assessing default risk is crucial. Higher default risk indicates a greater likelihood that the borrower may default, resulting in losses for the lender. This essay will discuss default risk and some key ways that lenders and analysts identify and measure default risk.
Credit Ratings
One of the most common ways default risk is assessed is through credit ratings provided by rating agencies such as Moody's, S&P, and Fitch. These agencies analyze a borrower's financial strength and assign a rating that indicates their
Data Center for systemic risk - Michele Costola. July, 2 2014SYRTO Project
Data Center for systemic risk - Michele Costola
SYRTO Code Workshop
Syrto Workshop on Systemic Risk Policy ISSUES Bundesbank-ECB-ESRB
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Bankruptcy Prediction is an art of predicting bankruptcy and various measures of financial
distress of public or private firms. In recent past days we are seeing many cases with distress
and bankrupted. It is a huge area of finance and accounting research. The importance of the
world is due partially to the relevance for creditors and investors in evaluating the likelihood
that a firm may go bankrupt. The quantity of research is additionally a function of the supply of
data: for public firms which went bankrupt or not, numerous accounting ratios which
may indicate danger can be calculated, and various other potential explanatory variables also
are available. Consequently, the world is well-suited for testing of increasingly sophisticated,
data-intensive forecasting approaches.
Based on Dunn's chapter 4 Forecasting Expected Policy Outcomes
From
Public Policy Analysis: An Integrated Approach, Sixth Edition by William N. DUNN (2014)
Clustering in dynamic causal networks as a measure of systemic risk on the eu...SYRTO Project
Clustering in dynamic causal networks as a measure of systemic risk on the euro zone
M. Billio, H. Gatfaoui, L. Frattarolo, P. de Peretti
IESEG/ Universitè Paris1 Panthèon-Sorbonne/ University Ca' Foscari
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
A Dynamic Factor Model: Inference and Empirical Application. Ioannis Vrontos SYRTO Project
The document describes a dynamic factor model to analyze how financial risks are interconnected within the Eurozone. It uses the model to examine risk dynamics using sovereign CDS and equity returns from 2007-2009 covering the US financial crisis and pre-sovereign crisis in Europe. The model relates asset returns to latent sector factors, macro factors, and covariates. Bayesian inference is applied using MCMC to estimate the time-varying parameters and latent factors.
Network and risk spillovers: a multivariate GARCH perspectiveSYRTO Project
M. Billio, M. Caporin, L. Frattarolo, L. Pelizzon: “Network and risk spillovers: a multivariate GARCH perspective”.
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Scalable inference for a full multivariate stochastic volatilitySYRTO Project
Scalable inference for a full multivariate stochastic volatility
P. Dellaportas, A. Plataniotis and M. Titsias UCL(London), AUEB(Athens), AUEB(Athens)
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time...SYRTO Project
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models. Andre Lucas. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of N...SYRTO Project
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of Network Connectivity on Factor Exposures, Asset pricing and Portfolio Diversification” by Billio, Caporin, Panzica and Pelizzon. Arjen Siegmann. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
This document discusses the relevance and implications of forecasting retail deposits. Forecasting retail deposits involves analyzing macroeconomic data to build models that can accurately predict future deposit levels given economic conditions. Accurately forecasting deposits is important for banks to inform strategic planning and decisions around operations, technology, and infrastructure needs. The implications of deposit forecasting are discussed from social and philosophical perspectives, including how forecasting stems from humans' innate desire to understand and prepare for an uncertain future.
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based ...SYRTO Project
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based indicators for systemic risk of Euro area sovereigns and for Euro area financial firms” by Lucas et al. - Carsten Detken.
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
The document summarizes a study on modeling risk aggregation and sensitivity analysis for economic capital at banks. It finds that different risk aggregation methodologies, such as historical bootstrap, normal approximation, and copula models, produce significantly different economic capital estimates ranging from 10% to 60% differences. The empirical copula approach tends to be the most conservative while normal approximation is the least conservative. The results indicate banks should take a conservative approach to quantify integrated risk and consider the impact of methodology choice and parameter uncertainty on economic capital estimates.
