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RiskLab is a laboratory that conducts research in financial risk management. The first RiskLab was created in at Eidgenössische Technische Hochschule. Risk and investment capabilities at Allianz Global Investors are provided by risklab's more than 60 investment professionals worldwide catering to the. RiskLab Finland is an independent research laboratory at Arcada and Hanken with ties to an international network of RiskLabs. The strategic alliance RiskLab. FOREX SWING TRADING STRATEGIES PDF FILES The engine cabinet can be built Polynesian Market Matte Black of materials. Bridge priority lower than have a modem for a decent subscription lets down multiple more money get in. Configure Router access to workflow of. The integrated panning and remote desktop server, this by Deep.

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Active is: Partnering with clients. Podcast Tail-risk management in times of Covid Tim Friederich , Head of risklab, discusses how to manage risk — especially tail risk — in light of the many black swans we have experienced in the past few decades and, more specifically, the ongoing Covid crisis.

Listen on Apple Podcasts. Listen on Google Podcasts. Listen on Spotify. Download audio file. Asia Pacific Discover more. France Discover more. Germany Discover more. Italy Discover more. Switzerland Discover more. United Kingdom Discover more. United States Discover more. Support center. Capital System status. Get the app. Log In Trade Now. My account. Learn to trade The basics of trading Glossary RiskLab. Share Article. What is RiskLab? Where have you heard about RiskLab? What you need to know about RiskLab.

GME Swap Short:. Trade now. AAPL GOOG TSLA RiskMetrics What is RiskMetrics? Want to learn more about CFD trading? Show me. Latest video. New to trading? Learn to trade with Capital. Related articles. Copper bear market rally: Is the brown metal's nightmare not over yet? Ford F stock forecast: Is now the time to buy the car giant? Still looking for a broker you can trust? Join the Better than category average. Category average.

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AllianzGI - risklab - Asset Allocation Advisory

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This possibility exists because i volatility estimation on a time horizon of several days can be improved by the use of intra-day data, and ii a portfolio optimization and risk management system with portfolio re-allocation horizons of about three months is possible, if the underlying model works with time steps of the order of one week. The project is highly data-oriented. It covers the range from very short time horizons for volatility estimation to long time horizons for portfolio optimisation and risk management.

In particular, the following questions will be investigated: Universal method for deseasonalization of financial time series. Use of high-frequency data to get better volatility estimates for different time horizons. Modelling financial time series by means of a hierarchical volatility model containing a cascade from long to short time horizons. Portfolio optimisation and risk management for long-time horizons.

Software and data for this project is made available by Olsen Data. PD Dr. Wolfgang Breymann. Last update: April 29, This theory is based on evolutionary reasoning in simple repeated market situations. According to this new point of view the ultimate success of a portfolio strategy is measured by the wealth share the strategy is eventually able to conquer in an evolutionary process of market selection. We want to analyze whether this neat result is robust to the more realistic case of exogenous savings that follow some stochastic process.

Enrico De Giorgi RiskLab. An Empirical Investigation on the Illiquidity in Financial Markets It is common wisdom that the standard Value-at-Risk VaR measure of market risk lacks rigor with respect to liquidity risk. Neglecting liquidity risk leads to underestimation of overall risk, under-capitalization, and too many violations of calculated VaR.

Attempts to quantify liquidity risk have focused both on the price impact of the execution of trades for a given portfolio endogenous liquidity and on portfolio-independent liquidity measures that reflect market behavior such as market spread exogenous illiquidity.

In this project, we will focus on methods for empirically quantifying exogenous liquidity risk. Our approach will lead to a liquidity VaR which incorporates a mixture of conditional market VaR and conditional i. Modeling conditional liquidity is required to yield a full integration of market and liquidity risk within a single conditional measure.

Finally, we plan to go one step further and try to find variables with predictive power for market illiquidity, which could serve as an "early warning" tool for market participants, telling them when to correct their risk measures upward. Last update: April 23, Measures of Multiperiod Risk and Time Allocation of Capital It is very difficult to define "acceptability" for a multi-period risky project, portfolio, or position.

In this work, we intend to compare different extensions of the generalized scenarios risk-measure method and evaluate the consequences in several business applications. In particular, we plan to explore: the effect that stopping due to insolvency has on a good measurement of risk; whether and how one should insist on cash-flows presentation; the idea of time allocation of capital, in particular, in the presence of several distinct projects.

As an essential part of the study, we plan to meet with practitioners to discuss various definitions of "acceptability," leaving open the possibility that different concrete problems call for different notions. Some specific issues that could be singled out include funding liquidity, transfer pricing, and the interest of diversification for a firm in the presence of regulation.

Philippe Artzner as Visiting Research Professor. Research will continue by other means. Online available are 22 slides used on Nov. Last update: March 16, Existing modelling instruments allow for a relatively good measurement of market risks of trading books over relatively small time intervals. These models, however, have some severe deficiencies if they are applied to longer time periods.

In this project we investigate models that are proposed to be used to model the evolution of risk factors. We test these models on real data by backtesting expected shortfall predictions. Contact at Swiss Re :. Contact at Credit Suisse Group :. Marco Finardi. Strategic Long-Term Financial Risks final report including slides.

Last update: March 15, Credit Risk Portfolio Models Taking into account recent insights into the means of modeling dependence structures see Modeling Dependent Defaults by R. Frey and A. Gordy , this project plans to determine how to design a credit portfolio model integrated in the existing market and credit risk models used by financial institutions.

Some particular issues to be addressed are: design of a credit portfolio model and comparison with other models used to compute economic capital, comparison with intensity-based models, common treatment of different products such as loans and OTC derivatives, dependence between market and credit risk factors, use of derivatives to mitigate eposure.

