A COMPARATIVE STUDY OF MONOTONE QUANTILE REGRESSION METHODS FOR FINANCIAL RETURNS

2016 ◽  
Vol 19 (03) ◽  
pp. 1650016
Author(s):  
YUZHI CAI
Keyword(s):  
Quantile Regression ◽  
Potential Problem ◽  
Financial Returns ◽  
Financial Return ◽  
Regression Methods ◽  
Conditional Quantiles ◽  
Extreme Quantiles ◽  
Estimation Problems ◽  
The Impact ◽  
Quantile Regression Method

Quantile regression methods have been used widely in finance to alleviate estimation problems related to the impact of outliers and the fat-tailed error distribution of financial returns. However, a potential problem with the conventional quantile regression method is that the estimated conditional quantiles may cross over, leading to a failure of the analysis. It is noticed that the crossing over issues usually occur at high or low quantile levels, which are the quantile levels of great interest when analyzing financial returns. Several methods have appeared in the literature to tackle this problem. This study compares three methods, i.e. Cai & Jiang, Bondell et al. and Schnabel & Eilers, for estimating noncrossing conditional quantiles by using four financial return series. We found that all these methods provide similar quantiles at nonextreme quantile levels. However, at extreme quantile levels, the methods of Bondell et al. and Schnabel & Eilers may underestimate (overestimate) upper (lower) extreme quantiles, while that of Cai & Jiang may overestimate (underestimate) upper (lower) extreme quantiles. All methods provide similar median forecasts.

Herding behavior in Islamic GCC stock market: a daily analysis

2018 ◽  
Vol 11 (2) ◽  
pp. 182-193 ◽  
Author(s):  
Mustapha Chaffai ◽  
Imed Medhioub
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Financial Markets ◽  
Stock Returns ◽  
Stock Markets ◽  
Content Type ◽  
Quantile Regression Analysis ◽  
Herd Behaviour ◽  
The Impact ◽  
Quantile Regression Method

Purpose This paper aims to examine the presence of herd behaviour in the Islamic Gulf Cooperation Council (GCC) stock markets following the methodology given by Chiang and Zheng (2010). Generalized auto regressive conditional heteroskedasticity (GARCH)-type models and quantile regression analysis are used and applied to daily data ranging from 3 January 2010 to 28 July 2016. Results show evidence of herd behaviour in the GCC stock markets. When the data are divided into down and up market periods, herd information is found to be statistically significant and negative during upward market periods only. These results are similar to those reported in some emerging markets such as China, Japan and Hong Kong, where stock returns perform more similarly during down market periods and differently during rising markets. Design/methodology/approach The authors present a brief literature on herd behaviour. Second, the authors provide some specificity of the GCC Islamic stock market, followed by the presentation of the methodology and the data, results and their interpretation. Findings The authors take into account the difference existing in market conditions and find evidence of herding behaviour during rising markets only for GCC markets. This result was confirmed after using the quantile regression method, as evidence of herding was observed only in highly extreme periods. Stock returns perform more similarly when market is down in Islamic GCC stock market. Research limitations/implications The research limitation consists in the fact that this work can be extended to compare the GCC stock markets with other markets in Asia such as Malaysia and Indonesia. Practical implications The principal implication consists in the fact that herding behaviour is limited in the GCC markets and Islamic finance can have an important contribution to moderate the behaviour in the financial markets. Social implications The work focusses on the role of ethics in the financial markets and their ability to reduce the impact of behavioural biases. Originality/value The paper studies the behaviour of investors in the Islamic financial markets and gives an idea about the importance of the behaviour in this particular market regarding its characteristics.

scholarly journals Extreme return, extreme volatility and investor sentiment

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 3949-3961 ◽  
Author(s):  
Xu Gong ◽  
Fenghua Wen ◽  
Zhifang He ◽  
Jia Yang ◽  
Xiaoguang Yang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Investor Sentiment ◽  
Generalized Least Squares ◽  
Regression Methods ◽  
Leading Role ◽  
The Impact

