MARKET AND VOLATILITY TIMING ABILITIES: NEW EVIDENCE OF MUTUAL FUNDS IN THAILAND

Transcription

1 Thammasat Review 1161 MARKET AND VOLATILITY TIMING ABILITIES: NEW EVIDENCE OF MUTUAL FUNDS IN THAILAND Pomchai Chunhachinda * Supradit Tangprasert** This paper examines the timing abilities of Thai mutual funds using the Treynor and Mazuy (J 966) market timing model, and the Busse (J 999) volatility timing model. Both weekly and monthly data of 65 open-ended equity mutual funds in Thailand during the period of are tested. According to weekly data, the evidence indicates that 54% and 55% of the 65funds studied, exhibit market and volatility timing abilities, respectively. However, for the monthly case, the results are much less concrete, indicating that only 4% and 12% of the 65 funds studied show market and volatility timing abilities, respectively. 1. Introduction With dedicated resources and large pooled funds from individual investors, mutual funds benefit from the economy of scales in utilizing the resources necessary to make effective investments. Normally, mutual funds can be classified into two categories: close-ended and open-ended. Being open-ended means that the fund can continuously sell shares to investors and investors can redeem shares whenever they wish, while this is not the case for close-ended. Funds are also different in their investment policies in order to serve different groups of investors. Investors can choose either from all equity, all bonds, balanced funds or even combining a set of mutual funds. Thus, the decision making process in mutual fund investments is divided into two stages. The first stage is the selection of mutual funds to invest in by an individual investor. The second stage is the * Faculty of Accounting and Commerce, Thammasat University ** Bank of Thailand

2 1621 Thammasat eview decision made by fund managers to select different assets to put into each fund. The end result is that an individual investor indirectly holds a portfolio of stocks, bonds, and/or other assets. Choosing which mutual funds to invest in may appear simple if investors focus their attention only on the returns. However, the risks involved with mutual fund investment cannot be ignored. Investors should compare the performance of mutual funds based on the risk-adjusted returns of the funds. The performance of each mutual fund may depend critically on the fund manager's decisions which include asset allocation, security selection, and market and volatility timing. Being able to better predict the market in advance, fund managers can take advantage of the variation to optimize their portfolio allocations. Therefore, good performance measurement should reflect results from what the fund managers intended to do. In this paper, we will focus on the market and volatility timing abilities of Thai mutual fund managers. Market timing is when fund managers increase their portfolio proportion in risky assets during expected high return periods. Focused on the returns of the market, fund managers can theoretically outperform others who have less information. Having to pay management fees, investors expect mutual funds to provide them with better investment yields. Unfortunately, many studies [e.g. Treynor and Mazuy (1966), Henriksson (1984), Daniel et.a1. (1997), Liljblom and Loflund (2000)] have found no evidence to indicate the market timing ability of mutual fund managers. The Thai stock market with high volatility, as similar to other emerging markets, should provide mutual funds managers more opportunities to time the market. Therefore, it is interesting to investigate whether Thai mutual fund managers have the market timing ability. In addition to market timing, mutual fund managers can also time the volatility which is to decrease the allocation of risky assets during high volatility periods. Breen et. a1. (1989) indicate that the ability to forecast the variance of excess return on stocks could worth as much as 2% per year. From this result, Busse (1999) further inves-

3 Thammasat eview 1163 tigates how much mutual funds can time the volatility. He finds out that fund managers can time the volatility, although they may actually make decisions according to other factors not just the volatility timing. Johannes et. al. (2002) suggest that market timing strategy performs worse than volatility timing. Using conditional mean-variance analysis to assess the value of volatility timing, Fleming et. al. (200 l) find out that volatility timing strategy outperforms the efficient static portfolio that has the same target of expected return and volatility. This study will explore both market and volatility timing of Thai mutual funds using new and different frequency data. With more recent data, we will be able to update the evaluation of the market timing ability of Thai mutual funds. Since 1999, the Thai mutual fund market has been much more developed and should be more effectively managed. Stronger market timing ability may, as a result, be apparent. In addition, we would like to compare whether weekly data is more powerful than monthly data in exhibiting significant market timing ability. Apart from studying the market timing ability, we also investigate the volatility timing ability which has been studied more presently. The results of this study will enhance the knowledge of Thai mutual funds performance on market and volatility timing abilities. The structure of this paper will be as follows. Section 2 presents the contributions of previous studies. Section 3 discusses the methodology and data. Section 4 presents the empirical findings. The last section provides concluding remarks. 2. Literature Review To find whether mutual funds generally perform better than the market, Treynor and Mazuy (1966) attempt to indirectly capture the non-linear sensitivity of the portfolio return to the benchmark return using a quadratic market-timing model. The model works under the assumption that when fund managers predict that the market will

