Loriano Mancini

Liquidity in the Foreign Exchange Market: Measurement, Commonality, and Risk Premiums

We provide the first systematic study of liquidity in the foreign exchange market. We find significant variation in liquidity across exchange rates, substantial illiquidity costs, and strong commonality in liquidity across currencies and with equity and bond markets. Analyzing the impact of liquidity risk on carry trades, we show that funding (investment) currencies offer insurance against (exposure to) liquidity risk. A liquidity risk factor has a strong impact on carry trade returns from 2007 to 2009, suggesting that liquidity risk is priced. We present evidence that liquidity spirals may trigger these findings.

The Term Structure of Variance Swaps and Risk Premia

We study the term structure of variance swaps, which are popular volatility derivative contracts. A model-free analysis reveals a significant jump risk component embedded in variance swaps. The variance risk premium is negative and has a downward sloping term structure. Variance risk premia due to negative jumps present similar features in quiet times but have an upward sloping term structure in turbulent times. This suggests that shortterm variance risk premia mainly reflect investors’ fear of a market crash. Theoretically, the Expectation Hypothesis does not hold, but biases and inefficiencies are modest for short time horizons. A simple trading strategy with variance swaps generates significant returns.

Option pricing with model-guided nonparametric methods

Parametric option pricing models are widely used in finance. These models capture several features of asset price dynamics; however, their pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches that learn and correct empirically the pricing errors. In this article we propose a new nonparametric method for pricing derivatives assets. Our method relies on the state price distribution instead of the state price density, because the former is easier to estimate nonparametrically than the latter. A parametric model is used as an initial estimate of the state price distribution. Then the pricing errors induced by the parametric model are fitted nonparametrically. This model-guided method, called automatic correction of errors (ACE), estimates the state price distribution nonparametrically. The method is easy to implement and can be combined with any model-based pricing formula to correct the systematic biases of pricing errors. We also develop a nonparametric test based on the generalized likelihood ratio to document the efficacy of the ACE method. Empirical studies based on S&P 500 index options show that our method outperforms several competing pricing models in terms of predictive and hedging abilities.

A Tale of Two Investors: Estimating Optimism and Overconfidence

We estimate investors’ sentiment from option and stock prices by anchoring objective beliefs to a neoclassical pricing kernel. Our estimates of sentiment correlate well with other sentiment measures such as the Baker–Wurgler index, the Yale/Shiller crash confidence index and the Duke/CFO survey responses, and yet contain additional information. Our analysis points out three significant issues related to overconfidence. First, the Baker–Wurgler index strongly reflects excessive optimism but not overconfidence. Second, overconfidence drives the pricing kernel puzzle. Third, the dynamics of optimism and overconfidence generate a perceived negative risk-return relationship, while objectively the relationship is positive. Optimism and overconfidence about market returns co-move together, inflating asset prices in good times and exacerbating market crashes in bad times.

Sentiment, Asset Prices, and Systemic Risk

Regulators charged with monitoring systemic risk need to focus on sentiment as well as narrowly defined measures of systemic risk. This chapter describes techniques for jointly monitoring the co-evolution of sentiment and systemic risk. To measure systemic risk, we use Marginal Expected Shortfall. To measure sentiment, we apply a behavioral extension of traditional pricing kernel theory, which we supplement with external proxies. We illustrate the technique by analyzing the dynamics of sentiment before, during, and after the global financial crisis which erupted in September 2008. Using stock and options data for the S&P 500 during the period 2002–2009, our analysis documents the statistical relationship between sentiment and systemic risk.

Robust Value at Risk Prediction: Appendix

This appendix extends simulation and empirical results reported in Mancini and Trojani (2010). It discusses the choice of the robustness tuning constants; describes the unconditional, independence and conditional coverage tests for VaR forecast evaluation; provides additional Monte Carlo simulation results on GARCH model estimation and VaR prediction; extends the empirical analysis on backtesting. Notation is the same as in Mancini and Trojani (2010).

Handbook on Systemic Risk: Systemic Risk and Sentiment

Abstract Regulators charged with monitoring systemic risk need to focus on sentiment as well as narrowly defined measures of systemic risk. This chapter describes techniques for jointly monitoring the co-evolution of sentiment and systemic risk. To measure systemic risk, we use Marginal Expected Shortfall. To measure sentiment, we apply a behavioral extension of traditional pricing kernel theory, which we supplement with external proxies. We illustrate the technique by analyzing the dynamics of sentiment before, during, and after the global financial crisis which erupted in September 2008. Using stock and options data for the SP JEL Codes : E61, G01, G02, G28 Introduction The report of the Financial Crisis Inquiry Commission (FCIC, 2011) emphasizes the importance of systemic risk and sentiment. These two concepts, and the relationship between them, are important for regulatory bodies such as the Financial Stability Oversight Council (FSOC) who, with the support of the Office of Financial Research (OFR), is charged with the responsibility for monitoring systemic risk throughout the financial system. This chapter describes tools regulators can use to monitor sentiment and its impact on systemic risk.

Internet Appendix for 'Liquidity in the Foreign Exchange Market: Measurement, Commonality, and Risk Premiums'

This supplemental appendix extends the results in Mancini, Ranaldo, and Wrampelmeyer (2011), presenting additional analyses and robustness checks. It also describes the cleaning procedure of the EBS data, compares EBS to other datasets, and discusses the robust estimation of the price impact model.

Transitory Versus Permanent Shocks: Explaining Corporate Savings and Investment

Theory has recently shown that corporate policies should respond differently to permanent or transitory cash flow shocks. We devise a novel filter to decompose cash flow shocks into permanent and transitory components. The policy choices of large publicly traded U.S. firms, such as cash holdings, credit line usage, and equity issuance, are related to the characteristics of the shocks estimated by our filter, i.e., volatilities, correlation and drift rates of the permanent and transitory shocks, as predicted by theory. Moreover, the interaction between the permanent and transitory cash flow shocks is strongly related to a firm’s leadership status within its industry.

Detecting Abnormal Trading Activities in Option Markets: Supplemental Appendix

This supplemental appendix extends the empirical results in the main paper. Abnormal trading activities on call and put options are analyzed for 19 companies in the banking and insurance sectors from January 1996 to September 2009. Our empirical findings suggest that certain events such as the takeovers of AIG and Fannie Mae/Freddie Mac, the collapse of Bear Stearns Corporation and public announcements of large losses/writedowns are preceded by abnormal trading activities in call and put options. The realized gains amount to several hundreds of millions of dollars. Several cases are discussed in detail.