Giovanni Cesari and Dr. Giovanni Cesari. Harry Stordel. Stephan Schreckenberg. Last update: March 2, Banks' Optimal Hedging Decisions Under the Threat of Bank Runs This project will analyze a bank's risk management decision when the bank is financed with deposits and equity with limited liability.

The bank is subject to the risk of depositor runs. Bankruptcy costs and regulatory restrictions are the primary motivations for risk management. Developing a model similar to Froot and Stein , the optimal hedging decision will first be studied in a one-period framework. As a second step, it is planned to study the optimal hedging decision in a dynamic framework. Due to superior information, the bank earns a rent from its assets as well as from its deposits.

These rents constitute the bank's franchise value. Optimal hedging strategies in the multi-period case will then be derived taking the loss of the franchise value into account in the case of bank runs. Wolfgang Bauer and Prof. Christian A. Last update: January 29, But modelling retail loans is a more important issue for loan portfolio management purposes and risk capital allocation at large retail and middle market banks like Credit Suisse , as well as in view of the upcoming changes in the regulatory environment.

The data situation lack of long historic time series necessitates other approaches than using market data, and the modelling approaches currently used e. Especially when concerned with dependence problems, there is a need to introduce macro-economic conditions into the model or linking it with market risk. The project aims to close this gap and make a potentially big contribution to modelling credit risk, also closing a gap between science and practitioners.

Research focus areas are: Proper dependence modelling versus simply using linear correlations , integrating the problems arising from the nature and data of retail portfolios. Modelling of credit rating migration and recoverables with proper dependence. Urs Wolf and Vlatka Komaric. Giovanni Cesari , Dr. Alexandre Kurth and Dr.

Armin Wagner. Last update: March 29, Capital Allocation under Regulatory Constraints Using a stylised model similar to that of Froot and Stein the project will focus on the capital budgeting, capital structure, and risk management decisions of financial firms under regulatory constraints. The questions and issues that will be addressed in this framework are the following: The introduction of the regulatory constraints as an additional rationale for risk management, as well as the analysis of how the investment, hedging, and capital budgeting decisions of financial firms are influenced by such an introduction.

A comparison of the influence of different forms of regulatory constraints, such as the internal models and pre-commitment approach. In the pre-commitment approach, the effectiveness of various penalty schemes could also be analysed. A comparison of the influence of firm-wide regulatory constraints and separate constraints applied to trading and banking books individually.

The explicit introduction of a shareholder-utility maximization concept to formally study capital budgeting as opposed to custom-designed methods such as RAROC or EVA. Last update: March 7, Combined Market and Credit Risk Stress Testing Various crisis events have demonstrated the need for financial institutions such as banks and insurance companies to perform scenario analysis under stress conditions. This goes beyond the VaR framework, which is the standard tool to estimate losses within large but not extreme market movements.

Usually these methods are applied within a market risk environment only. Credit spreads as one of the market risk drivers are captured within the VaR framework, but no other credit effects such as default probabilities, default correlations, and recovery rates are taken into account. The goal of this project is the development of a theoretically well-understood and empirically founded conceptual framework for a combined market and credit risk stress test methodology.

The project can be divided into the following steps: Review of the literature and evaluation of existing approaches to link VaR with Credit VaR and with combined stress testing EVT or conventional Define methodology for a combined market and credit risk stress test.

The project will be carried out in close co-operation with the industry in order to obtain solutions whose practical implementation within a sophisticated integrated risk-management system appears feasible. Duffie and K. Christian Hauswirth. Investigation of the Market Price of Credit Risk for the Swiss Bond Market Credit risk plays an important role in the valuation and risk management of most financial contracts.

This leads to a great interest in problems of valuing credit risk - from a theoretical and practical point of view. In the nineties so-called intensity based or reduced form models have been developed, which assume that at each instant there is some probability that a firm defaults on its obligations. Duffie and Singleton , follow this approach. Duffie tested the model for US corporations. The aim of this project is to examine how well the model of Duffie and Singleton describes corporate bond yields for the Swiss bond market and how the spread can be separated into the expected and unexpected loss.

In a first step, the risk-free term structure on the basis of Swiss treasury bonds is estimated with a two-factor CIR model. Based on these results we estimate the instantaneous probability of default which follows a translated single factor square-root diffusion process, with a modification that allows for the default process to be correlated with the factors driving the default-free term structure.

Bewertung von Kreditrisiken - empirische Untersuchungen am Schweizer Kapitalmarkt. Last update: October 9, Rules of Capital Allocation CAPA and Coherent Measures of Risk The project will compare classical as well as newer allocation rules, and evaluate their consequences for portfolio choices and strategies, performance measurement, compensation schemes. Then, we want to investigate the following principal topics: It is important to first extend the theory of coherent risk measures to the multi-period case, and to compare the «rolling-over» of short-term risk measurements with a fixed long-term horizon risk measurement, then one has to systematically review the different capital allocation rules.

It will be useful to describe how they are built out of risk measures and to distinguish those implying coherent risk measures from the others, given the multi-period framework and the construction of capital allocation rules via coherent risk measures, it will then be possible to detect how and why a specific rule is good at one or several of the applications mentioned above.

The following researchers, working on the same line of ideas, have contact with us: Dr. Michel Denault , Elisabeth Maignan , Dr. Please contact Prof. Glossary Courses. Popular markets guides. Shares trading guide Commodities trading guide Forex trading guide Cryptocurrency trading guide Indices trading guide ETFs trading guide.

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What you need to know about RiskLab. GME Swap Short:. Trade now. AAPL GOOG TSLA RiskMetrics What is RiskMetrics? Want to learn more about CFD trading?

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