Current and most
Recent Projects funded by the Anniversary Fund of the Austrian Central Bank (OeNB)
Project Lead: Martin Meier
Team:
Leopold Sögner
Duration:
March 2018 to February 2021
Funding: Oesterreichische Nationalbank (OeNB) Anniversary Funds - Project Number 17656
``Optimal High-Risk Investment'', 7th Austrian
Stochastic Days, September 13-14, 2018, WU Vienna.
``Optimal High-Risk Investment'', WU Brown-bag seminar
in Finance, WU Vienna, Vienna; May 8, 2019.
``Optimal High-Risk Investment - The Limited
Information Case'', 8th Austrian Stochastic Days, September 10-11, 2020,
University of Graz.
''Hunting for Superstars'', with M. Meier, Mathematics
and Financial Economics, Vol. 53, 335-371 [https://doi.org/10.1007/s11579-023-00337-9]
Abstract: The “superstar economy” is characterized by payoff
functions that depend in a discontinuous way on the quality level of the
corresponding products and services. Firm A might generate much higher returns
than firm B, although A’s product is only marginally superior to B’s product.
We look at an investor who considers to invest into start-ups that want to
become active in one particular technological segment. Consequently, only the
very best few projects generate high returns. The investor is faced with a
sequence of investment opportunities, observes the objective relative rankings
of the corresponding projects seen so far, and must decide whether and how much
to invest into the currently observed opportunity. Returns are realized at the
end of the investment horizon. We derive the value functions and optimal
investment rules for risk-neutral and risk averse investors. Under weak
assumptions, the expected infinite horizon utility exceeds that of initial
wealth. We show that for a risk-neutral investor “invest all or nothing”,
depending on the project’s ranking and time of occurrence, is an optimal
strategy. For a risk-averse investor the optimal rule is non-linear and path
dependent. A simulation study is performed for risk-neutral and log-utility investors.
“Optimal High-risk
Investment”, Martin Meier, Leopold Sögner and Gregor Kastner
Version at SSRN: https://ssrn.com/abstract=4546624.
Abstract: This article extends the investment model of Bruss
and Ferguson (2002), where an investor observes a sequence of T investment alternatives,
each endowed with a quality characteristic. The information available at any
period is the current and all prior quality characteristics. The investor has
to decide whether to invest in the same period the project shows up. Finally,
after the last investment alternative has shown up, those n projects with highest
realized quality characteristics generate positive gross-returns which depend
on their relative ranking, while the payoffs of all other projects are zero.
Under these assumptions we derive a recursive formulation of the value function
for risk-neutral or risk-averse investors. We show that value function is a sum
of expected utilities arising from past investments and a residual term which
describes the expected utility contribution from investing the remaining wealth
optimally. The first part is closed from while the residual term has to be
obtained recursively. Optimal vestment rules follow from past observations and
this residual term. A simulation study demonstrates how optimal investment
decisions are affected by the time horizon and by the attitudes towards risk.
In addition, we provide sufficient conditions under that the value function is
non-increasing in the number of periods T.
Project Lead: Jaroslava Hlouskova
Team: Ines
Fortin, Leopold Sögner
Duration:
April 2019 – October 2021
Funding: Jubiläumsfonds Project 18115
This project
introduces a new index measuring financial (in)stability in Austria and in the
Euro area. The new index is a so-called uncertainty index, which is
methodologically fundamentally different from existing financial stability
indicators. While financial stability indicators measure the level of
(in)stability in a financial system, our new `stress uncertainty index'
measures the degree of predictability of (in)stability.
Publications
and Presentations:
Ines Fortin (presenting), Jaroslava Hlouskova, Leopold
Sögner: Financial instability and economic activity, 15th International
Conference on Computational and Financial Econometrics (CFE 2021), London,
Dezember 2021.
''Financial and economic uncertainties and their
effects on the economy”, with Ines Fortin and Jaroslava Hlouskova, Empirica, 2023, Vol. 50, pp. 481-521.
[https://doi.org/10.1007/s10663-023-09570-3]
Abstract: We
estimate new indices measuring financial and economic uncertainty in the euro
area, Germany, France, the United Kingdom and Austria, following the approach
of Jurado et al. (Am Econ Rev 105:1177–1216, 2015), which measures uncertainty
by the degree of predictability. We perform an impulse response analysis in a
vector error correction framework, where we focus on the impact of both local
and global uncertainty shocks on industrial production, employment and the
stock market. We find that global financial and economic uncertainties have
significant negative effects on local industrial production, employment, and
the stock market, while we find hardly any influence of local uncertainty on
these variables. In addition we perform a forecasting analysis, where we assess
the merits of uncertainty indicators for forecasting industrial production,
employment and the stock market, using different performance measures. The
results suggest that financial uncertainty significantly improves the forecasts
of the stock market in terms of profit-based measures, while economic
uncertainty gives, in general, more insight when forecasting macroeconomic
variables.