The extreme return and extreme volatility have great influences on the investor sentiment in stock market. However, few researchers have taken the phenomenon into consideration. In this paper, we first distinguish the extreme situations from non-extreme situations. Then we use the ordinary generalized least squares and quantile regression methods to estimate a linear regression model by applying the standardized AAII, the return and volatility of SP 500. The results indicate that, except for extremely negative return, other return sequences can cause great changes in investor sentiment, and non-extreme return plays a leading role in affecting the overall American investor sentiment. Extremely positive (negative) return can rapidly improve (further reduce) the level of investor sentiment when investors encounter extremely pessimistic situations. The impact gradually decreases with improvement of the sentiment until the situation turns optimistic. In addition, we find that extreme and non-extreme volatility cannot a_ect the overall investor sentiment.

scholarly journals EFEKTIVITAS REGRESI KUANTIL DALAM MENGATASI PENCILAN PONTENSIAL

2020 ◽  
Vol 14 (2) ◽  
pp. 305-312
Author(s):  
Netti Herawati
Keyword(s):  
Quantile Regression ◽  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Robust Regression ◽  
Information Criterion ◽  
Regression Method ◽  
Least Square ◽  
Mean Square ◽  
The Impact ◽  
Quantile Regression Method

Abstrak Regresi kuantil sebagai metode regresi yang robust dapat digunakan untuk mengatasi dampak kasus yang tidak biasa pada estimasi regresi. Tujuan dari penelitian ini adalah untuk mengevaluasi efektivitas regresi kuantil untuk menangani pencilan potensial dalam regresi linear berganda dibandingkan dengan metode kuadrat terkecil (MKT). Penelitian ini menggunakan data simulasi dengan p=3; n = 20, 40, 60, 100, 200 and   and  diulang 1000 kali. Efektivitas metode regresi kuantil dan MKT dalam pendugaan parameter β diukur dengan Mean square error (MSE) dan Akaike Information Criterion (AIC). Hasil penelitian menunjukkan bahwa regresi kuantil mampu menangani pencilan potensial dan memberikan penaksir yang lebih baik dibandingkan dengan MKT berdasarkan nilai MSE dan AIC. Kata kunci: AIC, MSE, pencilan, regresi kuantil Abstract Quantitative regression as a robust regression method can be used to overcome the impact of unusual cases on regression estimation. The purpose of this study is to evaluate the effectiveness of quantile regression to deal with potential outliers in multiple linear regression compared to the least squares methodordinary least square (OLS).   This study uses simulation data with p=3; n = 20, 40, 60, 100, 200 and   and  repeated 1000 times. The effectiveness of the quantile regression method and OLS in estimating β   parameters was measured by Mean square error (MSE) and Akaike Information Criterion (AIC). The results showed that quantile regression was able to handle potential outliers and provide better predictors compared to MKT based on MSE and AIC values. Keywords: AIC, MSE, outliers, quantile regression

Impact investing in South Africa: managing tensions between financial returns and social impact

2019 ◽  
Vol 31 (3) ◽  
pp. 397-419 ◽  
Author(s):  
Eunivicia Matlhogonolo Mogapi ◽  
Margaret Mary Sutherland ◽  
Anthony Wilson-Prangley
Keyword(s):  
South Africa ◽  
Social Impact ◽  
Emerging Market ◽  
Financial Returns ◽  
African Countries ◽  
Financial Return ◽  
Impact Investing ◽  
Content Type ◽  
Trade Offs ◽  
The Impact

