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This study aims to document the time varying risk premia for market, size, value and momentum factors for an emerging market using a sophisticated conditional asset pricing model. The focus of this study is Turkish stock market denominated in local currency with its peculiar risk premia.

The authors employ Gagliardini et al.'s (2016) econometric method that uses cross-sectional and time series information simultaneously to infer the path of risk premia from individual stocks.

Using this methodology, the authors assess several conditioning information and conclude that local dividend yield, inflation and exchange rates have the most explanatory power. The authors document the time varying risk premia in Turkey over three decades.

Existing studies on dynamic estimation of risk premia lack a consensus as to which state variables should be included and to what extent they impact the magnitude of the premium. The authors extend the conditioning information set beyond the ones existing in the literature to determine variables that are specifically important for an emerging market.

In this paper, the authors aim to investigate the short‐run as well as long‐run market efficiency of Indian commodity futures markets using different asset pricing models. Four agricultural (soybean, corn, castor seed and guar seed) and seven non‐agricultural (gold, silver, aluminium, copper, zinc, crude oil and natural gas) commodities have been tested for market efficiency and unbiasedness.

The long‐run market efficiency and unbiasedness is tested using Johansen cointegration procedure while allowing for constant risk premium. Short‐run price dynamics is investigated with constant and time varying risk premium. Short‐run price dynamics with constant risk premium is modeled with ECM model and short‐run price dynamics with time varying risk premium is modeled using ECM‐GARCH in‐Mean framework.

As far as long‐run efficiency is concerned, the authors find that near month futures prices of most of the commodities are cointegrated with the spot prices. The cointegration relationship is not found for the next to near months futures contracts, where futures trading volume is low. The authors find support for the hypothesis that thinly traded contracts fail to forecast future spot prices and are inefficient. The unbiasedness hypothesis is rejected for most of the commodities. It is also found that for all commodities, some inefficiency exists in the short run. The authors do not find support of time varying risk premium in Indian commodity market context.

In context of Indian commodity futures markets, probably this is the first study which explores the short‐run market efficiency of futures markets in time varying risk premium framework. This paper also links trading activity of Indian commodity futures markets with market efficiency.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This paper aims to study the asset pricing implications for stock and bond markets in a long-run risks (LRR) model with regime shifts. This general equilibrium framework can not only generate sign-switching stock-bond correlations and bond risk premium, but also quantitatively reproduce various other salient empirical features in stock and bond markets, including time-varying equity and bond return premia, regime shifts in real and nominal yield curves, the violation of the expectations hypothesis of bond returns.

The researchers study the joint determinants of stock and bond returns in a LRR model framework with regime shifts in consumption and inflation dynamics. In particular, the means, volatilities, and the correlation structure between consumption growth and inflation are regime-dependent.

The model shows that the term structure of interest rates and stock-bond correlation are intimately related to business cycles, while LRR play a more important role in accounting for high equity premium than do business cycle risks.

This paper studies the joint determinants of stock and bond returns in a Bansal and Yaron (2004) type of LRR framework. This rational expectations general equilibrium framework can (1) jointly match the dynamics of consumption, inflation and cash flow; (2) generate time-varying and sign-switching stock and bond correlations, as well as generating sign-switching bond risk premium; and (3) coherently explain another long list of salient empirical features in stock and bond markets, including time-varying equity and bond return premia, regime shifts in real and nominal yield curves, the violation of the expectations hypothesis of bond returns.

The National commodity exchanges were established in India in the year 2003-2004 to perform the functions of price discovery and price risk management in the economy. The derivatives market can perform these functions properly only if they are efficient and unbiased. So, there is a need to properly evaluate these aspects of the Indian commodity derivatives market. The purpose of this paper is to test the market efficiency and unbiasedness of the Indian soybean futures markets.

The paper uses cointegration and a QARCH-M-ECM-based framework to test the market efficiency and unbiasedness in the soybean futures contract traded in the National Commodity Derivatives Exchange (NCDEX). The cointegration test is used to test the long-run unbiasedness and market efficiency of the contract, while the QARCH-M-ECM model is used to test the short-run market efficiency and unbiasedness of the contract by allowing for a time-varying risk premium. The price data is also tested for presence of structural breaks using a Zivot and Andrews unit root test.

The soybean contract is unbiased in the long run, but there are short-run market inefficiencies and also a presence of a time-varying risk premium. Though the weak form of market efficiency is rejected in the short run, the semi-strong market efficiency is not rejected based on the forecasts.

This is the first paper to consider time-varying risk premium while performing the tests of market efficiency and unbiasedness on Indian commodity markets.

Glode provides theoretical and empirical evidence that, in aggregate, funds underperform during economic expansions and outperform during contractions. The authors find that this result is not robust to the more appropriate conditional CAPM and to alternative methods for estimating market states. The purpose of this paper is therefore to thoroughly analyze mutual fund performance across the business cycle by disaggregating funds into different investment objectives to determine which funds possess this cyclical performance and which do not.