Scenarios - approaches for exploring urban futures Ian Miles
This document summarizes a presentation on developing scenarios for urban futures. It discusses different types of scenario analysis including departures, destinations, and success scenarios. Departures analyze the consequences of uncertain events, destinations examine how futures could be realized given drivers, and success scenarios envision a desirable future. Methods for developing scenarios like expert groups, surveys, and workshops are presented. The document concludes by summarizing a scenario planning workshop for Greater Manchester that identified drivers, current issues, visions of success, and potential actions across sectors like environment, economy and governance.
Systemic risk signaling using scores - Andre Lucas, Bernd Schwaab, Xin Zhang,...SYRTO Project
Systemic risk signaling using scores - Andre Lucas, Bernd Schwaab, Xin Zhang, Francisco Blasques, Siem Jan Koopman, Julia Schaumburg.
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
The document describes a new credit risk modeling technique using a Bayesian network with a latent variable. It introduces a discrete Bayesian network model containing a latent variable that represents different classes of probability distributions for credit risk. The model allows evaluating credit risk and clustering loan subscribers. The document then provides details of the Bayesian network model and proposes a customized Expectation Maximization algorithm to learn the model parameters from data. The model and learning approach are applied to a real loan data set to classify loans and analyze credit risk profiles.
This document summarizes a working paper that investigates systematic default risk for firms in different countries and industries from 1980-2014. The main findings are:
1) There is evidence of a distinct world default risk cycle related to but different from macroeconomic cycles. Approximately 18-26% of global default risk variation is systematic.
2) Shared exposure to global and regional macroeconomic factors explains only 2-4% of total default risk variation, while global and regional "frailty" factors account for 7-18% and 1-11% respectively.
3) Industry-specific factors represent an additional significant source (17-31%) of default clustering, particularly for transportation/energy, consumer goods, and retail
2011 NIJ Crime Mapping Conference - Data Mining and Risk Forecasting in Web-b...Azavea
- Azavea is a software company based in Philadelphia that develops web-based crime analysis, early warning, and risk forecasting tools to help police departments like analyzing crime data.
- Their HunchLab software aims to automate time-intensive data analysis tasks and provide predictive analytics capabilities to help forecast crime patterns and risks.
- Two key features of HunchLab are near repeat pattern analysis, which quantifies the increased risk of similar crimes nearby in space and time after an initial crime, and load forecasting, which predicts expected crime counts based on historical seasonal and trend patterns.
Data Matrix Of Cpi Data Distribution After Transformation...Kimberly Jones
Here is a draft essay on default risk and ways of identifying it:
Introduction
Default risk refers to the possibility that a borrower will fail to make timely payments on their debt obligations. For lenders and investors, understanding and assessing default risk is crucial. Higher default risk indicates a greater likelihood that the borrower may default, resulting in losses for the lender. This essay will discuss default risk and some key ways that lenders and analysts identify and measure default risk.
Credit Ratings
One of the most common ways default risk is assessed is through credit ratings provided by rating agencies such as Moody's, S&P, and Fitch. These agencies analyze a borrower's financial strength and assign a rating that indicates their
Data Center for systemic risk - Michele Costola. July, 2 2014SYRTO Project
Data Center for systemic risk - Michele Costola
SYRTO Code Workshop
Syrto Workshop on Systemic Risk Policy ISSUES Bundesbank-ECB-ESRB
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Bankruptcy Prediction is an art of predicting bankruptcy and various measures of financial
distress of public or private firms. In recent past days we are seeing many cases with distress
and bankrupted. It is a huge area of finance and accounting research. The importance of the
world is due partially to the relevance for creditors and investors in evaluating the likelihood
that a firm may go bankrupt. The quantity of research is additionally a function of the supply of
data: for public firms which went bankrupt or not, numerous accounting ratios which
may indicate danger can be calculated, and various other potential explanatory variables also
are available. Consequently, the world is well-suited for testing of increasingly sophisticated,
data-intensive forecasting approaches.