4 1641Thammasat I :eview fall, they will shift the composition of their portfolios from more to less volatile securities. If they predict that the market will rise, they will shift from less to more volatile securities. Using this approach, they find no evidence to support that mutual fund managers can outguess the market. Sharpe (1975) studies the potential gains from market timing and its relationship with manager's ability to make correct predictions of market conditions. With data from 1929 to 1972, he finds out that the benefit is only marginal, while it can be significant if the manager can accurately predict the market. However, this study only evaluates the potential of the general market and does not consider portfolio containing specific stocks. Henriksson and Merton (1981) propose the "switching slope" model to test the market timing ability of mutual fund managers. Applying Henriksson and Merton (1981) model on 116 U.S. mutual funds from 1968 to 1980, Henriksson (1984) finds out that fund managers have no market timing ability. A more specific market timing performance measurement is proposed by Graham and Harvey (1997). They measure the ability of investment newsletters that recommend increasing stock market weights before market appreciation and decreasing weights before market declines. With the asset allocation strategies from , they find no evidence that the newsletters can predict the direction of the market. Another family of measurement is the decomposition approach by Brinson et. al. (1986, 1990). They divide the returns on investment into different parts: normal return, return from security selection, return from market timing and residual return. Using a data set of many large U.S. pension funds, they find that the active selection of asset classes dominates the active selection of asset within classes. However, the passively managed portfolios dominate the actively managed ones. Applying u.k. data, Blake et. al. (1999) slightly modify the framework by including the evolution of portfolio weights. Their results also confirm Brinson et. al. (1990) findings and they also find that adjusting for risk does not affect the result. Similar performance measures are proposed by Daniel et. al.

5 Thammasat llevie 1165 (1997). They use benchmarks based on market capitalization, bookto-market, and prior-year return. There are five levels for each characteristic with a total of 125 benchmark returns. For each benchmark, they decompose the fund returns into components of Average Style, Characteristic Selectivity, and Characteristic Timing. In addition to the characteristic based measures, they also use the original Jensen measures, Grinblatt and Titman (1993) measure, and Jensen measure using Carhart (1997) four-factor portfolio. After testing 2,500 equity funds from 1975 to 1994, they find that the funds have some selectivity ability but no characteristic timing ability. Droms and Walker (1996) study the relationship between risk-adjusted returns of equity mutual funds and asset size, expense ratio, portfolio turnover, and load/no-load status. Based on data of 151 equity funds from 1971 to 1990, they find no relationship between investment performance and all the factors except expense ratio. Grinblatt and Titman (1994) compare Jensen Measure, Positive Period Weighting Measure developed in Grinblatt and Titman (1989b), and Quadratic Measure of Treynor and Mazuy (1966) on 279 funds and 109 passive portfolios. They find that the measures yield no significant market timing ability. Like Droms and Walker (1996), they test fund performance against net asset value, load, expense, portfolio turnover, and management fee. Their result shows that portfolio turnover significantly and positively related to the ability of fund managers to earn abnormal returns. Liljeblom and Loflund (2000) study the performance of mutual funds for different investment horizons in a small Finnish market using Treynor and Mazuy (1966) and Henriksson and Merton (1981) measures. They find that there is no significant positive market timing for any funds. Adopting Treynor and Mazuy (1966) model, Bollen and Busse (2001) find that the tests with daily data exhibit more significant timing ability than the monthly case. They conclude that standard regression-based tests have more power to detect significant timing activity when daily data is used. Nevertheless, using daily data on mutual fund portfolios can lead to a positive serial correlation

