(Fuente: Bespoke Investment Group)
Posts Tagged ‘Equity
17
Oct
11
Tabla du Jour: Sin palabras…
03
Oct
11
Paper: Equity Yields
Equity Yields
Abstract
We study a new data set of prices of traded dividends with maturities up to 10 years across three world regions: the US, Europe, and Japan. We use these asset prices to construct equity yields, analogous to bond yields. We decompose these yields to obtain a term structure of expected dividend growth rates and a term structure of risk premia, which allows us to decompose the equity risk premium by maturity. We find that both expected dividend growth rates and risk premia exhibit substantial variation over time, particularly for short maturities. In addition to predicting dividend growth, equity yields help predict other measures of economic growth such as consumption growth. We relate the dynamics of growth expectations to recent events such as the financial crisis and the earthquake in Japan.
Link al Paper
Detection of Crashes and Rebounds in Major Equity Markets
Financial markets are well known for their dramatic dynamics and consequences that affect much of the world’s population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framework to understand and diagnose financial bubbles from rational expectations and was recently extended to negative bubbles and rebounds. Using the JLS model, we develop an alarm index based on an advanced pattern recognition method with the aim of detecting bubbles and performing forecasts of market crashes and rebounds. Testing our methodology on 10 major global equity markets, we show quantitatively that our developed alarm performs much better than chance in forecasting market crashes and rebounds. We use the derived signal to develop elementary trading strategies that produce statistically better performances than a simple buy and hold strategy.
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31
Mar
11
Paper: VaR y correlación
Implied correlation from VaR
Abstract
Value at risk (VaR) is a risk measure that has been widely implemented by financial institutions. This paper measures the correlation among asset price changes implied from VaR calculation. Empirical results using US and UK equity indexes show that implied correlation is not constant but tends to be higher for events in the left tails (crashes) than in the right tails (booms).
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02
Mar
11
Paper: Modelos HFT
Developing High-Frequency Equities Trading Models
Link al Paper
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UPDATE
Llegamos a este paper via Quantivity, uno de los co-autores es un argentino suelto en NY, esperamos que cuando este en Buenos Aires y tenga tiempo nos pueda presentar su trabajo sobre HFT.
23
Feb
11
Screening de Acciones (bis)
Empirical Finance Blog lanzo un producto gratuito (por lo menos en su version beta) para hacer screening.
The Hazards of Volatility Diversification
Abstract:
Recent research advocates volatility diversification for long equity investors. It can even be justified when short-term expected returns are highly negative, but only when its equilibrium return is ignored. Its advantages during stock market crises are clear but we show that the high transactions costs and negative carry and roll yield on volatility futures during normal periods would outweigh any benefits gained unless volatility trades are carefully timed. Our analysis highlights the difficulty of predicting when volatility diversification is optimal. Hence insitutional investors should be sceptical of studies that extol its benefits. Volatility is better left to experienced traders such as speculators, vega hedgers and hedge funds.
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UPDATE
Falkenblog tiene un post donde comenta este paper.
(Fuente VisualizingEconomics.com, via FT Aphaville)
The Information Content of High-Frequency Data for Estimating Equity Return Models and Forecasting Risk
Abstract
We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted from high-frequency data. For this purpose, we introduce asymptotically exact volatility measurement equations in state space form and propose a Bayesian estimation approach. Our highly efficient estimates lead in turn to substantial gains for forecasting various risk measures at horizons ranging from a few days to a few months ahead when taking also into account parameter uncertainty. As a practical rule of thumb, we find that two years of high frequency data often suffice to obtain the same level of precision as twenty years of daily data, thereby making our approach particularly useful in finance applications where only short data samples are available or economically meaningful to use. Moreover, we find that compared to model inference without high-frequency data, our approach largely eliminates underestimation of risk during bad times or overestimation of risk during good times. We assess the attainable improvements in VaR forecast accuracy on simulated data and provide an empirical illustration on stock returns during the financial crisis of 2007-2008.
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Are You Trading Predictably?
Abstract:
Over the post-decimalization period, we find a predictable pattern of return continuation in equities. Stocks whose relative returns are high in a given half-hour interval today tend to exhibit similar outperformance in the same half-hour period on subsequent days. The effect is stronger at the beginning and end of the trading day, but exists throughout the day. Percentage changes in trading volume exhibit a similar pattern, but do not explain the return pattern. These results suggest that strategically shifting the timing of trades can significantly reduce execution costs for institutional traders.
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Quantitative Finance Club 