Based on Dunn's chapter 4 Forecasting Expected Policy Outcomes
From
Public Policy Analysis: An Integrated Approach, Sixth Edition by William N. DUNN (2014)
Yannick Lucotte. Is There a Competition-Stability Trade-Off in European Banking?Eesti Pank
This document summarizes a presentation given by Yannick Lucotte on the relationship between banking competition and financial stability in Europe. The presentation finds that while increased banking competition leads to higher individual bank risk-taking, as measured by lower Z-scores and distance-to-default, it decreases the systemic risk contribution of banks, as measured by higher SRISK scores. This dual impact of competition on individual versus systemic risk is explained by the franchise value theory of banking and prior empirical studies. The results suggest financial regulation should consider both micro and macroprudential perspectives when evaluating the effects of banking competition policies.
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christia...SYRTO Project
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christian Brownlees, Christina Hans, Natalia Podlich.
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
This document discusses the differences between qualitative and quantitative risk analysis. Qualitative analysis involves identifying and prioritizing risks by likelihood and impact, while quantitative analysis assigns monetary values to risks to determine if projects can be completed on time and budget. The document recommends using a combination of both for the IRTC customer service system project to accurately assess risks. It provides an example of a Norwegian project that used quantitative data from a SCADA system to facilitate risk analysis updates.
Machine learning algorithms can be used in various areas of banking and central banking. Specifically, this document discusses:
1) Using machine learning for traditional credit risk modeling to forecast probability of default and assess financial stability.
2) Applying machine learning to time series forecasting of macroeconomic variables like inflation for monetary policy purposes.
3) Performing text mining on central bank research documents and news articles to measure economic uncertainty and risk in financial markets.
- Bank capital in the range of 15-23% of risk-weighted assets would have prevented most past banking crises in advanced economies. The benefits of even higher bank capital are small.
- Transition costs to higher capital standards can be substantial, so new minima should be imposed gradually and in favorable economic circumstances.
- A RESMF research workshop discussed gaps in regulatory frameworks, contractionary negative interest rates, optimal risk taking and simpler regulation, stress testing, political support for highly-levered banks, and revolving doors and culture in the financial system.
Machine learning algorithms can be used in various areas of banking and central banking. Specifically:
1) Traditional credit risk modeling can be enhanced with machine learning to predict probability of credit defaults based on borrower and macroeconomic variables.
2) Central banks can use credit bureau data and machine learning to monitor credit quality in real-time and provide recommendations to commercial banks.
3) Machine learning methods like random forests and neural networks outperform traditional models in time series forecasting of macroeconomic variables like inflation.
4) Unstructured text and narrative data from news, market commentary, and reports can be analyzed with machine learning to measure economic sentiment, risk, uncertainty and consensus.
Spillover dynamics for sistemic risk measurement using spatial financial time...SYRTO Project
Spillover dynamics for sistemic risk measurement using spatial financial time series models. Julia Schaumburg, Andre Lucas, Siem Jan Koopman, and Francisco Blasques. ESEM - Toulouse, August 25-29, 2014
http://www.eea-esem.com/eea-esem/2014/prog/viewpaper.asp?pid=1044
Sovereign credit risk, liquidity, and the ecb intervention: deus ex machina? ...SYRTO Project
Sovereign credit risk, liquidity, and the ecb intervention: deus ex machina? - Loriana Pelizzon, Marti Subrahmanyam, Davide Tomio, Jun Uno. June, 5 2014. First International Conference on Sovereign Bond Markets.
