Predicting recovery rates using logistic quantile regression with bounded outcomes

Logistic quantile regression (LQR) is used for studying recovery rates. It is developed using monotone transformations. Using Moody’s Ultimate Recovery Database, we show that the recovery rates in different partitions of the estimation sample have different distributions, and thus for predicting recovery rates, an error-minimizing quantile point over each of those partitions is determined for LQR. Using an expanding rolling window approach, the empirical results confirm that LQR with the error-minimizing quantile point has better and more robust out-of-sample performance than its competing alternatives, in the sense of yielding more accurate predicted recovery rates. Thus, LQR is a useful alternative for studying recovery rates.     

Measuring currency exposure with quantile regression

In this paper, we explore an alternative explanation of the exposure puzzle, the missing variable bias in previous studies. We propose to correct the bias with the quantile regression technique invented by Koenker and Bassett (Econometrica 46:33–51, 1978). Empirically, as soon as we take into account the missing variable bias as well as time variation in currency exposure, we find that 26 out of 30 or 87 % of the US industry portfolios exhibit significant currency exposure to the Major Currencies Index, and 23 out of 30 or 77 % show significant exposure to the Other Important Trading Partners Index. Our results have important theoretical and practical implications. In terms of theoretical significance, our results strengthen the findings in Francis et al. (J Financ Econ 90:169–196, 2008), and suggest that methodological weakness, not hedging, may explain the insignificance of currency risk in previous studies. In terms of practical significance, our results suggest a simple yet efficient approach for managers to estimate currency exposure of their firms.     

An application of two-stage quantile regression to insurance ratemaking

Two-part models based on generalized linear models are widely used in insurance rate-making for predicting the expected loss. This paper explores an alternative method based on quantile regression which provides more information about the loss distribution and can be also used for insurance underwriting. Quantile regression allows estimating the aggregate claim cost quantiles of a policy given a number of covariates. To do so, a first stage is required, which involves fitting a logistic regression to estimate, for every policy, the probability of submitting at least one claim. The proposed methodology is illustrated using a portfolio of car insurance policies. This application shows that the results of the quantile regression are highly dependent on the claim probability estimates. The paper also examines an application of quantile regression to premium safety loading calculation, the so-called Quantile Premium Principle (QPP). We propose a premium calculation based on quantile regression which inherits the good properties of the quantiles. Using the same insurance portfolio data-set, we find that the QPP captures the riskiness of the policies better than the expected value premium principle.     

Diversification and risk-adjusted performance: A quantile regression approach

The effect of diversification on firm performance has been debated. We reexamine the effect using a sample of 44,248 observations of non-financial US firms for the 1997–2009 period employing the quantile regression approach. Our empirical results show that the effect of diversification on firm performance is not homogeneous across various quantile levels: the diversification discount (premium) shows up in firms with high (low) RoE quantiles. Further, we find that diversification affects firm risk as well. Therefore, we consider a risk-adjusted performance measure and find that both diversification discount and premium disappear, which is consistent with the risk-return trade-off principle.     

Value at risk estimation by quantile regression and kernel estimator

Risk management has attracted a great deal of attention, and Value at Risk (VaR) has emerged as a particularly popular and important measure for detecting the market risk of financial assets. The quantile regression method can generate VaR estimates without distributional assumptions; however, empirical evidence has shown the approach to be ineffective at evaluating the real level of downside risk in out-of-sample examination. This paper proposes a process in VaR estimation with methods of quantile regression and kernel estimator which applies the nonparametric technique with extreme quantile forecasts to realize a tail distribution and locate the VaR estimates. Empirical application of worldwide stock indices with 29 years of data is conducted and confirms the proposed approach outperforms others and provides highly reliable estimates.     

Financialization and de-financialization of commodity futures: A quantile regression approach

This study employs a quantile regression approach to examine the financialization of commodity futures. We confirm a strong degree of dependence in energy commodities from 2004 to 2013, with moderate effects in metals and lesser magnitudes in agriculture. Our findings show a strengthening in the financialization of energy commodities during the 2008–2009 global financial crisis, while there were weaker effects in agriculture and a decoupling or de-financialization in metal markets. The findings reveal the de-financialization of metals and agricultural markets from 2014 to 2017, after the 2013 closure of commodity trading units on Wall Street. Overall, our findings cast doubt on the diversification benefits of energy-dominated commodity indices after 2013. We argue the impact of financialization on commodity futures markets is more permanent than previously thought.     

The structure and degree of dependence: A quantile regression approach

The copula function defines the degree of dependence and the structure of dependence. This paper proposes an alternative framework to decompose the dependence using quantile regression. We demonstrate that the methodology provides a detailed picture of dependence including asymmetric and non-linear relationships. In addition, changes in the degree or structure of dependence can be modeled and tested for each quantile of the distribution. The empirical part applies the framework to three different sets of financial time-series and demonstrates substantial differences in dependence patterns among asset classes and through time. The analysis of 54 global equity markets shows that detailed information about the structure of dependence is crucial to adequately assess the benefits of diversification in normal times and crisis times.     

Financial market interdependencies: A quantile regression analysis of volatility spillover

This paper investigates the degree and structure of interdependence between emerging (Asian and Latin American) and developed (USA and Japan) stock markets through the study of volatility spillovers for the period spanning from January 1, 1993 to October 13, 2010. Using both standard GARCH model and quantile regression approach, we find the evidence of significant interdependence between financial markets which may give evidence of volatility transmission. The volatility transmission is closely associated with geographical proximity as well as with crisis periods which confirm the presence of contagion. The analysis of upper and lower quantiles allows observing that the interdependence increases during bullish markets while decreases during bearish markets. Accordingly, the structure of interdependence is asymmetric for both Asian and Latin American emerging markets. These findings open up new insights for government policy makers as well as for managerial purposes.     