In this paper, the authors employ a conditional asset pricing model that better captures the variations in the pricing kernel in different economic states. The empirical model adjusts for time‐variation in both risk (beta) and performance (alpha). The authors specify economic states using an ex‐ante measure, the expected market risk premium. This measure is continuous and better captures changing economic circumstances than the ex‐post, binary NBER cycle dates that are common in the mutual fund literature.

In this conditional framework, the authors find that recession protection is only offered by certain types of equity mutual funds. Managers of small‐cap and mid‐cap growth equity funds are able to deliver such state‐dependent performance but managers of value funds do not. In a comparison of active mutual funds with passive counterparts, it is found that both the stocks held by the small‐cap managers as well as their stewardship of the portfolio contribute to that performance.

Drawing from the recent asset pricing literature, the authors are the first to adapt an integrated conditional CAPM framework to examine the state‐dependent performance of mutual funds. Rather than report aggregate equity mutual fund performance, the authors provide an analysis for subsets of mutual funds separated by investment styles. Both managers of and investors in these funds will benefit from an understanding of how portfolio performance is impacted by changing economic conditions.

This paper aims to examine the risk premium for investors in a changing information environment in the Taiwan, New York and London real estate markets from March 2006 to November 2014. This study attempts to quantify behavioral expectations regarding (or motivation for) investment in the Taiwanese real estate in a changing information environment.

This paper uses the rolling generalised autoregressive conditionally heteroskedastic in mean (GARCH-M) methodology which fixes the problem of conventional GARCH-M methodology.

Empirical evidence suggests that the time-varying risk premium changed for the Taiwan real estate market with a new information set. The risk premium changed from 1.305 per cent per month to −7.232 per cent per month. The study also found persistent volatility shocks from March 2006 to November 2014. No such evidence was found for the New York and London real estate markets. Overall, this study finds evidence of a time-varying risk premium, partly explainable by governmental policies and partly unexplainable.

The use of the index of Standard and Poor’s Taiwan Real Estate Investment Trusts to study the Taiwan real estate industry may have aggregation effects in result.

The present study will provide guidance to investors as well as policymakers regarding the Taiwan real estate market.

This study uses the rolling GARCH-M model, which is a first for the Taiwan real estate market.

The purpose of this paper is to examine the volatility effects on the returns for six developed market indices factoring in the unprecedented event of September 11, 2001, hereafter referred to as 9/11, in the USA. It also looks at the correlations between the indices and the risk premium when uncertainty in the financial markets affects the investors psyche, eroding confidence as volatility increases.

The volatility of the indices in generalized autoregressive conditional heteroskedasticity (GARCH) framework, employing first the Box and Jenkins ARMA (p, q) to select models is investigated. The chosen models are based on the results obtained from Akaike information criterion and Schwartz Bayesian criterion. GARCH is a mechanism that includes past variances in the explanation of future variances.

The results highlight several findings, the variance of developed market returns appears to have increased after the 9/11 event; the correlation has increased among developed markets following 9/11; 9/11 affects developed markets, holding short‐term assets do not provide the investors with the reward they usually seek, but results are mixed in the case of holding long‐term assets; for all the period including sub‐period, signs of significant volatility clustering are found; but shocks are not explosive throughout.

The effect of 9/11 on the markets is different from previous worldwide crashes, such as that of October 19, 1987. This paper will be of value to policy makers and managers/institutional investors and those who have some stakes in international portfolio diversification, as the objective of diversification, is to avail the opportunity to improve portfolio performance on the low correlations across international stock markets.

The study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns.

The study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020.

The findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research.

The empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market.

The study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.

Understanding correlations between stock and direct real estate returns, which is the key factor that determines diversification benefits in a portfolio, helps formulate and implement better investors' asset allocation and risk management strategies. The past studies find that direct real estate returns have a low unconditionally (long‐run) correlation with the returns of equities. However, assuming that such correlation is constant throughout all periods is implausible. The purpose of this study is to test the time‐varying correlations of returns between general stocks and direct real estate.

This study uses the dynamic conditional correlation (DCC) model, which is a simplified version of the multivariate generalised autoregressive conditional heteroskedasticity (GARCH) model, proposed by Engle to test the time‐varying correlations between stock and direct real estate returns in six markets, which include the USA, the UK, Ireland, Australia, Hong Kong and Singapore.

The empirical results show significant time‐varying effects in the conditional covariance between stock returns and direct real estate returns. The results vary across different real estate sub‐sectors, and across different countries. It is observed that the conditional covariance increases in the boom markets, but becomes weaker in the post‐crisis periods. The authors observed significant jumps in the conditional covariance between the two asset markets in Singapore and Hong Kong in the post‐1977 Asian Financial crisis periods and in the post‐2007 US Sub‐prime crisis periods.

The past studies find that direct real estate returns have a low unconditionally (long‐run) correlation with the returns of equities. However, assuming that such correlation is constant throughout all periods is implausible. This study fills in the gap by using the dynamic conditional correlation models to allow for time‐varying effects in the correlations between stock and real estate returns.