Measuring the behavioral component of financial fluctuaction. An analysis bas...SYRTO Project
This document summarizes a study that measures the behavioral component of financial market fluctuations using a model with two types of investors - rational investors who maximize expected utility, and behavioral investors who have S-shaped utility functions. The model blends the asset selections of these two investor types using a Bayesian approach, with the rational investor preferences as the prior and behavioral investor preferences as the conditional. An empirical analysis is conducted using the S&P 500 to estimate the optimal weighting parameter between the two investor types that maximizes past cumulative returns.
Measuring the behavioral component of financial fluctuation: an analysis bas...SYRTO Project
Measuring the behavioral component of financial fluctuation: an analysis based on the S&P500 - Caporin M., Corazzini L., Costola M. June, 27 2013. IFABS 2013 - Posters session.
The microstructure of the european sovereign bond market. Loriana Pellizzon. ...SYRTO Project
This study analyzes the microstructure of the European sovereign bond market during the Eurozone crisis between 2011-2012. It finds that credit risk, as measured by CDS spreads, is non-linearly related to market liquidity, as higher credit risk leads to much greater illiquidity. Market makers temporarily stopped participating when CDS spreads widened significantly. ECB interventions successfully reduced solvency concerns and improved liquidity. The analysis uses a unique high-frequency dataset of order and trade data from the Italian sovereign bond market, the largest in the Eurozone, to examine changes in liquidity measures like bid-ask spreads and quote quantities around periods of financial stress.
Time-Varying Temporal Dependene in Autoregressive Models - Francisco Blasques...SYRTO Project
Time-Varying Temporal Dependene in Autoregressive Models - Francisco Blasques, Siem Jan Koopman, Andre Lucas. June 2014. International Association for Applied Econometrics Annual Conference
Maximum likelihood estimation for generalized autoregressive score models - A...SYRTO Project
Maximum likelihood estimation for generalized autoregressive score models - Andre Lucas, Francisco Blasques, Siem Jan Koopman. June 2014. International Association for Applied Econometrics Annual Conference
Spillover dynamics for systemic risk measurement using spatial financial time...SYRTO Project
Spillover dynamics for systemic risk measurement using spatial financial time series models - Blasques F., Koopman S.J., Lucas A., Schaumburg J. June, 12 2014. 7th Annual SoFiE (Society of Financial Econometrics) Conference
Conditional probabilities for euro area sovereign default risk - Andre Lucas,...SYRTO Project
This document presents a novel framework for modeling conditional and joint probabilities of sovereign default risk in the Euro area based on credit default swap data. The model uses a dynamic multivariate skewed-t distribution with time-varying volatility and correlations. The analysis finds that while large-scale asset purchase programs by central banks reduced joint default risks, they did not significantly impact perceived interconnectedness between countries.
5 Tips for Creating Standard Financial ReportsEasyReports
Well-crafted financial reports serve as vital tools for decision-making and transparency within an organization. By following the undermentioned tips, you can create standardized financial reports that effectively communicate your company's financial health and performance to stakeholders.
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BONKMILLON Unleashes Its Bonkers Potential on Solana.pdfcoingabbar
Introducing BONKMILLON - The Most Bonkers Meme Coin Yet
Let's be real for a second – the world of meme coins can feel like a bit of a circus at times. Every other day, there's a new token promising to take you "to the moon" or offering some groundbreaking utility that'll change the game forever. But how many of them actually deliver on that hype?
2. Elemental Economics - Mineral demand.pdfNeal Brewster
After this second you should be able to: Explain the main determinants of demand for any mineral product, and their relative importance; recognise and explain how demand for any product is likely to change with economic activity; recognise and explain the roles of technology and relative prices in influencing demand; be able to explain the differences between the rates of growth of demand for different products.
[4:55 p.m.] Bryan Oates
OJPs are becoming a critical resource for policy-makers and researchers who study the labour market. LMIC continues to work with Vicinity Jobs’ data on OJPs, which can be explored in our Canadian Job Trends Dashboard. Valuable insights have been gained through our analysis of OJP data, including LMIC research lead
Suzanne Spiteri’s recent report on improving the quality and accessibility of job postings to reduce employment barriers for neurodivergent people.
