Contributions to risk-sensitive asset management

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Jump-Diffusion Risk-Sensitive Asset Management I: Diffusion Factor Model

This paper considers a portfolio optimization problem in which asset prices are represented by SDEs driven by Brownian motion and a Poisson random measure, with drifts that are functions of an auxiliary diffusion factor process. The criterion, following earlier work by Bielecki, Pliska, Nagai and others, is risk-sensitive optimization (equivalent to maximizing the expected growth rate subject to a constraint on variance.) By using a change of measure technique introduced by Kuroda and Nagai we show that the problem reduces to solving a certain stochastic control problem in the factor process, which has no jumps. The main result of the paper is to show that the risk-sensitive jump diffusion problem can be fully characterized in terms of a parabolic Hamilton-Jacobi-Bellman PDE rather than a PIDE, and that this PDE admits a classical C^{1,2} solution.Comment: 33 page

The Swiss black swan bad scenario: is Switzerland another casualty of the Eurozone crisis

Financial disasters to hedge funds, bank trading departments and individual speculative traders and investors seem to always occur because of non-diversification in all possible scenarios, being overbet and being hit by a bad scenario. Black swans are the worst type of bad scenario: unexpected and extreme. The Swiss National Bank decision on January 15, 2015 to abandon the 1.20 peg against the euro was a tremendous blow for many Swiss exporters, but also Swiss and international investors, hedge funds, global macro funds, banks as well as the Swiss central bank. In this paper we discuss the causes for this action, the money losers and the few winners, what it means for Switzerland, Europe and the rest of the world, what kinds of trades lost and how they have been prevented

Some historical perspectives on the Bond-Stock Earnings Yield Model for crash prediction around the world

We provide a historical perspective focusing on Ziemba's experiences and research on the bond-stock earnings yield differential model (BSEYD) starting from when he first used it in Japan in 1988 through to the present in 2014. The model has called many but not all crashes. Those called have high interest rates in long term bonds relative to the trailing earnings to price ratio. In general, when the model is in the danger zone, almost always there will be a crash. The model predicted the crashes in China, Iceland and the US in the 2006-9 period. Iceland had a drop of fully 95%. For the US the call was on June 14, 2007 and the stock market fell 56.8%. A longer term study for the US, Canada, Japan, Germany, and UK shows that over long periods being in the stock market when the bond-stock signal is not in the danger zone and in cash when it is in the danger zone provides a final wealth about double buy and hold for each of these five countries. The best use of the model is for predicting crashes. Finally we compare Shiller's high PE ratio crash model to the BSEYD model for the US market from 1962-2012. While both models add value, the BSEYD model predicts crashes better

How to lose money in derivatives: examples from hedge funds and bank trading departments

What makes futures hedge funds fail? The common ingredient is over betting and not being diversified in some bad scenarios that can lead to disaster. Once troubles arise, it is difficult to take the necessary actions that eliminate the problem. Moreover, many hedge fund operators tend not to make decisions to minimize losses but rather tend to bet more doubling up hoping to exit the problem with a profit. Incentives, including large fees on gains and minimal penalties for losses, push managers into such risky and reckless behavior. We discuss some specific ways losses occur. To illustrate, we discuss the specific cases of Long Term Capital Management, Niederhoffer’s hedge fund, Amaranth and Société Genéralé. In some cases, the failures lead to contagion in other hedge funds and financial institutions. We also list other hedge fund and bank trading failures with brief comments on them

Does the bond-stock earning yield differential model predict equity market corrections better than high P/E models?

In this paper, we extend the literature on crash prediction models in three main respects. First, we relate explicitly crash prediction measures and asset pricing models. Second, we present a simple, effective statistical significance test for crash prediction models. Finally, we propose a definition and a measure of robustness for crash prediction models. We apply the statistical test and measure the robustness of selected model specifications of the Price-Earnings (P/E) ratio and Bond Stock Earning Yield Differential (BSEYD) measures. This analysis suggests that the BSEYD, the logarithmic BSEYD model, and to a lesser extent the P/E ratio, are statistically significant robust predictors of equity market crashes

A tale of two indexes: predicting equity market downturns in China

Predicting stock market crashes is a focus of interest for both researchers and practitioners. Several prediction models have been developed, mostly for use on mature financial markets. In this paper, we investigate whether traditional crash predictors, the price-to-earnings ratio, the Cyclically Adjusted Price-to-Earnings ratio and the Bond-Stock Earnings Yield Differential model, predicts crashes for the Shanghai Stock Exchange Composite Index and the Shenzhen Stock Exchange Composite Inde

Using a mean changing stochastic processes exit-entry model for stock market long-short prediction

Stochastic processes is one of the key operations research tools for analysis of complex phenomenon. This paper has a unique application to the study of mean changing models in stock markets. The idea is to enter and exit stock markets like Apple Computer and the broad S&P500 index at good times and prices (long and short). Research by Chopra and Ziemba showed that mean estimation was far more important to portfolio success than variance or co-variance estimation. The idea in the stochastic process model is to determine when the mean changes and then reverse the position direction. This is applied to Apple Computer stock in 2012 when it rallied dramatically then had a large fall and Apple Computer and the S&P500 in the 2020 Covid-19 era. The results show that the mean changing model greatly improves on a buy and hold strategy even for securities that have has large gains over time but periodic losses which the model can exploit. This type of model is also useful to exit bubble-like stock markets and a number of these in the US, Japan, China and Iceland are described. An innovation in this paper is the exit entry long short feature which is important in financial markets