6 1661 Thammasat view problem. This problem leads to a downward bias in the estimation of variance of returns. Dimson (1979) proposes many methods to correct this bias. One way is to include lag variables in the regression model which is adopted by Bollen and Busse (2001). Srisuchart (2001) evaluates Thai mutual fund selectivity and market timing ability during January 1990 and May He uses monthly returns adjusted by dividend of close-ended equity funds, fixed income funds, balanced funds and flexible portfolio funds. Using 5 models, which are Jensen (1968), Treynor and Mazuy (1966), Henriksson and Merton (1981), Kon and Jen (1978) and Kon (1983), he finds that equity funds outperform fixed income funds in market timing ability, while fixed income funds outperform equity funds in selectivity ability. Yet, the study is hampered by many problems such as the limitation of data on risk-free rate and mutual funds, and the regulatory constraint on holding equity securities. Another area of timing study is volatility timing used to reduce exposure to risky assets when the volatility of those assets increases and vice versa. Based on the finding ofbreen et. al. (1989) that volatility timing could worth as much as 2% per year, Busse (1999) uses the Single, Three, and Four-Index models to study the volatility timing ability of mutual funds. They find that many mutual funds show volatility timing for both daily and monthly data, and the multiple index models yield stronger volatility timing ability than that of the single index model. To address the issue that volatility timing may manifest itself from other decisions, Busse (1999) tests the relationship between conditional market returns and conditional market volatility and finds no relationship. In addition, to address the error-in-variable problem from using EGARCH, Busse (1999) uses the extreme-value estimation method to find the monthly volatility of the market and finds that the estimated standard deviations are similar to that of the original method. The economic value of volatility timing is also studied by Johanes et. al. (2002), Fleming et. al. (2001) and Fleming et. al. (2003). Johanes et.al. (2002)'s important finding is that a strategy based solely

7 Thammasat evie\" 1167 on volatility timing uniformly outperforms market timing strategies. Fleming et. al. (2001) use conditional mean-variance analysis to find the value of volatility timing to short-horizon investors. They find that volatility timing strategies outperform the unconditional efficient static portfolios. Using intraday returns to calculate daily volatility, Fleming et. al. (2003) find that the value of volatility timing strategy increases. Therefore, the results of the above studies should encourage fund mangers to consider timing the volatility when managing a portfolio. 3. Methodology 3.1 Timing Ability Models Market Timing Ability We study the market timing ability using only the Treynor and Mazuy (1966) model (TM model hearafter) since many previous studies show that other alternative models also yield a similar result. If market timing ability exists, the characteristic line will shift according to the market condition. During high market return periods, market timers will increase the market sensitivity by increasing the portfolio market beta. In contrast, during low market return periods, market timers will decrease the market sensitivity by decreasing the portfolio market beta. The resulting characteristic line will be a convex curve as seen in Figure 1.

8 1681Thammasat ~eview Figure 1 Characteristic Lines of Mutual Funds with Timing Ability. Fund Rate of Return - NoTiming Market Rate of Return Funds with a convex characteristic line are funds with market timing ability while funds with a straight characteristic line are funds with no market timing ability according to Treynor and Mazuy (1966). Therefore, the ability to time the market is shown through the convexity of the characteristic line. TM (1966) market timing model modifies the CAPM model by including the square return term as the following equation: R. - R = a + b(r - R ) + c.(r - R )2 + E I, I f I I I m, I f I 1 m, I f I I, I (1) where Ri, I is the fund return at period t, Rf, I is the risk-free rate at period t, Rm, I is the return on the market at period t. The testing hypotheses are: Ho: The mutual fund i has no market timing ability (ci = 0) H : The mutual fund i has market timing ability (c of 0) a 1 It is also possible that the coefficient of the squared term is negative which means the mutual funds with negative coefficient behave as opposed to what should be in timing the market.

9 Thammasat~eview Volatility Timing Ability As for volatility timing, we will evaluate the mutual funds by applying the Busse (1999) one index model. Starting with the CAPM single index model: R. - R = a + [J (R - R ) + C I, I f, I 1 1 m, I f, I t, I (2) where Ri, I is the fund return at period t, Rj, I is the risk-free rate in period t, R is the return on the market at period t. m,1 Busse (1999) accounts for volatility timing by using a simplified Taylor expansion on market beta as a linear function of the difference between market volatility and its time-series mean: f3 = b. + d. (a - a ) i,t l l m,t m (3) Substituting beta into the original CAPM model in equation (2) yields Ri, 1- Rj, 1= ai + bi (Rm, 1- Rj,) + di(am,1 - am )(Rm, 1- Rj,) + ci, I (4) where amt is the market volatility during period t, am is the average period volatility. The testing hypotheses are: Ho: The mutual fund i has no volatility timing ability (d; = 0) H: The mutual fund i has volatility timing ability (d '* 0) a 1 Similar to TM (1966), the existence of volatility timing is shown through the sign of the coefficient of the volatility term d{' d; being less than zero indicates volatility timing ability. The relationship between the volatility term, (a m, t - a m)(rm, I - RI) and portfolio return should be negative such that during high volatility periods the

