Wystup fx options and structured products


More video on topic «Wystup fx options and structured products»

Dr Taylor Spears is a research fellow in the Sociology of Financial Modelling at the School of Social and Political Science in the University of Edinburgh.

The Thalesians

Using examples from portfolio management and quantitative trading we illustrate the power and flexibility of conic programming. We point to various common mistakes in setting up portfolio models and in solving them algorithmically. Several Python code fragments are given.

Exchange Rate. Money Management

SIMM and sensitivity based FRTB: double AD - Algorithmic Differentiation and computation of sensitivities - First AD: fast inputs for SIMM/FRTB - Second AD: sensitivity of the IM/Capital itself . sensitivities - Second AD applications: attribution and marginal IM/Capital

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We are named after Thales of Miletus (Θαλῆς ὁ Μιλήσιος), a pre-Socratic Greek philosopher who lived in ca. 679 BC-ca. 596 BC. Thales was a mathematician and is familiar to many secondary school students for one of his theorems in geometry.

• What is cointegration? • How to test for cointegration? • What is pairs trading? • How to find cointegrated pairs? • How to generate a tradeable signal?

Robin Dale Hanson is an associate professor of economics at George Mason University and a research associate at the Future of Humanity Institute of Oxford University. He is known as an expert on idea futures and markets, and he was involved in the creation of the Foresight Exchange and DARPA's FutureMAP project. He invented market scoring rules like LMSR (Logarithmic Market Scoring Rule)used by prediction markets such as Consensus Point (where Hanson is Chief Scientist), and has conducted research on signaling.

Many speakers who have also spoken at the Thalesians will be speaking, including Uwe Wystup and Attilio Meucci. Many other well known figures such as Bruno Dupire will also be addressing the conference.

Managed portfolios that take less risk when volatility is high produce large alphas, increase Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, and investment factors, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in volatility are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions yet still earns high average returns. This rules out typical risk-based explanations and is a challenge to structural models of time-varying expected returns.

We find that an increase in the "unusualness" of news with negative sentiment predicts an increase in stock market volatility. Our analysis is based on over 865,555 articles on 55 large financial companies, mostly banks and insurers, published in 6996--7569. We find that the interaction between measures of unusualness and sentiment forecasts volatility at both the company-specific and aggregate level. These effects persist for several months. The pattern of response of volatility in our aggregate analysis is consistent with a model of rational inattention among investors.


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