Demand for international reserves in developing nations: A quantile regression approach

I estimate the determinants of the demand for international reserves using quantile regressions. Employing a dataset of 108 developing nations over the period of 1980-2007, I find considerable differences at different points of the conditional distribution of reserves. The estimates of elasticities that were found to be insignificant in ordinary least squares regressions become statistically significant at various quantiles of the distribution. Furthermore, majority of the explanatory variables have lower coefficients for the nations that are located on the right tail of the conditional reserve distribution. This finding shows that the level of reserves is as important as the other determinants of the demand for reserves.     

Does investor sentiment predict bitcoin return and volatility? A quantile regression approach

The study investigates hypotheses relating to the effect of investor sentiment on predicting bitcoin returns and volatility. Using moments quantile regression, we present robust empirical evidence for the period 2017–2021. Our findings demonstrate that investor interest and emotions are significant predictors of bitcoin returns and volatility, while VIX and Bitcointalk.org forum are the most suitable predictors for representing investor emotions and interest, respectively. The findings also indicate a nonlinear relationship between investor sentiment and bitcoin returns and volatility, with predictable power changing based on the market conditions. Thus, the study enriches existing literature by providing empirical evidence to affirm the viability of behavioral finance theories in the bitcoin market and complements investors with more information to seek profits in different market conditions.     

Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach

We construct the complete network of tail risk spillovers among major cryptocurrencies using the Least Absolute Shrinkage and Selection Operator (LASSO) quantile regression. We capture important features of the network, including major risk-driving and major risk-receiving currencies, and the evolution of the tail dependence among the currencies over time. Importantly, we reveal a striking finding that the right tail dependence among the cryptocurrencies is significantly stronger than the left tail counterpart. This unique characteristic may have contributed to the rise in popularity of cryptocurrencies over the last few years. Our portfolio analysis reveals that diversification in cryptocurrency investment can be accomplished simply by employing the naïve equal-weighted scheme even when transaction costs are taken into account.     

风电走出低谷抓手

经历了前两年发展低谷期的国内风电行业,或将在2014年迎来逐步回暖的新局面。然而,作为中国新能源行业领军者,华锐风电科技（集团）股份有限公司却在新年伊始,接到了公司被证监会立案调查的公告,经历裁员、盈利困难、换帅等风波后,这一风电行业的传奇企业再次成为了社会的焦点。     

Monetary environments and stock returns: International evidence based on the quantile regression technique

This paper investigates the impact of the local and the US monetary policy environments on stock returns at the different locations on the return distributions. Using data for stock returns and interest rates of 30 countries, the quantile regression technique is employed to estimate the sensitivity of the returns to monetary policy at the different points on the return distributions. The results suggest that higher returns are associated with expansionary monetary policy. Furthermore, some of the stock markets in the sample are found to react to the local, but not the US monetary environments at the lower quantiles while the response at the higher quantiles appears to be sensitive to the US, but not the local monetary conditions. These findings are further supported by the slope equality tests, discussed in Koenker & Bassett (1982), and the analysis of weighted absolute residuals (ANOWAR), proposed by Chen, Ying, Zhang, & Zhao (2008).     

On the volatility spillover between lslamic and conventional stock markets: A quantile regression analysis

This paper aims at analyzing the degree and structure of interdependencies in terms of volatility (transmission, contagion) between Islamic and conventional stock markets on calm periods and at times of financial fragility and crisis. We focused on the recent financial instability periods and used the Quantile Regression-based GARCH model. Main results lead to very interesting conclusions. First, it has been found that Islamic stock markets are not totally immune to the global financial crisis. Second, a very strong interdependence is sensed from the conventional to the Islamic stock markets, especially, from the conventional developed markets to the Islamic Emerging and Arab markets and to the Islamic developed markets. Finally, it has been proved that the interdependencies from conventional to Islamic markets are propagated between Islamic markets. Our findings suggest that the Islamic finance industry does not seem able to provide cushion against economic and financial shocks that affect conventional markets.     

煤企如何穿越低谷

劳动力和资本要素推动经济发展的动力已经消失,高效率和全球化成为化解当前煤炭企业市场危机的根本途径。当前,煤炭企业面临严峻的经济形势。环渤海5500大卡动力煤平均价格报收643元/吨,较年度高点下降147元。秦皇岛港在最萧条时期一度出现无船装煤现象,这种现象自港口开航以来从未发生。综合各方信息看,国内煤炭行情正处于市场拐点,市场话语权已由卖方转向买方。国内做强、国外做大,已成为我国煤炭企业可持续发展的必经之路。     

抵押风险分析和抵押贷款违约损失率研究

违约损失率是BaselⅡ规定的六大风险指标之一,而抵押是BaselⅡ标准法规定的信用风险缓释工具之一,两者在巴塞尔新资本协议中有着非常关键的地位和作用.本文总结了国内外违约损失率的研究概况,在利用历史数据对我国商业银行抵押贷款的违约损失率进行实证分析后发现：（1）回收率同融资金额成反比;（2）回收率同融资折率成反比;（3）回收率呈“U”型分布.本文的分析有助于进一步探究我国商业银行抵押贷款违约损失率的特征,是量化风险暴露和计算监管资本的基础,并为我们下一步的折率研究做好了铺垫.