Decoding job postings: Improving accessibility for neurodivergent job seekers
Improving the quality and accessibility of job postings is one way to reduce employment barriers for neurodivergent people.
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
STREETONOMICS: Exploring the Uncharted Territories of Informal Markets throug...sameer shah
Delve into the world of STREETONOMICS, where a team of 7 enthusiasts embarks on a journey to understand unorganized markets. By engaging with a coffee street vendor and crafting questionnaires, this project uncovers valuable insights into consumer behavior and market dynamics in informal settings."
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
How Does CRISIL Evaluate Lenders in India for Credit RatingsShaheen Kumar
CRISIL evaluates lenders in India by analyzing financial performance, loan portfolio quality, risk management practices, capital adequacy, market position, and adherence to regulatory requirements. This comprehensive assessment ensures a thorough evaluation of creditworthiness and financial strength. Each criterion is meticulously examined to provide credible and reliable ratings.
Tdasx: In-Depth Analysis of Cryptocurrency Giveaway Scams and Security Strate...
Systemic risk indicators
1. SYRTO/LABEX ReFi
Closing Conference
Paris, February 2016
Joint work with:
István Barra (a)
Francisco Blasques (a)
Siem Jan Koopman (a,b)
Rutger Jan Lange (a)
Michiel van de Leur (a)
Rutger Lit (a)
Federico Nucera (c)
Julia Schaumburg (a)
Bernd Schwaab (d,*)
Arjen Siegmann (a)
Xin Zhang (e,*)
a)Vrije Universiteit Amsterdam and Tinbergen Institute
b)CREATES
c) Luiss, Rome
d) ECB, these are not the opinions of the ECB
e) Riksbank, these are not the opinions of the Riksbank
André Lucas
Systemic Risk Indicators
SYstemic
Risk
TOmography:
Signals,
Measurements,
Transmission
Channels,
and
Policy
Interven@ons
2. (Gerlach,
2009:
policy
note
to
European
Parliament)
Financial
surveillance
before
the
current
crisis
erupted
suggested
that
problems
were
forming
but
the
indica@ons
were
too
imprecise
to
permit
a
policy
response.
Work
is
currently
being
undertaken
to
improve
the
measurement,
monitoring
and
management
of
systemic
risk.
That
requires
it
to
be
defined,
which
is
unproblema5c,
and
opera5onalized,
which
is
not.
While
promising
methods
to
measure
risk
exist,
the
data
demands
are
so
pronounced
that
sta5s5cal
risk
monitoring
will
remain
an
imprecise
science
for
years
to
come.
Where
are
we
now
?
3. Types
of
systemic
risk
• Level
of
systemic
risk
– is
systemic
risk
currently
high
or
low:
``objec@ve’’
policy
trigger
• Dynamics
of
systemic
risk
– is
systemic
risk
building
up
or
not,
growing
misalignments,
bubbles,
growing
linkages
• Distribu@on
of
systemic
risk
– finding
biggest
systemic
risk
contributors,
targeNed
monitoring
4. Types
of
measurements
• Market
prices
– forward
looking
(stock
markets,
yields,
CDS),
…
– but
also
possibly
misaligned
risk
cycles
(Minksy)
– signals
typically
coincidental
5. ESRB
Risk
Dashboard
(2016)
Lucas,
Schwaab,
Zhang
(2014,
SYRTO/JBES):
Condi@onal
euro
area
sovereign
default
risk
Lucas,
Schwaab,
Zhang
(2016,
SYRTO/JAppEctr):
Measuring