Purpose Impact investment is an emergent field worldwide and it can play an especially important role in Africa. The aim of this study was to examine how impact investors in South Africa manage the tensions between financial returns and social impact. Design/methodology/approach The research was based on 15 semi-structured interviews with key stakeholders in the impact investment community in South Africa to understand the related challenges, trade-offs and tensions. Findings There are two opposing views expressed as to whether the tensions between financial return and social impact result in trade-offs. It is proposed that impact investors embrace this duality and seek to approach it through a contingency and a paradox view. The tensions can be approached by focussing on values alignment, contracting processes, engaged leadership and sector identification. The authors integrate the findings into a proposed framework for effective tension management in an impact investment portfolio. Research limitations/implications This study was limited to selected South African interviewees. It would be valuable to extend the study to other African countries. Practical implications The issue of values alignment between investors, fund managers and investee firms is an important finding for practice. As is the four-part iterative framework for sensing the operating environment, defining impact, organising internally and defining the investment approach. Originality/value This study contributes empirical evidence to scholarship around organisational tensions, especially work in hybrid organisations. It affirms the value of a nuanced application of paradox theory. It examines these tensions through the lived experience of impact investing professionals in an emerging market context.

scholarly journals Spatial Variation and Factors Impacting Grassland Circulation Price in Inner Mongolia, China

2018 ◽  
Vol 10 (12) ◽  
pp. 4381 ◽  
Author(s):  
Jing Zhang ◽  
Colin Brown
Keyword(s):  
Quantile Regression ◽  
Spatial Autocorrelation ◽  
Inner Mongolia ◽  
Local Market ◽  
High Demand ◽  
Grazing Land ◽  
Regression Methods ◽  
Price Distribution ◽  
Quantile Regression Analysis ◽  
The Impact

As the circulation of grassland use rights in China increases, relatively little is known about the factors that influence circulation price. This paper examines the spatial distribution of grassland circulation prices and the impact of various attributes on grassland circulation prices in Inner Mongolia Autonomous Region (IMAR). Spatial autocorrelation tests and quantile regression methods are applied to data from an online land-circulation website covering the period from January to October 2017. The spatial analysis found that grassland circulation price does vary greatly throughout IMAR but that no significant spatial autocorrelation is evident. The quantile regression analysis revealed significant, though varied, quantile effects across the price distribution indicating that local market structures, strong demand for grazing land in desert steppe, high demand of poor herders for smaller plots, and high demand of richer herders for larger plots all play an important role in determining circulation prices. These nuanced findings should enable policy makers, grassland users, and other grassland actors to better understand how grassland price is determined with respect to a range of factors across the quantiles of price as well as the spatial pattern of price characteristics. This information and understanding are a crucial step in improving grassland circulation.

scholarly journals Capital Structure and Bank Profitability in Vietnam: A Quantile Regression Approach

2020 ◽  
Vol 13 (8) ◽  
pp. 168 ◽  
Author(s):  
Tu D. Q. Le ◽  
Dat T. Nguyen
Keyword(s):  
Capital Structure ◽  
Quantile Regression ◽  
Banking System ◽  
Capital Requirements ◽  
Bank Capital ◽  
Bank Profitability ◽  
Continuous Increase ◽  
Regression Approach ◽  
The Impact ◽  
Quantile Regression Method

We empirically investigate the impact of capital structure on bank profitability using a quantile regression method in the Vietnamese banking system during 2007–2019. Our results suggest that the nonlinear relationship between capitalization and bank profitability is only significant at the 90th quantile. This is the first study to conclude that the turning point of capital ratio increases throughout the profitability distribution. Our findings thus suggest that a continuous increase in bank capital requirements does not necessarily result in higher bank profitability.

scholarly journals How Do Workers Sustain COVID-induced Labour Market Shock: Evidence from the Indian State of Kerala

2021 ◽  
pp. 001946622110238
Author(s):  
Muhammed Refeque ◽  
P Azad ◽  
PK Sujathan
Keyword(s):  
Human Capital ◽  
Quantile Regression ◽  
Labour Market ◽  
Empirical Analysis ◽  
Jel Codes ◽  
Explanatory Variables ◽  
Regression Methods ◽  
Labour Market Participation ◽  
Indian State ◽  
The Impact

