Realistic volatility risk premia

The volatility risk premium compensates investors for taking volatility risk. Conceptually it is based on the difference between options-implied and expected realized volatility. In equity markets this premium should be positive in the long run and fluctuate overtime depending on the market’s willingness to pay for protection against future changes in price volatility. In practice, measuring the premium overtime is challenging, particularly because expected realized volatility is not known. Using recent realized volatility as a proxy can be highly misleading. However, a realistic estimate can be constructed by considering the trade-off between timeliness and noise ratio of recent price changes and the long-term mean reversion of volatility. This “realistic” volatility risk premium has been positively correlated with subsequent daily volatility index future returns.

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How systemic financial risk is measured

Public institutions have developed a wide range of methods to track systemic financial risk. What most of them have in common is reliance on financial market data. This implies that systemic risk indicators typically only show what the market has already priced, in form of correlation, volatility or value. They cannot anticipate market crises. Their main use is to predict when and how market turmoil begins to sap the functioning of the financial system. Some methods may be useful for macro trading. For example, Conditional Value-at-Risk can identify sources of systemic risk, such as specific institutions or market segments. Principal Components Analysis can indicate changing concentration of risk across securities and markets.

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How convenience yields have compressed real interest rates

Real interest rates on ‘safe’ assets such as high-quality government bonds had been stationary around 2% for more than a century until the 1980s. Since then they have witnessed an unprecedented global decline, with most developed markets converging on the U.S. market trend. There is evidence that this trend decline and convergence of real rates has been due prominently to rising convenience yields of safe assets, i.e. greater willingness to pay up for  safety and liquidity. This finding resonates with the historic surge in official foreign exchange reserves, the rising demand for high-quality liquid assets for securitized transactions and the preferential treatment of government bonds in capital and liquidity regulation (view previous post here).

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Variance term premia

Variance term premia are surcharges on traded volatility that compensate for bearing volatility risk in respect to underlying asset prices over different forward horizons. The premia tend to increase in financial market distress and decrease in market expansions. Variance term premia have historically helped predicting returns on various equity volatility derivatives. The premia themselves can be estimated based on variance swap forward rates and their decomposition into expected underlying price variance and risk premia. In particular, the variance term premia are obtained as the difference between forward swap rates and realized volatility forecasts, whereby the latter are related to a “volatility state vector”.

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How regulatory reform shapes the financial cycle

Ambitious regulatory reform has changed the dynamics of the global financial system. Capital ratios of banks have increased significantly, reining in bank credit. Counter-cyclical bank capital rules slow credit expansions by design and yield greater influence to non-banks. Meanwhile, the liquidity coverage ratio has restricted one of the key functions of banks: liquidity transformation. Regulation has also created its own moral hazards. In particular, the preferential treatment of government bonds has boosted their share in bank assets. The neglect of sovereign risk in liquidity regulation constitutes a significant systemic risk as public debt-to-GDP ratios are at or near record highs in many key economies.

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The predictability of market-wide earnings revisions

Forward earnings yields are a key metric for the valuation of an equity market. Helpfully, I/B/E/S and DataStream publish forward earnings forecasts of analysts on a market-wide index basis. Unfortunately, updates of these data are delayed by multiple lags. This can make them inaccurate and misleading in times of rapidly changing macroeconomic conditions. Indeed, there is strong empirical evidence that equity index price changes predict future forward earnings revisions significantly and for all of the world’s 25 most liquid local equity markets. This predictability can be used to enhance the precision of real-time earnings yield data and avoid misleading trading signals.

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Multiple risk-free interest rates

Financial markets produce more than one risk-free interest rate. This is because there are several separate market segments where structured trades replicate such a rate. Differences in remuneration arise for two reasons. First, financial frictions can prevent arbitrage. Second, some risk-free assets pay additional convenient yields, typically by virtue of their liquidity and suitability as collateral. Put simply some “safe assets” have value beyond return. U.S. government bonds, in particular, seem to provide a sizable consistent convenience yield that tends to soar in crisis. This suggests that there are arbitrage opportunities for investors that are flexible, impervious to convenience yields and tolerant towards temporary mark-to-market losses.

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How lazy trading explains FX market puzzles

Not all market participants respond to changing conditions instantaneously, not even in the FX market. Private investors in particular can take a long while to adapt to changes in global interest rate conditions and even institutional investors may be constrained by rules and lengthy process. A theoretical paper shows that delayed trading goes a long way in explaining many empirical puzzles in foreign exchange markets, i.e. deviations from the rational market equilibrium, such as the delayed overshooting puzzle or the forward discount puzzle. Understanding these delays and their effects offers profit opportunities for flexible information-efficient traders.

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A brief history of quantitative equity strategies

Understanding quantitative equity investments means understanding a significant portion of market positions. Motivated by the apparent failure of the capital asset pricing model and the efficient market hypothesis, a large share of equity investors follows stylized “factors” that are expected to outperform the market portfolio in the long run. Yet, popularity and past performance of such factors can be self-defeating, while published research is prone to selection bias and overfitting. Big data has introduced greater information efficiency with respect to textual analysis, picking up short-term sentiment but without clear and documented benefits for long-term investment so far. In the future greater emphasis may be placed on dynamic factor models that – in principle – can apply plausible performance factors at the times that they matter.

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Predicting equity volatility with return dispersion

Equity return dispersion is measured as the standard deviation of returns across different stocks or portfolios. Unlike volatility it can be measured even for a single relevant period and, thus, can record changing market conditions fast. Academic literature has shown a clear positive relation between return dispersion, volatility and economic conditions. New empirical research suggests that return dispersion can predict both future equity return volatility and equity premia. The predictive relation has been non-linear, suggesting that it is the large changes in dispersion that matter.

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