10 1701 Thammasat eview portfolio return should behave in the opposite direction of the market, whereas the portfolio return should behave in the same direction of the market during low volatility periods. 3.2 Timing Coefficient Estimations For both TM (1966) market timing and Busse (1999) volatility timing models, we will use the Generalized Autoregressive Conditional Heteroscedasticity GARCH (1,1) method to estimate the timing coefficients: TM (1966) market timing: R. - R = a + b(r - R ) + c (R - R Y + t, I f, I t t m, I f, I t m, I f, I m, I 0'2 I= (jj + ac2 I-I + fjvi-i R,..2 (5) Busse (1999) volatility timing: R - R = a + b (R - R ) + d /0' - 0' ) (R - R ) + (6) i,i f, I i i m, I f, I i \' m,1 m ni, I f, t- I ni, I 0'2 I= (jj + ac2 I-I + fjvi-i R,..2 We believe that it is appropriate to use GARCH (1,1) estimation for both market timing and volatility timing studies because the nature of the market return data is known to have a certain degree of autocorrelation and heteroscedasticity problems. While the problems may not be as severe as the daily case, it is still appropriate to use GARCH (1,1) for our weekly estimation. For the monthly case, we will also use GARCH (1,1) to estimate the timing coefficients in order to be consistent with the weekly study. However, for the robustness check, we also utilize OLS, OLS with Newey-West Heteroscedasticity and Autocorrelation Consistent Covariance to estimate the timing coefficients.

11 Thammasat evie Data Previous studies used different frequencies of data e.g. weekly, monthly, quarterly and yearly. The argument for using low frequency data is that mutual funds would be hesitant to adjust their portfolio composition, and, hence, change fund volatility much often as mentioned in Treynor and Mazuy (1966). On the other hand, Goetzmann et. al. (2000) argue that monthly observations cannot describe the manager's timing activities because the decisions on portfolio composition can be made everyday and usually done more than once a month. The effect of the shift may not be realized until other data is collected. Furthermore, the lack of data is the problem for longer interval time series. For example, if yearly data is used, it would require at least 10 years of data. In addition, it is difficult to obtain useful 10 years data because it leads to paradigm shift problem, and many Thai mutual funds are still very young. In this paper, we use both weekly and monthly data from 200 I to 2003 since the stock market condition is more improved and stabilized Transforming Weekly to Monthly Returns To obtain monthly data, we convert the weekly market return, weekly repurchase rate and weekly mutual fund returns to monthly returns. Similar to Bollen and Busse (200 I), we construct monthly returns from the weekly returns as follow: lo+n-l RM = II(1 + R ) - I I, T 1= In 1, t (7) where R~ T is the monthly return at month T, to is the first week in month T, N is the number of weeks in month T. Therefore, the monthly return in equation (7) is simply the product of N weekly returns during month T.

12 1721Thammasat Review Mutual Fund Returns From the database of Thailand Association of Investment Management Companies, we obtain the weekly net asset value (NAV) and total asset value of all open-ended mutual funds in Thailand from 200 I to The reason to use open-ended mutual fund is that the shares can be issued and redeemed at net asset value as investors wish. Therefore, to always attract new investors and retain current ones, open-ended fund managers may have stronger motivation to actively manage the fund's assets. In addition, we use only equity funds because in Thailand the liquidity of equity is much greater than that of other securities. open-ended From the list of all mutual funds, we can select only 65 funds that exist throughout the period of For mutual fund returns, we use the formula: R = NA~,t-NAVi,t.I+Dividendi,t 1,1 NAV (8) where NA ~,t is the net asset value of mutual fund i at time t and Dividendi,t is the dividend paid by fund i at time t Market Returns SET index is used as a proxy for the Thai stock market. The index is calculated from the market capitalization weighted prices of all common stocks on the main board. The return on the market is calculated from the equation: R m,1 SETI- SETI 1 SETI_1 (9) where SET is the SET index at time t. I We use weekly SET index data to calculate the weekly market returns. For monthly data, we used the conversion described in equation (7). formula