This article is an empirical analysis of the resilience of workers over the COVID-hit labour market in the Indian state of Kerala. Quantile regression methods are used to ascertain the impact of COVID-19 on the labour market. This method is more advantageous than the traditional OLS method as it does not presume a constant effect of explanatory variables on the distribution of dependent variable. Evidences convey that all the five categories of workers under study were disproportionately buffeted by the pandemic. However, the factors education and experience were found to have a stabilising effect on the rate of labour market participation. The article pitches for a more responsive and responsible role that the State can deliver to embolden and reinforce human capital so that the pandemic like COVID-19 can at best be averted. JEL Codes: E24, H12, I15, J64

Conditional Quantile Estimation and Inference for Arch Models

1996 ◽  
Vol 12 (5) ◽  
pp. 793-813 ◽  
Author(s):  
Roger Koenker ◽  
Quanshui Zhao
Keyword(s):  
Quantile Regression ◽  
Prediction Intervals ◽  
Quantile Estimation ◽  
Arch Models ◽  
Conditional Quantile ◽  
Regression Methods ◽  
Conditional Quantiles ◽  
Inference Methods

Quantile regression methods are suggested for a class of ARCH models. Because conditional quantiles are readily interpretable in semiparametric ARCH models and are inherendy easier to estimate robustly than population moments, they offer some advantages over more familiar methods based on Gaussian likelihoods. Related inference methods, including the construction of prediction intervals, are also briefly discussed.

A semiparametric nonlinear quantile regression model for financial returns

2017 ◽  
Vol 21 (1) ◽  
Author(s):  
Krenar Avdulaj ◽  
Jozef Barunik
Keyword(s):  
Quantile Regression ◽  
Value At Risk ◽  
Regression Models ◽  
Tail Dependence ◽  
Financial Economic ◽  
Financial Returns ◽  
Ample Evidence ◽  
Conditional Quantiles ◽  
Conditional Dependence ◽  
Quantile Regression Model

AbstractAccurately measuring and forecasting value-at-risk (VaR) remains a challenging task at the heart of financial economic theory. Recently, quantile regression models have been used successfully to capture the conditional quantiles of returns and to forecast VaR accurately. In this paper, we further explore nonlinearities in data and propose to couple realized measures with the nonlinear quantile regression framework to explain and forecast the conditional quantiles of financial returns. The nonlinear quantile regression models are implied by the copula specifications and allow us to capture possible nonlinearities, tail dependence, and asymmetries in the conditional quantiles of financial returns. Using high frequency data that covers most liquid US stocks in seven sectors, we provide ample evidence of asymmetric conditional dependence with different levels of dependence, which are characteristic for each industry. The backtesting results of estimated VaR favour our approach.

scholarly journals Bayesian quantile regression analysis for continuous data with a discrete component at zero

2017 ◽  
Vol 18 (1) ◽  
pp. 73-93 ◽  
Author(s):  
Bruno Santos ◽  
Heleno Bolfarine
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Durable Goods ◽  
Continuous Distribution ◽  
Real Data ◽  
Discrete Component ◽  
Bayesian Quantile Regression ◽  
Conditional Quantiles ◽  
Quantile Regression Analysis ◽  
Quantile Regression Method

In this work, we propose a Bayesian quantile regression method to response variables with mixed discrete-continuous distribution with a point mass at zero, where these observations are believed to be left censored or true zeros. We combine the information provided by the quantile regression analysis to present a more complete description of the probability of being censored given that the observed value is equal to zero, while also studying the conditional quantiles of the continuous part. We build up a Markov Chain Monte Carlo method from related models in the literature to obtain samples from the posterior distribution. We demonstrate the suitability of the model to analyse this censoring probability with a simulated example and two applications with real data. The first is a well-known dataset from the econometrics literature about women labour in Britain, and the second considers the statistical analysis of expenditures with durable goods, considering information from Brazil.

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