Project Lead: Leopold Sögner
Team: Ines
Fortin, Masoud Abdollahi Mobarakeh, Martin Wagner
Duration:
April 2022 – March 2025
Funding: Oesterreichische Nationalbank (OeNB) Anniversary Funds - Project Number 18766
This research
project develops and applies econometric tools to perform online break-point
detection (monitoring) in a Johansen (1995)-type vector error correction model. To monitor
structural changes the break-point detection procedures of Seo (1998) and
Hansen and Johansen (1999) are planned to be adapted to the monitoring case.
The monitoring tools are applied to investigate the stability of money demand
and some arbitrage parities discussed in finance literature. The tools are made
available in an R-package.
Publications and Presentations:
''Eigenvalue
based monitoring of structural breaks in error correction models'',
15th
International Conference on Computational and Financial Econometrics (CFE'21)},
18-20 December 2021, London and virtual conference.
''Mixed-Frequency
Dynamic Factor Models'', 16th International Conference on Computational and
Financial Econometrics (CFE'22), 17-19 December 2022, King's College, London.
``Retrieval from
Mixed Sampling Frequency: Generic Identifiability in the Unit Root VAR'', Tenth
Italian Congress of Econometrics and
Empirical Economics,
University of
Cagliari, 26-28 May 2023.
''Monitoring of
Structural Breaks in Error Correction Models'', 17th International Conference
on Computational and Financial Econometrics (CFE'23), 16-18 December 2023,
Hochschule für Technik und Wirtschaft, Berlin.
(presented by Masoud Abdollahi Mobarakeh)
''Open-End
Monitoring of Structural Breaks in the Cointegration VAR'', 17th International
Conference on Computational and Financial Econometrics (CFE'23), 16-18 December
2023, Hochschule für Technik und Wirtschaft, Berlin.
``Open-End
Monitoring of Structural Breaks in the Cointegration VAR'', {\em HiTEc meeting & Workshop on Complex data in
Econometrics and Statistics (HiTEc \& CoDES
2024)} Cyprus University of Technology, Limassol, Cyprus, 23-24 March 2024.
ng
''Monitoring of
Structural Breaks in Error Correction Models'', 18th International Conference
on Computational and Financial Econometrics (CFE'24), 14-16 December 2024, King’s
College, London, (presented by Masoud Abdollahi Mobarakeh).
``Online
Breakpoint-Detection in Cointegrating Relationships'', {\em
18th International Conference on Computational and Financial Econometrics
(CFE'2024)}, 14-16 December 2024, King's College, London.
Project Lead: Martin Meier
Team:
Lawrence Blume (Cornell University and IHS Vienna), Aleksandra Lukina (Harris
School of Public Policy, University of Chicago), Martin Meier (University of
Bath and IHS Vienna, PI), Leopold Sögner (IHS Vienna and VGSF), Stefan Thurner
(Medical University of Vienna and Complexity Science Hub, Vienna).
Duration:
July 2023 – June 2027
Funding: Oesterreichische Nationalbank (OeNB) Anniversary Funds - Project Number 18789
Economies around the world have been ``shut-down'' by
the novel coronavirus SARS-CoV-2.
Literally: Stores have closed their doors, manufacturing plants have
shut down, and commerce has slowed to a crawl. Bringing the economy back is a
gigantic coordination problem; shops cannot reopen until their suppliers do,
those suppliers in turn need inputs, and so forth.
In particular, we want to understand how the nature of
global supply chains affects the length of time it will take for the economy to
recover, to get back on path. We propose to see in a first part what we can
learn from theoretical models, in a second part we plan to use empirical data
to obtain estimates on recovery times.
Project Lead: Ines Fortin
Team:
Jaroslava Hlouskova, Leopold Sögner
Duration:
November 2022 – November 2024
Funding: Oesterreichische Nationalbank (OeNB) Anniversary Funds - Project Number 18798
Bayesian estimation of DSGE models using global nonlinear approximations
and hierarchical continuation
| Macroeconomics and Business Cycles
Project Lead: Tamas Papp
Team: Michael Reiter, Leopold Sögner
Duration: March 2023 – February 2025
Funding: Oesterreichische Nationalbank
(OeNB) Anniversary Funds - Project Number 18847
Macroeconomic
models used for understanding and forecasting GDP, employment, investment, and
consumption have a lot of parameters that determine the preferences of
consumers and firms, the technological constraints, labor market and investment
frictions, and how prices react to monetary policy.
These
parameters need to be estimated from past data. The most advanced statistical
methodology for this is Bayesian estimation, but with current tools it is only
feasible in practice for complex models when a linear approximation of a model
is used. While for some models this provides satisfactory results, for other
models, such as monetary policy models which take into account the fact that
nominal interest rates cannot be negative, or models of uncertainty shocks,
nonlinearities are important and need to be accounted for.
This project
aims to develop a practical methodology for estimation of mid-size
macroeconomics models, such as those used by central banks and forecasting
institutions, in their full nonlinear form, using a global approximation
combined with recent methodological advances in computational statistics. The
resulting software suite, documentation, and examples will be made available
under a free software license.