13 Thammasat 1- eview Risk-Free Rates We use the 7-day repurchase rate from the Bank of Thailand to calculate the weekly risk-free return'. The repurchase agreements at the Bank of Thailand are considered as risk-free because they are collateralized by government bonds. The 7-day repurchase period was chosen because the horizon matches our weekly mutual fund and market return data. The monthly risk-free rate is obtained from weekly returns by equation (7) Market Volatility To examine the volatility timing ability of mutual funds using Busse (1999) model, we must first estimate the weekly and monthly excess return volatilities using Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) method with 2 orders. The EGARCH (2,2) model has the following specification: R -R =c + C m, I f,1 m m, I Cm, I1 cm, I-I' cm, 1-2~ ~ t (0, 0':/1,) (10) m,1 ;=, 1 m,i-1 j=1 J In 0'2 = (0+ ± [J logo'2+ ± [al o'. Cm,l:i -Hr n +y - m,l-.j m,l-.j J C O'm,t_j.] where Rm,t is the market return at time t and Rf,t is the risk-free rate at time t. The reason we chose EGARCH (2, 2) as our core volatility estimation is because it yields the most convincing pattern, and the test statistics such as Log Likelihood, Akaike Information Criterion 1 Using the government security rate is not applicable in the case of Thailand because of the inadequacy of maturity and liquidity.

14 1741Thammasat Heview and Schwarz Criterion are slightly better2 The estimated standard deviations for weekly and monthly cases are shown in Figure 2 below. Figure 2 Standard Deviation of Excess Market Return Using EGARCH (2, 2) Estimation. A. Weekly Case % NN 0N(:l N""' (:ln N ""'0 - N(:l v; 0\ ;:::: 0; t- 0\ - ""' ""' ;; -- ;; We also apply different methods such as GARCH (1,1) and regular standard deviation to find weekly and monthly market volatilities. We then use the results from these alternative methods to estimate the Busse (1999) volatility timing ability of mutual fund, and compare the results. However, to save space, we do not report the details of the results here.

15 Thammasat, eview 1175,...;N<') %,...; B. Monthly C':l 90'"' >~ ;!., :3 > 6.. 0C':l 0C':l <l.) C':l 0::;s ::;s~ Ż..., r:/l Z ~..., Case,...; Empirical Results 4.1 Market Timing Ability Table I panels A and B report the market timing abilities of 65 Thai mutual funds for both weekly and monthly data. For the weekly case, the TM (1966) market timing coefficients of 65 individual mutual funds indicate some sign of market timing ability with the mean value of and standard deviation of The mean value of the coefficient is positive suggesting that the overall market timing ability of mutual funds is observed. There are only 5 funds with negative coefficients which indicate worse performance than that of the market. Interestingly, only one of these negative coefficients is significant at the 10% level of significance and below. At the 1%, 5% and 10% significant levels, there are 20, 29 and 35 funds respectively that show evidence of positive market timing ability. Therefore, we can say that approximately half of the

16 1761Thammasat Review Thai mutual funds have market timing ability during the period of our study. Table 1 Market Timing Ability Coefficient c. 1 Using GARCH (1,1) Estimation Using Treynor and Mazuy (1966) market timing model as a mean equation: R. - R = a+ her - R ) + c.(r - R )2 + c. I, I j. I 1 I m, I f, I I m,1 f, I, I A. Weekly Case Statistics: Number Reject All c; c, = Positive Negative = of Funds: 1% 10% 5% Coefficient 1 B. Monthly Case Statistics: Number Reject All c, c; = Negative Positive = of Funds: 1% 10% 5% Coefficient We use GARCH (1,1) regression method to estimate the market timing ability of 65 open-ended equity funds for both weekly and monthly data during year with market volatility estimated from EGARCH (2,2) regression. The mean and standard deviation of the market timing ability coefficient, c" of all 65 funds are shown in the table. The second section of the table shows the number of funds that have significant market timing coefficient at different level of significance.