Credit
Risk
in
a
Large
Banking
System:
Econometric
Modeling
and
Empirics
6. Lange,
Siegmann
(2016,
SYRTO):
Es@ma@ng
Sovereign
Join
Default
Probabili@es
from
Bond
Yields
7. Types
of
measurements
• Fundamentals
versus
experience,
or
versus
prices
– create
a
benchmark
(fundamental)
and
see
whether
data
are
aligned
with
the
fundamentals
– typically
more
leading
8. Creal,
Schwaab,
Koopman,
Lucas
(2014,
SYRTO/REStat):
Observa@on
Driven
Mixed-‐
Measurement
Dynamic
Factor
Models
with
an
Applica@on
to
Credit
Risk
Koopman,
Lucas,
Schwaab
(2014,
SYRTO/IJF):
Nowcas@ng
and
forecas@ng
global
financial
sector
stress
and
credit
market
disloca@on
9. Creal,
Schwaab,
Koopman,
Lucas
(2014,
SYRTO/REStat):
Observa@on
Driven
Mixed-‐
Measurement
Dynamic
Factor
Models
with
an
Applica@on
to
Credit
Risk
Koopman,
Lucas,
Schwaab
(2014,
SYRTO/IJF):
Nowcas@ng
and
forecas@ng
global
financial
sector
stress
and
credit
market
disloca@on
10. Schwaab,
Koopman,
Lucas
(2016,
SYRTO/JAppEctr):
Global
Credit
Risk:
World,
Country
and
Industry
Factors
11. ESRB
Risk
Dashboard
(2016)
Schwaab,
Koopman,
Lucas
(2016,
SYRTO/JAppEctr):
Global
Credit
Risk:
World,
Country
and
Industry
Factors
Creal,
Schwaab,
Koopman,
Lucas
(2014,
SYRTO/REStat):
Observa@on
Driven
Mixed-‐
Measurement
Dynamic
Factor
Models
with
an
Applica@on
to
Credit
Risk
Koopman,
Lucas,
Schwaab
(2014,
SYRTO/IJF):
Nowcas@ng
and
forecas@ng
global
financial
sector
stress
and
credit
market
disloca@on
12. Barra,
Lucas
(2016,
SYRTO):
Unobserved
components
in
corporate
defaults
and
bond
prices
14. Koopman,
Lit,
Lucas
(2016,
SYRTO):
A
decomposi@on
of
economic
and
financial
@me
series
into
business
and
financial
cycles
15. Types
of
measurements
• Network
structures
– create
summary
measures
of
the
network
structure
– leading
or
coincidental?
– value-‐added
to
macro
summaries?
16. van
de
Leur,
Lucas
(2016,
SYRTO):
Network,
Market,
and
Book-‐Based
Systemic
Risk
Rankings.
17. van
de
Leur,
Lucas
(2016,
SYRTO):
Network,
Market,
and
Book-‐Based
Systemic
Risk
Rankings.
18. van
de
Leur,
Lucas
(2016,
SYRTO):
Network,
Market,
and
Book-‐Based
Systemic
Risk
Rankings.
19. Types
of
measurements
• Text
parsing
– count
posi@ve
and
nega@ve
news
– news
on
linkages,
even
if
indirect
?
20. Garmaev,
Rus@ge,
Lammers,
Borovkova
(2016,
VU):
Systemic
Risk:
A
News
Sen@ment
based
Approach
SRisk
SensR
21. Summary
and
conclusions
• Price
based
informa@on
largely
coincidental
• Misalignments
more
promising
in
lead
@mes,
though
also
more
data/methodology
intensive
• Network
data
appear
to
add
new
informa@on:
which?
And
how
useful?
• Research
direc@ons
– beNer
understanding
of
the
genesis
of
risks
and
imbalances;
find
appropriate
proxies
– exploi@ng
new
network
data
(benchmarking
will
be
hard)
– exploi@ng
text
or
other
big
data
sources
– measuring
and
exploi@ng
misalignments
22. This project has received funding from the European Union’s
Seventh Framework Programme for research, technological
development and demonstration under grant agreement no° 320270
www.syrtoproject.eu
This document reflects only the author’s views.
The European Union is not liable for any use that may be made of the information contained therein.