17 Thammasat 'evie'i1\< 1177 For the monthly case, Table 1 panel B shows that the market timing ability of the mutual fund manager is much weaker than that of the weekly case. The estimation indicates only 1 mutual fund that can positively time the market at the 5% significant level and below, and only 2 mutual funds show signs of negative market timing ability at the 10% significant level and below. Furthermore, only 3 mutual funds can positively time the market at the 10% significant level comparing to 35 funds in the weekly case. This result is consistent with previous studies [e.g. Treynor and Mazuy (1966), Srisuchart (2001)] which state that when using monthly or lower frequency data, the market timing ability hardly exists. However, Bollen and Busse (2001) show that higher frequency data provides the market timing study with a more precise and sharper inference. The more favorable result of high frequency data must be carefully interpreted since the significance of the market timing coefficient may manifest from other reasons. For example, for some periods, mutual fund managers may be able to gain abnormal returns just from the under-priced stocks and this will also lead to the regression that demonstrates market timing ability. Bollen and Busse (2001) also mention that if the investment strategy is changed over time, it may be difficult to assess the TM (1966) market timing model due to the misspecification of timing functions. 4.2 Volatility Timing Ability Table 2 panels A and B report the volatility timing ability for both weekly and monthly data of the 65 Thai mutual funds. For the weekly case, the Busse (1999) volatility timing coefficients of 65 individual mutual funds indicate a moderate sign of volatility timing ability. At the 10% significant level, more than half of the sample (36 funds) show evidence of volatility timing. At the 5% and 1% significant levels, there are 33 and 26 funds that have volatility timing abilities, respectively. Moreover, all of significant samples show negative signs of volatility timing coefficients which are

18 1781Thammasat Review correct according to the theory. Nevertheless, there are 8 out of 65 funds that have positive signs but not significant. We, therefore, can safely conclude that majority ofthe Thai mutual funds have volatility timing ability. Table 2 Volatility Timing Ability Coefficient dj Using GARCH( l, 1) Estimation Using Busse (1999) volatility timing model as a mean equation: Ri, 1- Rjol = Gi + bi(rm, 1- Rj;) + d/am, 1- am )(Rm, 1- Rj; I-I) + Ei, t A. Weekly Case Statistics: Number Reject All dj = Positive Negative of 65 Funds: 8 10% 5% dj 26 = 0 1% Coefficient 0 B. Monthly Case Statistics: Number Rejectect All = Negative Positive 55 dj 84 of 6510 Funds: 0 10% 5% Coefficient dj = 0 1% 0 We use GARCH (1,1) regression method to estimate the volatility timing ability of 65 open-ended equity funds for both weekly and monthly return data during year with market volatility estimated from EGARCH (2,2) regression. The mean and standard deviation of the volatility timing ability coefficient, di, of all 65 funds are shown in the table. The second section of the table shows the number of funds that have significant volatility timing coefficient at different level of significance.

19 Thammasat 1179 For the monthly case, the result indicates a much less degree of volatility timing ability. Table 2 panel B shows that at the 10% significant level, only 8 mutual funds have correct signs of volatility timing coefficient, and 1 fund has an incorrect sign. At the 5% and 1% level of significance, only 6 and 4 mutual funds have correct signs of volatility timing coefficients, respectively. 4.3 Robustness Tests To check the validity of the estimation method, we compare the coefficients estimated from GARCH (1,1), OLS and OLS with N ewey- West Heteroscedasticity and Autocorrelation Consistent Covariance (OLS with HAC). The results for the weekly case are reported in Table 3 panel A. The mean values of the market timing coefficients are , and for OLS, OLS with HAC and GARCH (1,1), respectively. When using GARCH (1,1) estimation, there are 35, 29 and 20 funds with significant market timing coefficients at the 10%, 5%, and 1% level of significance, respectively. For OLS estimation, there are 24, 21, and 13 funds that have significant coefficients at the 10%, 5% and 1% significant levels, respectively. When using OLS with HAC estimation, there are 30, 27 and 17 funds with significant coefficients at the 10%, 5% and 1% level of significance, respectively. From the results, it can be concluded that different estimation methods provide quite similar inferences. Table 3 Market Timing Coefficients from Different Methods of Estimation Based on Treynor and Mazuy (1966) market timing model: R. - R = a. + b.(r - R ) + e(r - R)2 +. I, t f, t 1 1 m, t f, t 1 mj j, I, I

20 1801Thammasat -=leview A. Weekly Case Square Ordinary GARCH (OLS) (1,1) Least using HAC Number of (+) (-) 0 7Funds: 58 (+) B. Monthly Case Ordinary Square GARCH (OLS) (1,1) Least using HAC Number of 2021 (+) 4718 (-) Funds: 47 (+) 21 We use Ordinary Least Square (OLS), Ordinary Least Square with Newey-West Heteroscedasticity and Autocorrelation Consistent Covariance estimates (OLS with HAC) and GARCH (1,1) methods to estimate the market timing ability of65 openended equity funds for both weekly and monthly data during year Table 3 panel B also shows similar results for the monthly market timing case. The average values for the coefficient are for OLS and OLS with HAC and GARCH (1,1) estimations. The numbers of significant coefficients at the 10% level are 2, 2 and 3 for OLS, OLS with HAC and GARCH (1,1). Again, the result is fairly insensitive to the method of estimation.

21 Thammasat qeview 1181 From Table 4 panel A, the weekly volatility timing estimation is slightly sensitive to the regression methods. The averages of the coefficient are for OLS and OLS with HAC, and for GARCH (1,1). However, the numbers of significant coefficients are somewhat different at the 10% significant level i.e. OLS, OLS with HAC and GARCH (1,1) provide 23, 42 and 36 significant coefficients, respectively. Therefore, OLS estimation seems to be more conservative in indicating the volatility timing ability of mutual funds even at different levels of significance. Table 4 Volatility Timing Coefficients from Different Methods of Estimation Based on Busse (1999) volatility timing model: R. -R =a.+b(r -R )+d(cr -cr )(R -R )+e I, ( f, ( 1 1 rn, ( f, ( 1 rn, ( rn rn, t.f, (-1 I, ( A. Weekly Case Ordinary GARCH Square (OLS) (1,1) Least using HAC Number of (+) 230 (-) Funds: 58 (+) 0

22 1821 Thammasat view B. Monthly Case Square Ordinary GARCH (OLS) (1,1) Least using HAC Number of (+) (-) Funds: 11 (+) 0 We use Ordinary Least Square (OLS), Ordinary Least Square with Newey- West Heteroscedasticity and Autocorre1ation Consistent Covariance (OLS with HAC) and GARCH (1,1) methods to estimate the volatility timing ability of 65 openended equity funds for both weekly and monthly data during year For the monthly case, the volatility timing estimation is more sensitive to the regression methods as presented in Table 4 panel B. Although the averages of the coefficient are for OLS and OLS with HAC, and for GARCH (1,1), the numbers of significant coefficients are much varied. At the 10% level of significance, the OLS, OLS with HAC and GARCH (1,1) estimations give 21, 39 and 8 significant coefficients, respectively. We cannot conclude that the weekly data gives a better volatility timing result than that of the monthly data as in the market timing ability case. Therefore, the method of estimation may have some impact on the inference of volatility timing results. 5. Conclusion In this paper, we examine market timing and volatility timing abilities of Thai open-ended equity mutual funds. Using Treynor and Mazuy (1966) market timing ability model, we find that a large portion of mutual funds show a sign of market timing ability for weekly

23 Thammasat :evie 1183 data. On the other hand, for monthly data, we can hardly find the evidence of market timing ability. The results are consistent with previous studies, for example, Srisuchart (2001) finds that Thai mutual funds have no market timing ability when monthly data is used and Bollen and Busse (2001) indicate that regression based performance measurements have more capability to detect timing activity when higher frequency data is used. For robustness check, we apply different methods of regression and find that the results are relatively consistent. We also study how fund managers react to changes in market volatility using the model proposed by Busse (1999). If fund managers have volatility timing ability, they will reduce market exposure when the market volatility is high and vice versa. We find that Thai mutual funds show some sign of volatility timing when weekly data is used. This result is consistent with the finding of Bus se (1999) that many mutual funds have the ability to time volatility using weekly return data. However, for monthly data, the result is not consistent i.e. different methods of estimation indicate different numbers of significant volatility timing coefficients. In general, the results suggest that Thai mutual funds perform effectively in timing the market and volatility. With superior information, mutual funds are capable of adjusting their investment according to the market condition. References Blake, D. and Lehman, B. N. and Timmermann, A., "Asset Allocation Dynamics and Pension Fund Performance," Journal of Business 72 (1999): Bollen, N. P. B. and Busse, J. A., "On the Timing Ability of Mutual Fund Managers," Journal of Finance 56 (2001): Breen, W. and Glosten, L. R. and Jagannathan, R. "Economic Significance of Predictable Variations in Stock Index Returns," Journal of Finance 44 (1989):

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